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One Not EveryoneHow to use Social Media to Personalise Consumer Experiences

One Not Everyone | 2

About this guide 4About the authors 6

Foreword 7

Executive summary 9

I. Personalisation

1. Building relationships through relevance 14 1.1 Future consumer engagement models 15 1.2 Personalisation: the business case 21

2. What does good look like? 27 2.1 Personalised vs. Too personal 32 2.2 Navigating data privacy concerns 36 2.3 Algorithms vs. Editorial curation 41 2.4 Brand led vs. Consumer enabled 47

II. Using social media for personalisation

3. Re-evaluating the role of social media 52 3.1 Redefiningsocialmedia 53 3.2 The social organisation: breaking out of the silos 55 3.3 Social media and the future of brand-consumer engagement 56

4. Social data 57 4.1 Whatissocialdataandhowdoesitdifferfromotherdata? 58 4.2 Opportunities and challenges of social media data 62 4.3 Socialmediadataavailabilityandaccess 68

Contents


5. Approaches (I): personalised marketing 71 5.1 Rethinking segmentation and targeting (interests, attitudes and emotions) 73 5.2 Micro-influenceandnetworkmarketing(relationships) 79 5.3 Momentmarketing(behaviourandintent) 88

6. Approaches (II): personalised experiences 97 6.1 Social CRM 99 6.2 Integrated customer experience 101

7. Approaches (III): customisation, co-creation and community 116 7.1 Customisationandco-creation 118 7.2 User-generated content 120 7.3 Data-driven creatives 122 7.4 Community and social recommendation 124

III. What next?

8. Lookingahead:thehypecycle,IoTandAI 134 8.1 Thehypecycle:it’sstillearlydays 135 8.2 TheInternetofThings 137 8.3 ArtificialIntelligence 139

9. Future challenges 142 9.1 How can we balance relevance and reach? 144 9.2 What does brand experience mean in the age of personalisation? 147 9.3 Data savvy, not data driven:

whatarethelimitationsofdataandanalytics? 148 9.4 Whose data is it anyway? 150 9.5 What is the future role for agencies? 152

Contributors and references 154

Contents


Background The increasing availability of digital technologies and data is changing the ways in which consumers and brands interact. The ability to engage consumers by creating more personalised brand experiences which drive relevance and value for the consumer is moving with that shift: from the nice-to-have to the expected.

Social media plays a key role in driving and enabling this shift. Brands are able to build more direct and personalised relationships with consumers, reaching them on the go and in familiar, social spaces where they choose to spend more and more of their online time. In building these relationships, brands are challenged to earn the right to be present by delivering appropriate,

About this guide

relevant and valuable experiences. The rich dataset associated with social media presents an opportunity to meet that challenge – to deliver these experiences within social media marketing through hyper-targeted communication and beyond social platforms to fuel experiences for consumers on whatever channels or devices they are using. However, the volume of real-time data, its structure, ownership and associated privacy concerns make this a complex landscape for brands to navigate, and this has held back experimentation and the shared learning of best practices.

Brands are able to build more direct and personalised relationships with consumers, reaching them on the go and in familiar, social spaces...


Objectives and ScopeThis guide seeks to help brands, agencies and the wider communications industry by:

Featured Case StudiesCase studies involving the brands listed here feature in this guide. These examples demonstrate how social has been used for personalisation bybrandsindifferentcontextsand to achieve a range of goals.

These cases cite evidence of success of varying degrees of rigour and a consistent body of best practice social personalisation cases is still developing inthisfast-movingfield.

a) Identifying emerging best practice principles for using social media and social media data to create more personalised consumer experiences

b) Highlighting case studies of how organisations have used social media to develop more personalised customer experiences that add value for both consumers and brands

c) Anticipating future issues surrounding the use of social media and social media data for personalisation


CelinaBurnett’scareercombinesconsulting and client side experience focused on digital marketing and analytics. Before joining ASOS to establish their marketing analytics practice, Celina was a partner at WPP marketing foresight consultancy, Gain Theory, and led client engagements at Deloitte Digital and NM Incite, the Nielsen-McKinsey joint venture specialising in social media research. Her experience includes setting up and managing the central social media hubfortheLondon2012OlympicandParalympicGames‎,establishingpersonalised marketing and audience experiences for the BBC as part of the myBBC‎personalisationprogramme, and using social media data and advanced modelling techniques to forecast and optimise the performance ofnewfilmreleasesforSonyPictures.AmemberoftheIPA’sSocialMediaSteering Group, Celina is passionate about driving innovation and best practice in all aspects of social media.

Colin Strong works with a wide range of brands and public sector organisations to combine market research with behavioural science, creating new and innovative solutions to long standing strategy and policy challenges. His career has been spent largely in market research, with much of it at GfK where he was MD of the UK Technology division. As such, he focuses on how technology disrupts markets, creating new challenges and opportunities, and on how customer data can be used to develop new techniques for consumer insights. Colin is author of ‘Humanizing Big Data’whichsetsoutanewagendafor the way in which more value can be leveraged from the rapidly emerging data economy. Colin is a regular speaker and writer on the philosophy and practice of consumer insight.

About the authors

Celina BurnettMarketing Analytics Lead, ASOS

Colin StrongHead of Behavioural Science, Ipsos


This familiar cry from many brands was the catalyst for the creation over three years ago of #IPASocialWorks to help the industry bridge this gap.

My investment in social keeps increasing, but I am not always certain of its true business value

Since its inception, #IPASocialWorks has been developing and accelerating bestpracticeintheeffectivenessanddelivery of the business value of social media. We do this in two ways. First, we have built up what is now a rich bank of peer reviewed case studies from across the world, including examples fromO2,TfL,IKEA,Coca-Cola,theUSNavy and many more, to rival 30 years ofpeer-reviewedIPAEffectivenessAwards and Marketing Society Awards for Excellence and where we can see a direct causal relationship between social – whether paid, earned or owned – and a business return. Second, we do this through bespoke training programmes and importantly three comprehensive ‘Howto’guidesacrossspecificareasof focus – Evaluation, Insight and now Personalisation.

#IPASocialWorksistheworld’s firstcross-industrycollaboration of its kind across brands, agencies, insight specialists and social platforms developing a more ROI-driven robust approach to social. #IPASocialWorks brings together the Institute of Practitioners in Advertising (IPA), The Marketing Society, and the Market Research Society (MRS), and is supported by Facebook and Twitter. As Paddy Barwise, Emeritus Professor of Marketing and Management atTheLondonBusinessSchoolandouracademic adviser on the project, says: “I’mnotawareofanythingelsewherethat matches its scale and quality.”

Foreword

Stephen Maher, CEO, MBA.Chair of #IPASocialWorks.Chairman of The Marketing Society.


This Guide is the third in our series, following our very successful guides, ‘Measuring Not Counting: How to evaluate social media for marketing communications’and‘IntegratedNot Isolated: How to improve customer insight by embracing social mediadata’.ThisGuidefocuseson the Personalisation strand of #IPASocialWorks and explores how to best use social media to create relevant consumer experiences. Wedemonstratehowsocial’srich dataset and intimacy can deliver strong business outcomes in our rapidly evolving new world of personalised, predictive and tech-enabled relationships. We do this through deep diving into a breadth of approaches from case studies involving brands such as adidas, the BBC, Amnesty International and many more, to the trends such as the Internet of Things that will shape the future of this burgeoning area of personalised marketing. In short, we explore and identify how to reap the business rewards from social that is focussedon‘OneNotEveryone’.

Special thanks to our inspired and inspiring authors Celina Burnett and Colin Strong, to all the case study contributors (listed on page 155), to the wonderful Fran Cassidy, our project director, and to all our brilliant colleagues from the #IPASocialWorks Group (listed on page 155) for their valuable, constant and insightful input into the #IPASocialWorks mission.

WehopeyoufindthisGuideusefulas we continue our #IPASocialWorks journey towards understanding the true business value of social media to brands – value that many of us intuitively believe in, but where we crave more robust evidence and where we need to keep learning together as social morphs excitedly every day. As Group member and behavioural change strategist Mark Earlssays:“Thisisnoone-off,wham-bam-thank-you-ma’amstudybutamore practical body of learning which moves and evolves as the environment does – and one thing we all know is that the landscape is not going to simply shift from how it was to some simple alternative state.”

Stephen Maher CEO, MBAChair of #IPASocialWorksChairman of The Marketing Society


1. Personalisation is generating new ways for brands to create business and customer value

The rise in digital and social media and the greater availability of data are creating new opportunities for brands to develop more relevant and valuable experiences through personalisation, from targeted marketing communications to personalised product and service delivery.

Although personalisation is still in its early days for many organisations, technology triggers such as the Internet ofThingsandArtificialIntelligencewillcontinue to drive new opportunities for engagement and scalability and encourage further experimentation. Organisations that do not have a coherent strategy about when and how to introduce increased personalisation will face increasing competitive risks.

2. Brands must strike a new balance between relevance and reach

Many brands creating personalised experiencesreportbenefitssuchasincreased engagement and conversion rates. In seeking to create value through personalisation, brands should not overlook the longer-term business effectsofdeliveringreach,fameandother goals that can be achieved cost-effectivelythroughmassmarketing.

Tostrikeaneffectivebalancebetweenprioritising strategies that will deliver relevance and those that will deliver reach, brands need to be clear about thespecificrolesandbenefitsofeach,and continue to evaluate their mix of approaches over time in order to meet their business objectives.

Executive summary


3. Identifying what ‘good’ looks like for your customers and brand is key

The potential role and value of personalisation can vary by category, brand, target audience, and even individual consumers. To develop a successful personalisation strategy, practitionerswillneedtodefinewhatgood personalisation looks like for their own customers and organisations. Theywillneedtofindasweetspotbetweenacknowledgingcustomers’preferencesinsufficientdetailto create relevance without becoming overly personal or creepy, balancing the role of personalisation by automated algorithms with that of editorial curation, and that of brand-led personalisation versus enabling consumers to tailor their own experiences.

Such a strategy should be built on afoundationof‘testandlearn’andevidence-based decision-making. This will enable the business to deliver incremental value through personalisation and to add to the organisation’sexistingknowledge base of what works in building successful brands.

4. Social media can facilitate personalisation by brands

Social media enables brands to create more direct relationships with consumers. Its rich dataset can provide insight into consumer interests, relationships and behaviours, enabling brands to create more relevant and effectiveexperiencesthanwouldotherwise be possible. Social media platforms are helping brands to leverage this data and their network reach to deliver more targeted communications at scale within their platforms. Brands are also looking to experiment with using this data to deliver more integrated and consistent experiences across touchpoints, although these cases are at much earlier stages of maturity.

Social media is also having a profound effectonpersonalisationthroughtheincreasing importance of community and the shift towards consumer empowerment. Brands are increasingly using activations rooted in social behaviour, enabling consumers to co-create, tailor and share content with their peers. Social media is supporting the move beyond a one-way conversation to one where users are personalising their relationship with brands themselves.


5. Social can enable integrated personalisation if it is empowered across organisations

In many organisations, social media already plays an important role in delivering customer experiences, as a communications channel and as a source of insight. However, social media activity and insight often operate in silos. It is important for social media capabilities to be integrated across organisations to support development of consistent personalised experiences for consumers and brands.

6. Be data savvy, not data driven

Data is fast becoming a strategic asset that can underpin how a brand understands and engages with its customers. But the insights that can be drawn from data rely on the availability, accuracy and completeness of the datasets, as well as the way in which the data has been treated and analysed. This data and the infrastructure to manage and analyse it come at a cost.

In addition, there will always be tensions between the potential value created for consumers and brands by collecting and acting on personal data, and the possible damage from real or perceived threatstoindividuals’privacybygathering this data. In the fast-moving world of social media, ownership and permitted use of data are often unclear andsubjecttoplatforms’changingpolicies. New models to enable consumers to curate and monetise their own data are emerging all the time, which could provide an indication of future data ownership models.

Brands need to be clear-eyed about any issues that could hamper their strategic and consistent use of social data in future. It is vital that practitioners acknowledge the limitations of data, as well as its uses, to ensure they are harnessing data to the needs of organisations rather than being driven by it.


7. Brand experience will evolve in the era of personalisation

Traditionally, a consistent and shared perception of a brand has been communicated to consumers by broadcasting carefully crafted messaging and imagery. Today, peer-to-peer networks have empowered consumers and the brand experience is fragmented across a growing range oftouchpointseachofferingvaryingdegrees of personalisation.

In such a context, the role of brand and the way in which it is managed become more important. We are moving from an age of marketing a carefully crafted external image to one where we must deliver a consistent and authentic brand experience.Abrand’svaluesandwhatitstands for need to be central to delivery of every aspect of the customer journey and experience, enabling the brand ownertocontinuetoreapthebenefitsthat consistent branding provides.

8. The role of agencies must evolve to help clients realise the potential of data and personalisation

As creativity evolves to become more data and technology-centric, agencies must embrace new capabilities and skills to stay relevant to clients. Some agencies have begun to recruit dedicated data and analytics teams and others are looking to build a network of relationships with partners, including tech start-ups.

In the longer term, agencies will need to become trusted advisers to clients to remain relevant and avoid disintermediation by new suppliers. It will be vital for agencies to ensure they have broad and deep partnerships across client organisations in order tounderstandthemarket’schangingrequirements and to ensure that agency propositionsandcapabilitiesarefit for purpose.

Brands seeking to manage high volumes of real-time data will increasingly take some capabilities in-house. While there remainsaclearneedforagencies’existing functions, some agencies should prepare for the additional role of advising clients on setting up such in-house capabilities.

As creativity evolves to become more data and technology-centric, agencies must embrace new capabilities and skills to stay relevant to clients.

1. Building relationships through relevance | 13

I. Personalisation1. Building relationships through relevance

1.1 Future consumer engagement models 1.2 Personalisation: the business case

2. What does good look like?

2.1 Personalised vs. Too personal 2.2 Navigating data privacy concerns2.3 Algorithms vs. Editorial curation2.4 Brand led vs. Consumer enabled


1. Building relationships through relevance

1.1 Future consumer engagement models

1.2 Personalisation: the business case


The consumer context

1. Today’s consumer is connected The rise of digital media and devices means that consumers are now connected to an unprecedented degree. For marketers, this means more opportunities to reach consumers with the right message at the right time and, correspondingly, more challenges to cut through the clutter of media messages.

Research by Media Dynamics found that the average American is exposed to 362 ads per day across traditional and online media, but that only 153 of these attract theperson’sattentionforafewsecondsor more1. The uptake in wearables and other connected devices is likely to accentuate the challenge of clutter.

2. Today’s consumer is empowered Connectivity gives consumers more opportunities to research and compare products online before purchasing. They have greater access to information and opinions from websites and peer-to-peer networks. Social media in particular has given consumers a voice and at scale, enabling product reviews and online word of mouth to spread further and faster than ever.

Brands that are able to meet or exceed customerexpectationswillbenefitfromthepeer-to-peereffectsofadvocacyand recommendation. Recognising this shift in power towards consumers, many brands are increasingly seeking out opportunities to enlist consumers andinfluencerstosupportcustomeracquisition and retention.

1.1

Future consumer engagement models

Social media in particular has given consumers a voice and at scale, enabling product reviews and online word of mouth to spread further and faster than ever.


3. Today’s consumer expects moreConsumers expect faster service, more personal experiences, and a clear value exchange from brands for their time and attention.

AYahoo!studyshowedthat78%ofUSconsumers expressed a desire for more personalised content, citing it as more engaging, educational, time-saving and memorable than generic content2. The samestudyreportedthatonly37%ofconsumers found the online ads they were exposed to on their desktops relevanttothem.Thisfiguredecreasedto30%foradsservedonsmartphones.

The failure of advertisers to address the challenge of creating relevant experiences for consumers has helped to drive the widespread adoption of ad blocking software, now used by one in fiveBritishadults,accordingtotheUKInternet Advertising Bureau (IAB)3. The issue of irrelevant communications will become more acute as the growth in connected devices results in consumers being exposed to more messages across more touchpoints.

A Yahoo! study showed that

78%of US consumers expressed a desire for more personalised content.


What does this mean for brand-consumer relationships?

Brands will seek new ways to balance reach and relevanceBrands will seek to cut through the clutter and build stronger and more relevant relationships with consumers through the increasing range of devices, media and data available to them. Organisations will continue todebatethemosteffectivewaytobalance their resources and priorities between personalising interactions and building reach and fame using broadcast approaches. But there is no doubt that a shift is taking place and that personalisation, which is already a high priority for many brands, is set to become more important across the marketing and communications industries.

Focus will shift from brand communications to delivering an end-to-end customer experienceConsumers experience brands across an increasing range of touchpoints. Whilst today personalisation is often delivered sporadically and in silos,

in future it will need to become integral to the customer experience and be delivered consistently across all consumer-facing points. Technology advances,suchasArtificialIntelligenceand machine learning, could enable organisationstopersonaliseofferingseffectivelyonascalenotcurrentlyachievable.

Consumers will be enabled to take the leadIncreased access to information, technology and peer networks has changed the roles available to consumers. Consumers can now become storytellers and creators, and are seeking support from brands to become the best versions of themselves. While the brand can and will still take the leading role in some relationships, in many cases, brands will in future play the role of enablers with consumers taking centre stage. In the context of the move towards personalisation, this raises questions over the future role of customisation, co-creation and consumer-enabled relevance.


Four future models of engagement

In its study The Future of Marketing and Agencies: The Next 10 Years for Consumer Engagement, the research group, Future Foundation, describes four potential engagement models for winning consumer favour over the next ten years4. The study argues that one of the consistent factors across all four models will be using personalisation to create greater relevance.

Brand models for the future of consumer engagement:

Source: ‘The Future of Marketing and Agencies, The Next 10YearsforConsumerEngagement’,FutureFoundation.

BRAND ME-Q

BEST BUY BRANDS iCONTROL

ME AND THE BRAND NEXT DOOR

Brand focuses on emotional engagement

Brand focuses on functional engagement

Brand is enabler or provider

Consumer is enabler or provider


The Brand Me-Q modelConsumers want a personal relationship with brands, moving beyond a purely functionalandsuperficialpersonalisedexperience to a place where they are recognised as individuals and able to talk with brands on a one-to-one level. As consumers experience more personalisation, their expectations rise and they expect services tailored to their individual needs. In this model, brands become genuine partners for consumers. Deep emotional engagement and lifestyle management are delivered via advice and entertainment, unlocking creativity and delivering surprise and delight.

The Me and the Brand Next Door modelConsumers lead very public and perfectly curated lives on social media. There is a pressure and a need for validation. For around half of consumers, there is also a feeling that they are not reaching their full potential.Inthisscenario,self-sufficientconsumers see their relationship with brands as friendships. Brands take a back seat, but provide consumers with platforms and opportunities to achieve andfulfilindividualpotential.Emotionalengagement with brands can be high, if less overt than with the Brand Me-Q model.

As consumers experience more personalisation, their expectations rise and they expect services tailored to their individual needs.


The iControl modelConsumers want to feel in control at all costs. They want to own their own futures and they want brands to help predict solutions to problems they don’tevenknowtheyhaveyet, giving them the control to make themselves their best possible selves. This scenario anticipates a future world oftech-empowered,self-sufficientconsumers who create tools, services and even products independent of brandedinfluence.

The Best Buy Brands modelConsumers want to have everything in just one click. They want brands to help them derive real value, optimise their lives and collaborate with other organisations, even competitor organisations, in order to deliver this. In this scenario, brands play a wholly functional role and consumers rely on them to deliver best-in-class products and services. Innovation and performance are prioritised by brands and emotional marketing takes a back seat.

The four engagement models proposed by Future Foundation highlight the role ofdataandtechnologyindefiningandenabling future relationships between brands and consumers. This includes the use of relevance for building both functional and emotional relationships, and the trend towards community and consumer-led relationships.

1.2 1. Building relationships through relevance | 21

Personalisation: the business case

Personalisation is not a new concept. Targeting has long formed a core part of marketing strategies, with the majority of marketers delivering segmented email campaigns to their customer base in order to drive greater engagement and uplift in conversions. However, trends in technology and consumer behaviour indicate that personalisation will become a high priority for many brands.

Defining personalisationPersonalisation describes the use of data to deliver the right experience to the right person at the right time. Usedeffectively,itwilldriverelevance,convenience and value for the consumer and deliver positive business outcomes.

The term denotes a range of strategies for delivering relevance which can either be on a unique, one-to-one basis or to definedgroupsorsegments.Manycasestudies today are at a group, or micro-segment, level.

...trends in technology and consumer behaviour indicate that personalisation will become a high priority for many brands.


The benefits of personalisation By introducing elements of personalisation, organisations reportbenefitssuchas:

• Increasedsalesandconversionrates• Increasedorderfrequencyand

average order value • Increasedengagement• Reducedchurn/increasedretention

and loyalty

For instance, in a Retail Week survey, 18%ofrespondentscitedincreasedconversionloyaltyand11%listedrevenuegrowthasthemainbenefitsthey had experienced from adopting personalisation5. In a wide-ranging study, McKinsey & Co. reported seeing personalisation reduce acquisition costs byupto50%,liftrevenuesby5to15%andincreasetheefficiencyofmarketingspendby10to30%6.

Thesefindingsareechoedbycasestudies featured in this report.

Examples

O2

O2usesFacebooktoreachcustomersatdifferentjourneystages(early upgraders, upgraders, out of contract etc.) with the right creative in order to drive repeat sign-ups. This targeted approach resulted in an average 49% decrease in the Cost Per Order across all three user segments, compared to untargeted approaches.

See case study, p107

EE

To promote its superfast 4G network, EE created variants of a video adtotargetaudiencesonFacebookwithdifferentpassions.Forexample, football fans on Facebook were shown a football-related ad. Results showed that targeted creatives were twice as likely to be viewed compared to non-targeted creatives, and that they delivered twice the uplift in traffictotheEEsitetofindoutmoreabout the 4G network.

See case study, p76


How personalisation works

Short and long-term effectsThere are clear reasons why personalisation could generate positive businesseffectsintheshortterm.Brandsaredeliveringoffers,messagesand experiences that will resonate with the individual at the right time and through the right channel, thereby increasing the likelihood of consumers performing the desired action, such as clicking through to purchase.

Research suggests that personalisation can also have a positive longer-term branding impact in contributing to more valuable and frictionless consumer experiencesandaidingeffectiverecall.Indeed, relevant online experiences are now expected by consumers and irrelevant experiences can create a negative impression.

Yahoo! A Yahoo! study showed that US consumers preferred personalised messagesastheyweremorerelevantandofferedvalueandconvenience.Theseadswerealsoproventobemorememorableandmoreeffectiveintermsofrecall,indicatingpotentialforlonger-termbrandeffects.

Source:‘TheBalancingAct:GettingPersonalisationRight’,Yahoo!

The study showed that for consumers success in delivering personalised ads encompassed getting three elements right:

a) Know me: Target what I like and who I am as an individual

b) Speak my language: Make ads resonate, sound like a trusted brand

c) Value my time: Give me better ads with more useful information

54% 52% 49% 45% 42%

Enagaging Teach me something new

Save me time

Memorable Relevant to me

Compared to general ads, personal ads are more:


The self-perception effectEvidence suggests that delivering greater levels of personalisation can drive incremental returns, and in addition that merely the perception or awareness by a consumer that a message has been personalised can increase its effectiveness.

A study entitled, ‘An Audience of One: Behaviorally Targeted Ads as Implied Social Labels’,describeshowwhenconsumers are served ads known to be based on their recent individual behaviour they are more likely to want to purchasetheadvertisedoffer,comparedto when they are exposed to ads served by demographic segments (such as age or gender) or untargeted ads7.

For example, where two groups were served an ad for a high-end wristwatch brand, those that believed the ad was targeted based on their individual behaviour later evaluated themselves as more sophisticated, compared to those who thought the same ad was not targeted at them. The shift in how consumers saw themselves increased their interest in the sophisticated product.

Crucially, the targeted ad was only able toaffecttheconsumer’sself-perceptionif the link between the ad and the individual’sbehaviourwasdeemedplausible.Theself-perceptioneffectswere not observed when no link could be made by the consumer between their previous behaviour and the ad they were exposed to.

The results of the study speak not only to the potential business impact of personalisation, but also underline why it is important to be clear to consumers when they are being served experiences based on their stated preferences and recommendations (which they can review and adjust), as well as about how their data is being used.

Forexample,bothAmazonandNetflixserve recommendations based on ‘becauseyoubought’or‘becauseyouwatched’,helpingaudiencesunderstandwhy they are being served content and also giving them opportunities to change their preferences and the recommendations they see.

Evidence suggests that merely the perception or awareness that a message has been personalised can increase its effectiveness.


Building functional vs. emotional relationships

Personalisation can be used to deliver ondifferentobjectives(short-termvs.long-termeffects)andtobuilddifferenttypes of relationships with consumers. It can be used to nurture functional and transactional relationships by providing seamless experiences that meet consumer needs and to foster more emotional links by engaging consumers at a more one-to-one level, generating a closer and longer-term bond of trust and loyalty.

The scenarios proposed in Future Foundation’sstudyshowtwoopposingbrand-led engagement models which will increasingly be fuelled by relevance:

• Best Buy Brands put products andservicesfirst,withengagementbased on convenience and rational decision-making. Examples include Alibaba, Amazon and mySupermarket. Personalisation here is about delivering the most relevant, valuable and convenient customer experience enabling ease of use and transaction.

• Brand Me-Q focuses on customer experience and maximising every interaction to build brand trust and advocacy. Examples include Nike and British Airways. Personalisation enables these brands to engage with consumers as individuals, developing a loyal and valued relationship.

Many brands currently aspire to emotional models such as Brand Me-Q. Research by Jill Avery, Susan Fournier and John Wittenbraker suggests that thereareasmanyas29differenttypesof relationships across the spectrum of functional to emotional, from One-Night Stand to Best Friend8. Brands can successfully operate across the spectrum of relationships, but not all modelswillfiteachverticalorbrand.Brands will be able to improve their bottom line by understanding which, of the broad range of relational types, their customers are looking for.


There is an acknowledgement that market share accrues to a more in-depth relationship(suchas‘bestfriends’or‘marriagepartners’).Thesecommittedrelationships drive long-term growth, but can be price sensitive. Intense but fleetingbrand-consumerrelationships(suchas‘flings’)aremoreabletosupport charging a premium, but have a lower correlation with market share growth.

The first step is to recognise that people have different kinds of relationships with brands and tailor engagement models in order to manage customer relationships most effectively.

Brand will need to align to the appropriate model, with expectations and rules of engagement varying by relationship type. For example, a customer looking for a simple one-offexchangemighthavedifferentexpectationsofabrand’sservicelevelscompared to a loyal customer, and the one-offcustomermightbedeterredifthebrand’scommunicationsattempttobetoofriendly.Thefirststepistorecognisethatpeoplehavedifferentkinds of relationships with brands and tailor engagement models in order to manage customer relationships mosteffectively.

2. What does good look like? | 27

2. What does good look like?

2.1 Personalised vs. Too personal

2.2 Navigating data privacy concerns

2.3 Algorithms vs. Editorial curation

2.4 Brand led vs. Consumer enabled


The operational complexities around delivering personalisation are often discussed,inparticularthedifficultiesassociated with data management and infrastructure. However, articulating a vision for personalisation can be just as challenging.

Personalisation is still early days for many organisations and focused on targeted marketing communications rather than delivering an integrated customer experience. Inspirational case studies are few and far between (see Spotify case study, p29) and there is no single best practice for industry to follow. What good looks like can differbyvertical,bybrand,bymarket,and even by individual consumer. Brand owners need to consider what they are aiming for both in terms of the customer experience and the objectives of the organisation, and strike a balance across the following areas:

Personalised vs. Too personal Understanding how to create value through personalisation, without taking ittoofarintotherealmsofthe‘creepy’.See sections 2.1 and 2.2

Algorithms vs. Editorial curation Ensuring algorithms do not overly determine what consumers see or brands can express.See section 2.3

Brand-led vs. Consumer-enabledJudging when brands should lead and when they should empower consumers to lead.See section 2.4

Relevance vs. ReachWeighing up the right balance of relevance and reach for your business objectives.See section 9.1

What good looks like can differ by vertical, by brand, by market and even by individual consumer.


Marketing To promote its service in Canada, Spotify used listening data and local insight to create a hyper-targeted and relevant campaign for local audiences. Over 25 custom playlists were created for local neighbourhoods and landmarks across Canada based on the most streamed songs in those areas. Tailored OOH placements advertised playlists using insights about local moments when streaming provided a soundtrack to day-to-day life in those locales.

Within days of the launch, Spotify moved from third place to the number one music service in the App Store. The campaign playlists received over 2.1 million plays, with the most popular playlist receiving 193,000 plays in a single day. Overall, streaming onSpotifyincreasedby71%,makingitthe biggest music streaming service in Canada in three months.

Content discoveryEvery Monday Spotify delivers ‘DiscoverWeekly’,apersonalised anduniqueplaylisttoover80million users. The playlists comprise 30 songs that the user has never listened to before, but that data suggests they will love.

Case study | Spotify

A series of Spotify examples demonstrates how the organisation has used personalisation from marketing communications through to product and service delivery9


The recommendations engine uses two sources of data to deliver the compilation:

Information from the billions of playlists created by all Spotify users, which can be used to identify which songs are often grouped together and are listened to by users with similar tastes. For example, if a song you have not listened to often appears in playlists alongside your favourite tracks it is more likely to be recommended to you.


1 2

Auser’sindividualtasteprofile,whichanalyses the songs they have listened to or saved, to understand preferences in artist and micro-genres.

Inthefirstsixmonths,DiscoverWeekly was used by over 30 million listeners, driving positive reviews and word of mouth for the accuracy of the recommendations and for helping customers to beat the Monday blues.

You listen and save songs

Spotify develops your ‘taste profile’

Spotify identifies similar songs

that appear on those playlists

Spotify finds songs that fit your profile,

but that you haven’t listened to

Discover Weekly

Spotify users create billions

of playlists

How it works


New product delivery Research shows that music enhances athletic performance by reducing a runner’sperceptionofexertionandfatigue and by increasing happiness and excitement. Spotify launched a new feature, Spotify Running, which personalisesauser’smusicselectionbased on listening history,

music preferences and running pace. By matching the beat of the music tothebeatoftherunner’ssteps, the feature aims to help runners push themselves to run further and train harder.

By matching the beat of the music to the beat of the runner’s steps, the feature aims to help runners push themselves to run further and train harder.



Personalised vs. Too personal

Consumers are often exposed to poorly executed personalisation which can stray into the realms of the creepy. Typically, these experiences involve retargeting ads that appear to follow consumers around the web (long after they have bought the products featured), or recommendation engines making suggestions obviously based on personal data that people had not appreciated the organisation held on them, or would use in this manner.

These everyday experiences and the availability of new and personal datasets, such as social media data, have sparked debates around personalisation and the complexity and brand risks that are associated with it. Indeed, some campaigns – such as the launchofthe‘WatchDogs’hackervideo

game campaign (see case study, p131) – have intentionally drawn attention to the ability of organisations to build an ominouslydetailedprofileofindividualsbased on their publicly available data. (The‘WatchDogs’campaignispremisedon the idea that an assassin could build aprofileofanindividual,simplybasedon publicly available web data.)

To develop a coherent approach in this sensitive area, organisations will need to address strategic questions about personalisation.

2.1


The tipping point

Research suggests that personalisation delivers incremental returns but there is a tipping point beyond which experiences can become so highly personalised that consumers reject them.

One study by GfK provides evidence that initially consumers enjoy the personalisation of marketing communications, with steadily improving brand attachment as personalisation increases10.

The Uncanny Valley: The impact of increased personalisation on brand attachment

However, there then appears to be a threshold after which there is too muchpersonalisationforconsumers’comfort. The experience can become creepy and brand attachment rapidly declines.This‘uncannyvalley’effectwas noted by Oliver Feldwick in his award-winning paper, The Uncanny Valley: The impact of increased personalisation on brand attachment11.

Bra

nd a

ttac

hmen

t

Level of personalisation

A B C E FD G

Source: GfK


Thetippingpointfordifferentbrandsmay well depend on a variety of factors. Hyper-personalisation may be more appropriate in some categories than others.WhatisconsideredfittingforGoogle may not be right for a consumer goods brand, for instance.

The tipping point is likely to vary according to the utility and consumer value derived from the personalised experience, and the sensitivity and value the consumer attaches to their data.

Entertainment and streaming services suchasAmazonandNetflixareoftenheld up as exemplars of personalisation. These markets are characterised by awealthofchoices,highfrequency/low value purchases, and a consumer selection process which is made according to highly individualised preferences. These verticals will likely see immediate incremental gains from low levels of personalisation and will continuetobenefitfromhigherlevelsof personalisation than will likely be the case in other verticals, such as automotiveandfinance,forexample.

Research shows that in categories where consumers require a certain breadth and depth of information to inform their decisions (e.g. news, automotive, finance)andwheretheyoftenrelyonexpert advice, editorial curation by the brand becomes more important and needs to be given greater prominence.

Withinverticals,differentbrandswillalsoneedtobeconsciousofdifferentthresholds according to the type of relationship they hold with customers. Consumersmayhavedifferentexpectations of brands, depending on whether they have an emotional or functional relationship with the brand in question. There are also verycleardifferencesinreceptivenesstopersonalisationacrossdifferentpopulation segments – partly, but not wholly, based on demographics such as age, gender and lifestyle. Even within the same demographic segment, receptiveness may vary considerably between individuals, presenting additional challenges and risks for the brand.


Marketers need to start asking where the tipping point might be for their brand,becausethebrand’smarketingactivity may well be doing the opposite of what is intended and having detrimentaleffects.

They will need to develop a ‘test and learn’approachtounderstandhowbest to personalise their propositions and create value for consumers without tipping over into fostering unease about why and how such data is being collated and used. By focusing on consumer demand rather than the supply of data, brandsmayfindthatthemosteffectiveoption is personalising to groups of individuals rather than on a unique, one-to-one basis.

Clearly, cost and return on investment to the business will also be an important factorshapingdecisionsoverthe‘right’level of personalisation. Even if a brand believes there is clear customer demand forittoofferincreasedpersonalisation,the associated investment in data and content creation may mean that maintaining the incremental level of personalisationcannotbejustified.

By focusing on consumer demand rather than the supply of data, brands may find that the most effective option is personalising to groups of individuals rather than on a unique, one-to-one basis.


Navigating data privacy concerns

The complexities of consumer attitudes towards data privacy

Consumers are increasingly sensitive about the way their data is used by brands.TheTRUSTe/NCSAConsumerPrivacy Index recently reported greater anxiety among consumers about how personal information collected about them online was being used than fear of losing their principal source of income.12

These attitudes towards privacy are complex but it is clear that consumers can both win and lose from sharing personal data.

On the positive side, consumers can enjoy:

• Tailoredexperiencesandservicesdelivering relevance, convenience and value for the consumer.

• Freecontentorservices.Forinstance,Facebook’sservicesarepredicated on this business model.

Otherbenefitsmayaccruewhichare notspecifictotheindividualconsumersuch as:

• Publicservicebenefitsfromtheaggregation of data. For example, the aggregation of web searches of many individuals could help detect disease outbreaks. Similarly, the aggregation of location data can be usedtoimprovetrafficconditionsand reduce road congestion.

• Enhancedconsumerdatamayallow brands to better target their marketing investment, thereby increasingeffectivenessorenablingreduced spend and re-investment of the budget, for example in product, services or pricing.

Buttherearepotential‘costs’ornegative consequences consumers can face from both privacy violations and disclosure of their data.

Ryan Calo, a legal academic at the University of Washington, distinguishes between subjective and objective harms that can arise from real or perceived violations of data privacy13.

2.2


Calo cites the work of economist Alessandro Acquisti14, a leading authority on the psychology of privacy, who describes subjective harms as relating to “the anticipation of losing control of personal data [which] can include anxiety, embarrassment, or fear; the psychological discomfort associated with feeling surveilled; the embarrassment associated with public exposure of sensitive information; or the chilling effects of fearing one’s personal life will be intruded upon.”

The objective harms related to privacy, meanwhile, “can be immediate and tangible, or indirect and intangible” and range from damage caused by identity theft to time spent dealing with annoying telemarketing.

According to Acquisti, there are three main ways15 in which consumers can sufferfromdatadisclosure:

• Consumerscanmakemistakesastheydon’tfullyunderstandwhatmight happen if they reveal too much about themselves, particularly if they do not know how data is being collected or used.

• Thelifecycleinwhichpersonal data operates is now so complex it is impossible for individuals to work out when best to disclose such data and when to keep it to themselves.

• Evenifconsumerswereabletoaccess complete information and had the cognitive power to process it exhaustively, cognitive biases would typically lead to behaviours thatweresystematicallydifferentfrom those predicted by rational choice theory (which argues that individuals will always make the most rational and prudent decisions in their self-interest).

Consumers are therefore often not in a position to gauge the consequences of disclosing personal information. In any case, a privacy issue can arise insomanydifferentcontextsthatanindividual is unlikely to have completely constant preferences for every context. Forinstance,issomeonefindinga naked picture of you online on a par withthemfindingoutwhatyoulastpurchased at the supermarket?

Acquisti argues that there are so many differentelementstocalculatingthepotential costs to the users from a privacy violation that predicting how they will weigh up the value exchange involved in giving organisations their personal data is a far from exact science.


The implications for brands

It is clear that, above all, brands must avoid thinking simplistically about this topic. Privacy is an amorphous and complex issue.

Two areas are critical for brands to consider: 1. Transparency and brand trust All too often brands take the view that increasing the means by which consumers can exercise their privacy will be a threat to business. Arguably, there has been a history of institutions seeing how far they can go to capture data on consumers without necessarily making it clear that they are doing so or being transparent about the way in which that data will be used. However, we are starting to see some organisations making a virtue out of respecting consumer privacy.

There is a growing market for products that manage privacy or that set the bar higher in terms of the care being taken over how information is collected. Equally, we are seeing cases where companiesfacesignificantcommercialsetbacks for not appearing to treat their customers’privacywithduecare.

Research suggests that depending on the type of relationship consumers have with a brand, their expectations and level of trust can vary.

There is a growing market for products that manage privacy or that set the bar higher in terms of the care being taken over how information is collected.


An experiment by the academics Susan Fournier, Jennifer Aaker and S Adam Brasel16 illustrated how consumers had differentexpectationsofbrandsthattheyhadtransactionalandfleetingrelationships with compared to brands with which they perceived they had more lasting and intimate relationships.

In the experiment, two online photographic service brands were created, each with contrasting ‘personalities’usingcolour,languageandimages.Onewas‘sincere’inbrandpersonality, with classic and traditional core values suggesting sustained friendships with consumers. The other was‘exciting’,withamoremodern,irreverentfeelencouragingmorefleetingrelationships. A group of customers interacted with the service up to three times a week over a two-month period. Theywerethentoldthatastaffmemberhad accidentally erased their online photos. Two days later, they were sent apologies and informed that the online

albumshadbeenrestored.The‘sincere’friendship brand, which had developed strong bonds with its customers, sufferedmoreintermsofcustomerperceptionthanthe‘exciting’flingservice which was able to grow stronger than before the incident.

Aswiththe‘uncannyvalley’effectshown earlier (p33),the‘right’levelofpersonalisation and data collection will vary by brand and consumer according to the type of relationship held. Brands may well discover that their data privacy policieswillinvolvetrade-offsbetweenshort-term tangible gains and long-term intangible downsides as they identify the level of personalisation appropriate to them.

Brands may well discover that their data privacy policies will involve trade-offs between short-term tangible gains and long-term intangible downsides...


2. The value exchangeConsumers are increasingly recognising the value of their personal information and are demanding a more open conversation about what they will get in return for disclosure of this data.

For instance, audience members who provide Channel 4 with their social datagainaccesstothebroadcaster’sprogramme archive, personalised reminders for favourite programmes and exclusive content. Similarly, it is commonplace for apps and other providers to ask users to enable location services on their mobile phones in return for providing relevant map and search functionality.

These developments relate to the broader concept, developed by the academic and commentator Doc Searls17, of a future Intention Economy in which markets will become more oriented around the needs of buyers, whoaresufficientlyempoweredtobeable to compel businesses to tailor offeringstotheirneeds.

Organisations need to consider the value exchange they currently provide to consumers in return for the use of their data. This is not always straightforward since, as shown elsewhere in this report, data ownership in social media can be fragmented between platforms, third parties and brands, consumers may be unclear how or when they gave permission for use of their data, and individuals’viewsmaychangeastime or contexts evolve.

Failure to articulate the value exchange in a transparent way may not impact the brand in the short term but could have long-term consequences for the health of the customer-brand relationship. Given the very personal nature of social media, consumers may have higher expectations about social data than they do for other data types, and the potential fallout from poor data policies might therefore be worse.

Organisations need to consider the value exchange they currently provide to consumers in return for the use of their data.


Algorithms vs. Editorial curation

The consumer impact: is Google making us stupid?

Using algorithms has become a core part of delivering more relevant and personalised consumer experiences at scale. But it may not be without unintended consequences for consumers and their behaviour.

With more services being driven by algorithms, there is a risk that our exposure to new ideas and content – oneofthekeybenefitsoftheinternetand connectivity – is diminishing.

In an interview with The Wall Street Journal in 201018,Google’sEricSchmidtsummarised how he saw the role of technology developing:

“It will be very hard for people to watch or consume something that has not in some sense been tailored for them.” Eric Schmidt, Executive Chairman, Alphabet,(Google’sparentcompany).

In enabling technology to show us what it thinks we want to see based onourprofilesandbehaviour,somecommentators believe we are allowing technology too much control over our access to information and content.

Others have questioned whether technologyisalsoaffectingour thought processes and behaviour. The writer Nicholas Carr famously raised these questions in his piece for The Atlantic magazine ‘Is Google making us stupid?’19

2.3

It will be very hard for people to watch or consume something that has not in some sense been tailored for them.

Eric Schmidt, Executive Chairman, Alphabet


Carr argued that technology is making usmoreefficientandproductivebutalso risks reducing our capacity for concentration and contemplation. In his view, short-form content and the constant distraction of headlines and hyperlinks encourage us to scan and move on rather than immerse ourselves in more in-depth material.

For Carr, instant connectivity and ease of access to relevant information mean we no longer need to retain the same level of information. Algorithms are beingusedtopromoteefficiencyandautomationinhowwefindinformationand extract meaning from it. But while we are increasing our productivity of thinking we are taking less time to engage our brains without distraction and to synthesise information from text.

Possible changes in our creative thought process as a result of personalisation have also been studied. An unpublished experiment(bythisreport’sco-author, Colin Strong) found that individuals exposed to greater levels of personalisation exhibited less creativity in a classic test where they were asked to list multiple uses for a brick. This couldbeconsideredareflectionofthedifferencesinhowthebrainisengagedwhen exposed to personalised content.


Striking a balance: ‘need to know’ vs. ‘want to know’Consumption of algorithmically served content online is rapidly growing, but in many cases this can, and should, be complemented by editorially curated content. Consumers often require both informationthatthey‘needtoknow’andinformationthatthey‘wanttoknow’from brands, and this balance will vary by category.

The aforementioned Yahoo! study (p16) found that consumers demand the greatest levels of personalisation from organisations that most closely align with their passions, such as entertainment brands. In categories such asnews,automotiveandfinance,whereconsumers require a certain breadth and depth of information to support their decisions, editorial curation and expert advice are more important.

The increasing use of social media as a means of consumers keeping up with breaking news has sparked debate on this topic. The algorithms used to tailor individuals’contentfeedsmaybeactingasfilterslimitingusers’abilitytodiscover‘needtoknow’contentorbreadthofopinions.

In talking about the importance and role of algorithms, Mark Zuckerberg is reported to have told colleagues:

“A squirrel dying in your front yard may be more relevant to your interests right now than people dying in Africa.” Mark Zuckerberg, Co-Founder and CEO, Facebook20.

By contrast, it could be argued that the role of traditional media organisations is to expose the public to a wide range of subjects – not just the ones that peoplemight‘wanttoknow’butalsotheinformation and range of viewpoints that they‘needtoknow’toenablethemtofunction in a democratic society.


In his TED Talk, ‘Beware online filter bubbles’,EliPariser21 describes our shift from a world where broadcasters acted as the main gatekeepers of information to one where algorithms determine what many users do and do not see. Yetalgorithmslackthebroadcasters’civic responsibilities to present us with a view of the most important information andarangeofdifferentopinions.

TheBBC’sapproachtopersonalisationillustrates the tension and need to strike the appropriate balance. The corporation is continually adapting the personalised experiences it delivers (see case study, p111).Buttoensureitfulfilsitspublicservice remit ‘to inform, educate and entertain’audiences,theBBCneedsto balance personalised elements with content that its editorial teams believe thepublic‘needstoknow’.

The challenge exists across categories where brands must consider that people’sbehaviourcanoftenhavetwo sides which are equally important to them. Using the media space as an analogy, there is the content we consume as our aspirational selves – this could be educational, cultural or currentaffairscontent–andthecontentwe consume as our impulsive selves which relaxes, amuses, or serves as a distraction. Algorithms that serve up high volumes of the latter content type can serve to limit our exposure, awareness and consumption of the former type of content.

...algorithms lack the broadcasters’ civic responsibilities to present us with a view of the most important information and a range of different opinions.


Fear of missing outConsumers want to see what peers have seen. There is a fear of missing out on the conversation, on the newest and hottest trend and on the next big thing. Ifourpeersarebenefitingfromcontentor an experience, then we are likely to want in too, or at minimum we want to know the experience exists and enough about it to judge whether ornotwewouldalsobenefitfromit. This behaviour has been perpetuated and reinforced by mobile connectivity and social media use, which are creating an environment where we are connected and have instant access to a wealth of information, including the thoughts, discoveries and recommendations of our peers.

Algorithms can be considered to contribute to this fear of missing out by limiting consumer exposure to consistent content and experiences. Mybehaviourandprofilemightleadme to be served one experience and my peer another. Although I value the relevance of my experience, it is also natural that I want to know what my peer was shown in case I have missed out on something.

One way in which brands have long recognised this fear and behaviour and used it to aid discovery is the use of peer-to-peer curation within owned properties.‘MostWatched’or‘MostRead’tabsand‘MostPopular’productfiltersareusedtoshowcasepopularcontent or products amongst their customer base. In some cases, these arefurtherrefinedtowhatispopular inacustomer’sownnetworkthroughthe use of a social login. For instance, loggingintocertainmusicorfilmstreaming services via your Facebook details can be used to enable you to see what content peers in your Facebook network have consumed.

Although I value the relevance of my experience, it is also natural that I want to know what my peer was shown in case I have missed out on something.


How balance supports brand strategy

Fromthebrand’sperspective,providing a balance of algorithm-driven personalisation and editorial curation can be instrumental in helping consumers understand the brand offeringandpositioning.

This report has already outlined the potentialbenefitsofpersonalisingcontentbasedontheuser’sstatedpreferencesorthe‘wanttoknow’category. However, by ensuring consumers are also exposed to the ‘needtoknow’,brandscangiveaudiences a greater opportunity to understand the full breadth of products

and services available from the brand, as well as its personality and what it stands for. Editorial curation can be a key way for brands to demonstrate their authority and expertise in a particular area, whether the category isfinanceorfilms.

Editorialcurationcanalsoinfluencethe meanings attached to the brand amongst the wider market by helping to create a shared common perception ofitsoffering.Althoughthiseffectmayappear‘wasteful’whencomparedtoa more targeted approach, it has been argued that what appears to be wastage can in fact deliver longer-term brand effects(see section 9.1).

Editorial curation can be a key way for brands to demonstrate their authority and expertise in a particular area, whether the category is finance or films.


Brand led vs. Consumer enabled

Personalisation is often considered as a one-way, brand-led experience, whether that be through algorithms or editorial curation.However,today’sconsumerisincreasingly in control. Unprecedented access to information and peer networks and the ability to engage in two-way dialogues through social media have led to a change in brand-consumer relationship dynamics. Brands are increasingly enabling consumers to take an active role in shaping their experiences. This can deliver more effectiveexperiences,butalsohasbroader implications for the future of relevance and how it can be delivered.

Explicit preferences and transparency

The balance of algorithms and editorial curation discussed in section 2.3 can be augmented by a level of consumer transparency and explicit control over the experience. Research studies suggest this is both demanded by consumersandcanbeamoreeffectivemeans to personalise their experiences. The Yahoo! personalisation study found that consumers are on the whole aware of personalisation and they believe it bringsrelevanceandefficiencytothecontent experience. However, over 60%ofrespondentswantedtoknow how and why the personalised content was selected for them, with 29%wantingalittlecontrolovertheirpersonalisedexperienceand41%asking for complete control.

2.4

41%of consumers asked for complete control of their personalised experience in a Yahoo! study


Often, organisations are working with incomplete or inaccurate data in order to serve personalised experiences. By enabling consumers to set explicit preferences or support machine learning by dismissing irrelevant recommendations, brands can draw on both implicit and explicit signals to deliver a better experience.

Evidence also suggests that this transparency and the mere perception or awareness by a consumer that an experience or message has been personalised can increase its effectiveness.Thestudy,‘An Audience of One: Behaviorally Targeted Ads as Implied Social Labels’,showsthat where consumers were served ads known to be based on their recent individual behaviour they were more likely to want to purchase the advertised offer,comparedtowhentheywereexposed to ads based on demographic segments (such as age or gender)

or those with no targeting. This greater effectivenessisattributedtotheimpactofthepersonalisationontheconsumer’sself-perception. This speaks to the businessbenefitsoftransparencyand of making explicit to users the basis on which preferences and recommendations have been created.

AmazonandNetflixprovidetwoexamples of this approach, serving recommendations based on ‘because youbought’or‘becauseyouwatched’to help audiences understand why they are being served content as well as giving them opportunities to adjust their preferences and the recommendations they see to create a feedback loop (see section 1.2 for further discussion).


Customisation and peer-to-peer community

The vision set out by Future Foundation in section 1.1 describes the emergence of consumer enablement as a key dynamic in the development of brand-consumer engagement. In future models, some brands will provide the tools and the stage enabling consumers to take charge of their experience. In this view, consumer control will move beyond explicit preferences towards a model where consumersareself-sufficientandtherole of the brand is to help consumers to achieve their potential. This trend in engagement is already evident and there is a clear need for brands to consider the implications it has for their personalisation strategies.

Customisation and co-creation already feature in many engagement strategies, enabling individuals to tailor and share brand content themselves. Relevance is enabled by the brand and created by the consumer, leveraging earned media to drive content reach. Strategies are typically social by design. They are reliant on advocacy and earned media rather than data, drivingeffectivenessthroughtrustedpeer-to-peer recommendations and enablingbrandstomatchtheefficiencyofpersonalisationwiththeeffectivenessof mass reach. This balance between relevance and reach and the role of community means that activity can beeffectiveatdrivingbusinessKPIssuch as awareness and advocacy.

...consumer control will move beyond explicit preferences towards a model where consumers are self-sufficient and the role of the brand is to help consumers to achieve their potential.


More broadly, the role of community and peer-to-peer recommendations will become integral to the customer experience and to personalisation.

Communities provide an always-on, two-way dialogue, enabling consumers to become directly involved in the brand. This involvement can range from being given a role in new product development and innovation communities, to providing peer-to-peer support through customer service and product reviews and recommendations. Two-way collaboration and community elements will be essential to the success of consumer-enabled personalisation. The limited number of success stories surrounding experiments with social recommendation through social login provide examples of this. If social recommendations are implemented as part of one-way communication strategies, execution can often come across as forced and invasive.

The emergence of consumer-enabledmodelscanprovideeffectivestrategies that complement brand-led personalisation and that can deliver on differentobjectives.

Concerns around data privacy and shifts in data ownership could also mean that consumer-led models are inevitable to the future of engagement and relevance. Companies are already springing up to empower customers to own and leverage their own data. Citizenme is one example discussed further in section 9.4.

As we move towards a data economy where ever more consumer data is availabletousandwhereconsumers’feelings about their personal data and its uses continues to grow more complex, brands will need to consider the role of consumer control and the balance of brand-led vs. consumer-enabled relevance.

3. Re-evaluating the role of social media | 51

II. Using social media for personalisation

3. Re-evaluating the role of social media

3.1 Redefiningsocialmedia3.2 The social organisation: breaking out of the silos3.3 Social media and the future of brand-consumer engagement

4. Social data

4.1 Whatissocialdataandhowdoesitdifferfromotherdata?4.2 Opportunities and challenges of social media data4.3 Social media data availability and access

5. Approaches (I): personalised marketing

5.1 Rethinking segmentation and targeting (interests, attitudes and emotions)

5.2 Micro-influenceandnetworkmarketing(relationships)5.3 Moment marketing (behaviour and intent)

6. Approaches (II): personalised experiences

6.1 Social CRM6.2 Integrated customer experience

7. Approaches (III): customisation, co-creation and community

7.1 Customisation and co-creation7.2 User-generated content7.3 Data-driven creatives7.4 Community and social recommendation


3. Re-evaluating the role of social media

3.1 Redefiningsocialmedia

3.2 The social organisation:

breaking out of the silos

3.3 Social media and the future of

brand-consumer engagement


Definitionsofsocialmediawillcontinuetochangetoreflectshiftsinconsumer behaviour and technology, but the element of peer-to-peer interaction will remain constant. Most often, the term brings to mind a handful of the most popular online social networks, such as Facebook, Twitter and Snapchat, which enable users to connect, publish and share content and ideas.

However, the biggest social media platforms already stretch beyond this, typically enabling one or more ofthefollowingfivesocialbehaviours:

Redefiningsocialmedia

3.1

Connecting

Enabling users to connect and exchange ideas and/orcontentisacoresocial behaviour that unites platforms such as FacebookorLinkedIn,messaging services such as WhatsApp, and interest-based networks and forums (e.g. Mumsnet, The Student Room).

Publishing

Enabling users to publish and share content among a network or group of followers. These platforms include those focused on the creation and sharing of user-generated content (e.g. Instagram) and those focused on content curation and bookmarking (e.g. Pinterest, Paper.li).

Crowdsourcing and Co-Creation

Enabling users to contribute to or source information,ideasand/orcontent from a large group of users. This includes wikis (e.g. Wikipedia), ratings and reviews (e.g. TripAdvisor), innovation and new product development (e.g.LegoIdeas,GE’sFirstBuild), and service models(e.g.giffgaff).


Collaboration

Enabling users to work jointly on projects and other activities (e.g. Facebook at Work, Yammer).

Participation

Enabling users to participate in social activities, such as social gaming (e.g. Zynga), immersive experiences(e.g.virtualreality)andproducts/ services within the Internet of Things which centre on peer-to-peer interaction and participation.

Butthedefinitionandconceptofsocialmediaisnolongerlimited to third-party platforms. In addition to working with social platforms, many brands are also considering the role that community and peer- to-peer can hold more broadly within their organisations.


The social organisation: breaking out of the silos

Initsearlierphases,socialmedia’scorevaluewasperceivedtobeitspotential as a communications channel to help marketers drive reach. However, it is increasingly becoming integral to other teams within the organisation, including customer services, human resources and insight.

Similar to the shift already seen in digital, social is breaking out of organisational silos leading to a seismic shift in engagement and proposition delivery.

Insight

Mining publicly available and privacy-compliant social media data for unprompted research and insight (e.g. mining brand mentions online, including applying sentiment and text analytics, to understand brand perception) and using online communities to deliver prompted insight.

Communication

Using social media platforms and capabilities for external communication (e.g. marketing communication or customer service delivery) or internal enterprise communication and collaboration.

3.2

For many organisations, the role of social already spans three key areas:

Product/service delivery

Delivering products and services founded in or enhanced by community and social media capabilities (from social gaming through to the ecosystem of products described under the Internet of Things heading which are based on peer-to-peer interaction).


Social media and the future of brand-consumer engagement

Social media can enable relevanceConsumer trends demonstrate personalisation will be foundational across future models of engagement (see section 1.1). Social media will be critical to brands as an enabler of direct one-to-one communication and relationships and as a rich source of data which can fuel personalisation across brand touchpoints.

Social media can enable emotional and authentic connectionsThe rise of relevance in the brand-consumer relationship will lead to a change in the role and importance of brand. Building an authentic and emotional connection will be an essential part of many engagement models. Social media enables brands to engage with consumers in a familiar space where consumers choose to spend an increasing proportion of their time. Social enables a direct, two-way dialogue and supports brands in telling stories in richer ways than many

othermediaoffer,includingthroughits diverse and rich media formats, sequential messaging, interactivity and engagement, and co-creation opportunities.

Social media empowers consumers In some sectors, brands are becoming the enablers while consumers become the storytellers and creators. Social media will continue to be key to this shift in power and the role of community and peer-to-peer could take centre stage in many engagement models. This trend has deep implications for the future of relevance and personalisation models, as customisation and co-creation become core to delivering these experiences.

3.3

Social media will be integral to how brands define their future relationships with consumers:

Social media will continue to be key to this shift in power and the role of community and peer-to-peer could take centre stage in many engagement models.

4. Social data | 57

4. Social data

4.1 What is social data and how does

itdifferfromotherdata?

4.2 Opportunities and challenges

of social media data

4.3 Social media data availability

and access

4. Social data | 58

What is social data andhowdoesitdifferfrom other data?

4.1

The term ‘social media data’ refers to all forms of data collected through social media, including:

Information explicitly shared by social media users:− profiledata(e.g.gender)− content(e.g.imagesposted)− conversation(e.g.interactions

and conversations with other users)

− likes/votes(e.g.likes,favourites)

− shares(e.g.retweets)− consumption(e.g.clicks)− connections(e.g.fan,

following)

Additional associated data: − metadataassociatedwith

social media user activity (e.g. time, location)

− deriveddatapointsassociated with social media user activity (e.g. topic and sentiment classificationforapost,and theinfluencescoreofasocialmedia user)

Conversation

Like/votesShares

Consumption

User profile

Connections

Content

4. Social data | 59

How does social media data differfromotherdata?

Socialdatadiffersfromotheravailabledatasets through the type, granularity and timeliness of the information that itofferstobrands.Thesecharacteristicsprovide marketers with a range of opportunities as well as operational complexities if they are to take full advantage of them.

Historically, as marketers, we have had to rely on a variety of interventions in order to measure and understand human behaviour. We have placed people in laboratories and looked to see how they operate under controlled conditions. We have asked people survey questions to elicit insight into their behaviours and attitudes. We have attached electrodes to track the inner workings of their brains. We have visited peoples’homestobetterunderstandhow they live and have given them life-logging tools that record their daily activities.

Today, we are able to explore what people actually do rather than what they say they do when prompted. Social media is thus driving the trend towards‘datafication’ofhumanbehaviour. It captures data of such a granular and intimate level that it can tell us not only what people are doing but also what they are thinking and what is shaping their behaviour. We have perhaps not yet fully appreciated the implications of this for our understanding of human behaviour, but they are undoubtedly enormous.

Today, we are able to explore what people actually do rather than what they say they do. Social media is thus driving the trend towards ‘datafication’ of human behaviour.

4. Social data | 60

Some of the ways in which social media is driving this trend include:

Datafication of interests, attitudes and emotionsThe explosion of self-reporting has led us to provide very intimate details of ourselves. With billions of people now using social media, we have anincredibledatabaseofpeople’sinterests, opinions and feelings. Many market research companies already use this at an aggregate level to obtain detailed information on audience sentiment relating to particular topics, often discussions of brands, products and services. As social media moves from text to rich media, so analytics is evolving to help us understand the content of these images and videos.

Datafication of relationships and networksWe are now not only able to see the way in which people relate but with whom they relate. So again, social media has transformed our understanding of relationshipsby‘data-fying’professionaland personal connections. Historically, our ability to collect relational data has necessarily been through direct contact and this has generally restricted studies of social interactions to small bounded groups such as clubs or individuals living

and working in a particular geographic area. Social media now allows us to explore relationships on a global scale.

Datafication of behaviour and intentSocial media also provides real-time contextual information about users – their behaviour, location and intent. For example, a user might post the intention to book a holiday or share aplanneddayout.Auser’slocationdatamight also show habitual locations and whentheyarevisitingdifferentareas.

With billions of people now using social media, we have an incredible database of people’s interests, opinions and feelings.

4. Social data | 61

Social media data vs. Traditional research dataOur ability to generate insight on interests and attitudes means we often draw comparisons between social media data and traditional research (e.g. survey data).

Differences to consider include:

Representative vs. Customer levelSocial media data requires caution in how it is used for insight. Itrepresentsaspecificuserbase and will not be representative in the same way as traditional research can be. However, crucially for targeting and personalisation, social media data can be collected at a customer level and used to augment individual customerprofiles.

Prompted vs. UnpromptedSocial data can show what matters to the consumer. The data has not been generated because someone has been stopped in the street and asked a question; the data is there becauseit’ssomethingthemarket wants to express an opinion on.

Authenticity and BiasSocial media is a platform where consumers write frankly on all manner of topics which can give brands access to unparalleled insight. However, we need to consider the public and

self-reportednatureofauser’s activity and whether this provides an authentic and complete representation ofthemselvesoronewhichreflects what they want others to see.

TimelinessSocial data can be collected in real time and collated relatively quickly compared to survey data, enabling brands to act on it in a more timely manner. Socialdatapointsavailablemaydifferin recency and frequency. For example, Facebook‘likes’maygenerateinsightintoauser,butwillall‘likes’berecentand still relevant enough to show the user’scurrentinterestsandpreferences?

GranularitySocialmediainsightisspecificanddetailed. For qualitative insight, social isoftenreferredtoastheworld’slargestfocus group, contrasting with the tick-box approach of surveys and polls whereopen-endedfieldstendtobelimited by budget, time and resource. This qualitative detail in social data can help to convey reasons behind statistical results, making them more actionable than would otherwise be the case.

CostRelative to other datasets, there is low investment required to collect and analyse many social datasets, which are often publicly available.

4. Social data | 62

Opportunities and challenges of social media data

Opportunities of social dataSocial data is most often used at an aggregate level for marketing evaluation and consumer research. Personalisation applications are still relatively nascent in thisfield,butrepresentanareaofrapidgrowth in interest and experimentation.

Marketing and communications evaluationQualitative and quantitative insight from social media data can be used to measure and improve marketing and communicationseffectiveness.Thisincludesevaluatingtheeffectivenessof social media itself and using social mediadatatoevaluatetheeffectivenessof other marketing activity. Such evaluationcanexploitsocialmedia’sability to track activity in real time and enable brands to course correct.

Research Social media data can be used as a complement to (or in some cases a replacement for) traditional research to inform planning and creative processes. Specificusecasesinclude:

• Brand and marketing insight, e.g. Brand perception and competitive positioning; informing marketing content/channel/audience.

• Product insight, e.g. Product feedback and development; discovering unmet needs and category trends to inform New Product Development.

• Consumer insight, e.g. Consumer Decision Journey analysis; audience segmentation.

4.2

The differences between social media and traditional data sources highlight a number of opportunities and challenges.

4. Social data | 63

PredictionThe insight that social media data brings into human behaviour means that it is often considered a useful predictive tool. There has been a lot of excitement and experimentation related to this potential use.

For example, a study led by Cambridge University22 used data from approximately 37,000 users and 42,000 venuesinLondontobuildanetworkof Foursquare places and the parallel Twitter social network of visitors, adding up to more than half a million check-ins over a ten-month period. From this, researchers were able to quantify the social diversity of a particular place and predict when a neighbourhood would go throughtheprocessofgentrification.

However, a number of studies have shown we need to pay close attention tothespecificusecasesandpossiblepitfalls of prediction from social data. In many cases, social data is used in isolation without accounting for context or use of other data sources.

For example, Daniel Gayo-Avello, of the University of Oviedo in Spain, has disputed the ability of Twitter to predict elections. Gayo-Avello undertook an analysis23 of the research done to date in this area and concluded that the assumptions underlying much research wereflawed.Hisanalysissuggestedthatwork had generally assumed all tweets were trustworthy, representative and not affectedbyself-selection.Alloftheseissues, he claims, explain why he was notabletofindanypaperthatwas able to provide a credible prediction of a future election result based on social media.

These studies highlight the fact that prediction is not a straightforward matter. More research is needed to understand the context in which predictions work well and when they need to be treated with greater caution. Prediction from social data needs to be approached with thought and the recognition that whilst there are clearly gains to be had, predicting outcomes, particularly of complex social phenomena (such as consumer buying behaviour), needs careful consideration and investment.

4. Social data | 64

Personalisation and targeting Social media data can augment customerprofilestodeliverenhancedinsight, targeting and personalisation. Datatypesavailablecanincludeprofileand demographics and the following types outlined in section 4.1 which representspecificvaluedriversforthe use of social media data vs. other datasets:

There are opportunities to use this data to create more relevant experiences in-channel using social media platform targeting. Where data can be integrated into a single customer view, it can be used to enrich and fuel cross-channel experiences. For example, insight on interestsorproductaffinitygleanedfromsocial media can be used to automate content personalisation on a website or in email marketing.

Oneofthebenefitsofsocialmediadata is the richness and persistence of the dataset available at a customer level. This data enables us to better understand who the customer is, their preferences and personality.

Interests, attitudes and emotions1.

2.

3. Behaviour and intent

Relationships and networks

Where data can be integrated into a single customer view, it can be used to enrich and fuel cross-channel experiences.

4. Social data | 65

Predicting Personalities with Facebook Likes

Source: ‘Computer-based personality judgments are more accuratethanthosemadebyhumans’,UniversityofCambridge

Research by Cambridge University24 demonstrates how this insight can be incremental to existing customer profilesbycomparingtheinsightintoaperson’spersonalitygeneratedthroughsocial media data to that articulated byaperson’slovedones.Thestudydiscoveredthatauser’sFacebook‘likes’are a better predictor of their personality than their friends and family.

This research also demonstrates the opportunities for moving beyond delivering personalisation and recommendations based on the consumer’shistoricalbehaviourand

transaction data. Our understanding of our customer and what we can serve them is currently often based on what they have already bought from us (and what others who look like them have bought) and is limited by the length and richness of this history. This can be a particular challenge in understanding new customers. The use of social data to inform personalisation and predictive analyticshavesignificantimplicationsfor understanding and delivering experiences both for loyal existing customers, but also for newer customers wherewedon’thavethisestablishedrelationship and history.

By analysing...10 LIKES70 LIKES150 LIKES300 LIKES

...We know your personality better than your...Work colleagueFriend/RoommateParent/SiblingSpouse

The average person has 227 Facebook Likes

4. Social data | 66

The 3 Vs of big data: Volume, Velocity and VarietySocial media brings the challenges of big data to organisations. It delivers high volume, real-time data: in 2014, Facebook was reportedly garnering over fourmillion‘likes’perminutefromusersand Twitter users were sending over 300,000 tweets25.Definingwhatdatatocollect and how to collect it, and how to organiseandanalysethisdataeffectivelycan be a challenge for any organisation. Variety adds further complexity as social data can take structured and unstructured formats (for example, tweet text is unstructured social data, while tweet metadata such as location tends to be structured) and the rich media analytics required to understand image and video content are still emerging. In developing their social data strategy, brands need to set clear objectives and requirements to inform which data points to collect and how to do so.

OwnershipSocial data can include internal and external data points (for example, comments on owned web properties are internal, while interactions on a third-party network such as Twitter or Instagram are external). This ownership has implications for access to and use ofdifferentdatasets.Accesstoexternaldata points is dependent upon the platformowners’strategies,whichcanbe subject to change with little warning. Brands need to remain alert to this data dependencyindefiningtheirapproachand strategy.

Privacy and ethicsFrom a brand-consumer relationship perspective, this is a complex issue to navigate. Consumers do not have a linear relationship with personal data and the implications on brand trust can be severe if brands get it wrong (see section 2.2). From an operational perspective, organisations need to put data governance processes in place andupdatethesetoreflectchangingregulations relating to data privacy.

Challenges of social dataSocial data presents more challenges for organisations than other media in terms of:

4. Social data | 67

Data integrationUse of social media data at a customer level is still relatively nascent. Within marketing, data is often used by brands at an aggregate level, on an ad hoc basis, or in silo from other marketing channels and the rest of the business rather than being integrated into a single customer view. This is both due to the immaturity of use cases and shared best practice and the complexities associated with integrating and matching social media data with other sources.

Differenttechniquesarebeingusedby organisations, including the use ofanindividual’ssocialloginandauthentication (explored further in section 6 on personalised experiences). But these are often the exception rather than the norm. By sharing knowledge and case studies through initiatives such as #IPASocialWorks, industry will be better placed to experiment with and derive value from social media data.

AuthenticityThe limitations of social data, its authenticity and validity, are often questioned.Doesauser’sactivityon social media truly represent their personality and behaviour, or are they projecting a false self-image that shows only what they want others to see? Equally, a user will not talk about everything that matters to them, only those parts of their life that they wish to share publicly. There are clear, potentiallydifficultcharacteristicsofsocial data which are unique to this dataset. These should not detract from its ability to deliver incremental value, but should be considered in developing and communicating its use cases.

Does a user’s activity on social media truly represent their personality and behaviour, or are they projecting a false self-image that shows only what they want others to see?

4.3 4. Social data | 68

Social media data availability and access

The availability and access of social media data will vary:

• Byplatform,accordingto eachplatform’smaturityandmonetisation strategy.

• Byuserbehaviour,according to how much information each user provides to a platform throughhisorherprofile and platform activity.

• Byprivacysettingsand permissions granted, according to both platform policy and to individual user settings.

Broadly, there are two levels of social media data access for brands:

In these instances the social media user has agreed that his or her data and activity can be openly viewed by any other user. For example, username, bio and location are examples of Twitter profiledatapointsthatareoftenpubliclyon view. These data points, and data suchassentimentandinfluencethatarederivedfromusers’profilesandactivity,are privacy-compliant and accessed through third-party social engagement and analytics tools to be used either at a customer level or at an aggregate level for research and insight purposes. The challenges brands face in matching this public data to their internal CRM database records have to date limited attempts to use customer-level data in these ways.

1. Public profile and interaction data

4. Social data | 69

In these instances the social media user hassethisorherprofileandactivitysothatonlyconnectionsoradefinedgroupof users can see them. For example, Facebookprofiledatasuchasauser’sbirthday and home town and interactions such as status updates are often set to private by users so that only friends can see them. These data points can be accessed at an aggregate level on an anonymised basis and are often used by advertisers for insight and targeting. Some data points can also be available

at a customer level through brands asking for and users granting extended permissions, often enabling data to be matched to CRM records by using the individual’semailasauniqueidentifier.

This private data will typically include additional customer data points such asprofile,interestsandconnections.However, the data available varies by platform and by user activity and profilecompletion.

2. Private profile and interaction data

Availability of profile data varies by platform

Email

Name

Country/City/Locale

Birthday

Gender

Profile photo

Friends/Connections

Source:JanrainSocialProfileNavigator;LoginRadiusSocialProfileDataPoints

4. Social data | 70

Name

Facebook Profile Data

Twitter Profile Data

Source:JanrainSocialProfileNavigator

Media PschographicGeneral interests

Hobbies

Personal URLs

Identity

Identity

Contact info

NameLocation

Location

Demographic

Demographic

Activity

Activity

General

Name

Formatted name

Timezone

Display name

Current location

Profile photo

Homepage

About me

Status

Photos

Religion

Political views

Relationship status

Languagesspoken

Gender

Photos

AlbumsVideos Status

Birthday

Current location

TimezoneAddresses

Display name

ActivitiesGames

Books

Music

GroupsQuotes

LikesHeroes

TV Shows

Movies

Sports

Profilephoto

Interested in meeting

Interests

About me

Name

Emails

Verifiedemail

Homepage

URLs

Organisations

Addresses

5. Approaches (I): personalised marketing | 71

5. Approaches (I): personalised marketing

5.1 Rethinking segmentation

and targeting (interests,

attitudes and emotions)

5.2 Micro-influenceandnetwork

marketing (relationships)

5.3 Moment marketing

(behaviour and intent)


The majority of social media personalisation cases today focus on deliveringmoreeffectiveandmoreefficientmarketingcommunications.Brands are exploring how to use the datasets and targeting capabilities associatedwithsocialmediatorefinethe audiences they are reaching and tailor the message delivered to these audiences.

We saw in section 4 that social media can give us access to unprecedented levels of information about an individual. Inadditiontoprovidingprofileanddemographic information, it can give us insight into additional areas which representspecificvaluedriversforthe use of social media data as a complement to other datasets. These areas are typically:

1. Interests, attitudes and emotions

2. Relationships and networks

3. Behaviour and intent

In sections 5.1 to 5.3, we explore how brands are using each of these three types of data to deliver more relevant marketing communications.


Rethinking segmentation and targeting (interests, attitudes and emotions)

Traditionally, segmentations would have involved creating only a few groupsofconsumers,reflectingthedataavailableandarelianceonchannels and technologies with limited capacity for personalisation. Data typically used would include:

Customer data First-party data in the form of customer demographics and transaction history (e.g. customer value, frequency of purchase, average value per transaction etc.). This data provides a robust guide to current and historic behaviour. However, itfailstoconsidertheindividual’swider buying behaviour with competitor brands or attitudes and future needs. It also only relates to current (or lapsed) customers, on whom the organisation has data, rather than the wider potential customer base.

5.1

Market researchThird-party panel and survey data, which is often used to develop segmentations that are designed aroundconsumers’needsandattitudes. Whilst these segmentations are often a powerful means of informing marketing activity, they are typically hard to relate to actual customer behaviour.


Today, greater customer-level data is available and the media landscape is changing. This includes broadcast media where addressable TV is now available in the UK with products such as Sky AdSmart, for example.

Example

EE

To promote its superfast 4G network, EE created variants of its video ad totargetaudienceswithdifferentpassions on Facebook. For example, football fans on Facebook were shown a football- related ad. Results showed that ads with personalised creatives were twice as likely to be viewed compared to non-targeted creatives, and that they delivered twice the uplift intraffictotheEEsitetofindoutmoreabout the 4G network.

See case study, p76

Example

Coca-Cola

For its 2014 Super Bowl campaign, Coca-Cola delivered tailored video creatives to 13 micro-audience segments. Segments were based ondifferentethnicities,lifestylesand passions. As part of a broader campaign, the brand succeeded in reversing its sales decline and increasing consumption in its target market of 19-24-year-olds.

See case study, p77

Social media data holds particular value in this context. It provides rich data around demographics as well as about affinities,opinionsandinterests.Brandsare using these data points to improve theirunderstandingandidentificationofcustomers and prospects throughout the purchasefunnelandtoreachdifferentconsumerswithdifferentmessagestodrive improved business outcomes.

Importantly, these strategies enable brands to deliver relevance at scale. Thisreflectsthefactthatsocialmedianetworks now have vast potential

reachandofferbrandstheabilitytotarget millions of users based on rich customer-level data.


Social data can be augmented by third-party data either through products available from social media platform owners or separately. For example, through its partner programme, Facebook is able to augment its user targeting with data from database marketingcompanies(suchasDLX,Acxiom and Mosaic). Where social media data is publicly available, third-party agencies have also developed derivedsocialmediadatapointstoofferimproved platform segmentation and targeting. For example, some companies offeraugmentedTwittersegmentationby using publicly available user and network data and derived data points aboutinfluenceandaffinities.

Insomeinstances,abrand’sowncustomer data is matched with that of a social media platform to facilitate andimprovetheeffectivenessofthebrand’stargetingefforts.Forexample,the advertising products Facebook Custom Audiences and Twitter Tailored Audiences allow brands to match their CRM records to individual users based onuniqueidentifiers,suchasemailaddresses, thus facilitating advertising todefinedcustomergroups.Duetothecomplexities around the ownership, access and integration of social media data, this data is often held and used in silo for a given social media platform.

1. Third-party data integrations2. First party data integrations and social CRM

Integratingfirstandthird-partydata

Most social media data strategies are currently operating in channel silos, using social data to deliver targeting through social media platforms. First and third-party integrations are becoming increasingly common due to the release of social media platform advertising solutions that help marketers to augment their targeting:

Thelayeringoffirstandthird-partydataoversocialmediadataandtargeting options has become commonplace. Hitherto, there have been relatively few examples of social media data being integrated into an organisation’s360-degreeviewofthecustomer,thoughexamplesofthis are beginning to emerge (further discussion in section 6).


Objectives To increase awareness and consideration of its superfast 4G network, the EE team used Facebook to extend the reach of its TV campaign in a more personalised way.

Strategy Four pieces of original video content were created for Facebook. To reach the 14 million football fans on Facebook, the creative showed someone watching an England football game. With the match tied going into the 91st minute, England bursts through on goal, looks poised to score and…the screen BUFFERS! In another example, fans of the‘XFactor’wereshownpresenterDermotO’Learyraising the microphone to announce the winner and…BUFFERING!

ImpactThe campaign results showed that on average: • Personalisedcontentwas

2Xaslikelytobeviewed• Thebest-performingcontent

was3Xmorelikelytobeviewed• 2Xasmanycustomersvisited

thewebsitetofindoutmore aboutEE’sSuperfast 4G network

Case study | EE

Bufferface

EE’s Bufferface campaign targeted Facebook users with tailored video creatives to communicate its message in a more personalised way, at scale and across devices.

Personalised content drove

2xmore customers to findoutmore


Case study | Coca-Cola

It’s Beautiful

Coca-Cola delivered tailored creatives to micro-audience segments to create a heartfelt experience for Americans during the 2014 Super Bowl

ObjectivesWith sales volumes declining in 2013 for the ninth consecutive year, Coca-Colawantedtore-invigorateAmericans’loveforitsbrand.Thebrand’sobjectivewastoremindAmericansthatCokewasmorethanasoftdrink:itwasaniconthatcoulduniteAmerica’sdiversecitizensthrough shared optimism.

StrategyCoca-Colapremieredthe‘It’sBeautiful’adduringthe2014 Super Bowl, with a 60-second spot featuring Americans across the country – varied in ethnicity, race, religion, gender and sexual orientation – singing the traditional patriotic song ‘America the Beautiful’insevendifferentlanguages.Theadcelebratedthe diversity and beauty of America and showed Coke bringing friends and families together.

Theteamidentified13micro-audiencesegmentsmostlikelytosupportitsmessagebasedontheirdifferentethnicities, lifestyles and passions (for example, the 2.1 million Muslim Americans and the estimated 22 million lesbian, gay, bisexual and transgender Americans). It created a content and targeting calendarthatwouldreachdifferentaudiencesbased on the conversations that were anticipated. This enabled each of these communities with thegreatestpotentialaffinitywith‘It’sBeautiful’to engage in conversations as they unfolded, and to support the Coca-Cola message.


AfterairingduringtheSuperBowl,‘It’sBeautiful’ wassharedacrossCoca-Cola’ssocialcommunities with the hashtag #AmericaIsBeautiful. The agency then monitored the keywords and themes of the audience conversation.Inrealtime,ittraffickedmultiplesearchandsocialadsfocusedspecificallytoeachmicro-segment,helpingtodirectconversation and remind all those making negative comments that every singer featured in the creatives was an American.


It’s Beautiful

Impact‘It’sBeautiful’turnedouttobethecompany’smostsuccessfulcampaign in years: young people aged 19 to 24 bought Coca-Cola products20%moreoftenthantheydidthemonthbeforeandthebusinessreporteda1%salesliftduringthemonthofFebruary2014.Following the success of the campaign, Coca-Cola continued to usethemulti-languageexecutionof‘It’sBeautiful’initsnextbigmarketing tentpole event – the 2014 Sochi Olympics.

Agency Credits

Young people aged 19 to 24 bought Coca-Cola products 20%

more often


Micro-influenceandnetwork marketing (relationships)

Influenceandinfluencersarecontentioussubjects. The competing theories based on the premise that ideas, attitudes and behaviours spread from person to person rather than as a result of autonomous individual decisions have been hotly debated.

Influencermarketingisunderpinnedby the assumptions that some people (influencers)aremoreinfluentialthanothers,thatthisinfluencecanbeidentifiedandmeasured,andthatmarketingtoinfluencerswillbemoreeffectivethanmarketingtotargetaudiences or the whole market.

Influencermarketinghasgainedpopularity since the publishing of ‘The Tipping Point’26 in 2000 and ‘The Influentials’27 in 2003 which support the idea that new trends start with small groups, and that the individuals in these small groups are key to whether or not thetrendwilltakeoff.

However, opposing schools of thought arguethattheimpactofinfluenceisoftensignificantlyoverestimated.Multiple studies by Duncan Watts28 have shownthatatrend’ssuccessdependsnot on the person who starts it, but on how susceptible the society is overall to the trend. Watts argues that marketers should aim to reach a lot of people through mass media, and then do what they can to enable consumers to pass the message along.

Afurthercounterpropositiontoinfluenceis homophily, the tendency of like-minded individuals to adopt similar behaviour, as in ‘Birds of a feather flocktogether’.StudiesbySinanAral29 have demonstrated that homophily can explain much of the impact attributedtoinfluence.Inthesecases,campaign evaluation is failing to take into account the counter factual – what would have happened anyway. (See #IPASocialWorks Guide, ‘Measuring Not Counting’, for further discussion on the measurementofinfluence.)

5.2


What is less debatable, however, is that some brands have adoptedmodelsofinfluenceandadvocacyaspartoftheirpersonalisation strategies. Such strategies aim to communicate abrand’srelevancytonicheaudiencesornetworksbyworkingthroughanindividual–orinfluencer–whomconsumersbelieve to have a particular authenticity or appeal, or with whom they feel they have a personal connection.

Influencer’s typically fall into four categories

1. Professionals (occupational influencers such as bloggers, journalists, experts, analysts)

2. Celebrities (well-known individuals who have built large followings, online and/or offline)

3. Ambassadors and members of influencer communities (individuals who have been signed up by an organisation to act as the agents of influence marketing)

4. Customers and advocates (individuals who use/buy a product or service and who influence others, either by making recommendations or being seen to use/buy things)

The distinction between these is increasingly blurred with the rise of influencerswhohavegainedpopularitythrough social media.

The associated strategies can range from broadcast to highly targeted in

focus, often falling at either end of the spectrum.Thisreflectsthetensionbetween relevance and reach which is often called out and debated in relation to the rise of digital platforms and personalisation (see section 9.1).


Delivering reach: Celebrityinfluence

Celebrity partnerships form the majority ofcurrentinfluencerstrategies.Thereisalonghistoryofpayinghighprofileindividuals to endorse products, with the likes of tennis star Roger Federer making $71 million a year from sponsorships and endorsements and his rival, Rafael Nadal, being paid $525,000 to wear a watch at the French Open. These partnerships are akin to broadcast media sponsorships.

YouTube creators and social media celebrities provide brands with a similar opportunity to achieve mass reach amongst harder-to-reach younger audiences.Theseinfluencersaretypically given the creative freedom to create and deliver content which is highly relevant and authentic for their audiences.

Theeffectivenessofcampaignsinthisgrowingareacanbedifficulttomeasure.However, cases such as Mattessons Fridge Raiders, which partnered with The Syndicate Project, a gaming celebrity, show these strategies can deliver a measurable ROI when well executed (see case study, p84).

reported annual earnings from brand endorsements for YouTube creator Zoella30

£300,000


Delivering relevance: Micro-influenceandnetworkeffects

Modelsthatuse‘everyday’ormicro-influencerssuchasinfluentialcustomers,advocates and the close connections of prospectiveusers/buyersarebecomingmore popular.

Therearedifferentdefinitionsofmicro-influence.Asurveyoftwomillionsocialmediainfluencersbyinfluencermarketing platform Markerly found that forunpaidposts,Instagraminfluencerswith fewer than 1,000 followers had a‘like’rateofabout8%,thosewith1,000to10,000followershada‘like’rateof4%andInstagraminfluencerswith 10,000 to 100,000 followers had a2.4%‘like’rate31. In summary, the greater the follower reach, the lower theengagementrate.Markerlyidentifiedthe‘sweetspot’formaximumimpact ofaninfluencerwithbetween10,000and 100,000 followers.

Alternatively, social ad platform Gnack argues that followers who are more likely to be friends and family of the account owner will treat their posts as more trustworthy and engaging. The most effectivemicro-influencersarethereforelikely to have fewer followers32.

Followers who are more likely to be friends and family of the account owner will treat their posts as more trustworthy and engaging. The most effective micro-influencers are therefore likely to have fewer followers.


This personal connection to their followers is believed to make micro-influencersmoreeffectiveasbrandpartners.Ifmessages arecreatedorcustomisedbythemicro-influencer,theyarelikely to sound more authentic. An example of this approach in practice isWilkinsonSword’s‘FacebookCouples’campaign.

Example

Wilkinson Sword

Wilkinson Sword supported the Hydro 5 launch with a Facebook co-creation campaign, ‘Facebook Couples’.Applyingtheinsightthatmen’sshavinghabitsandhairstylesareheavilyinfluencedbytheirgirlfriends and partners, Wilkinson Sword targeted women with a campaign which enabled them to createpicturesoftheirpartnerwithdifferentfacialhair and encouraged them to share these images with their partner.

See case study, p86

This type of activation aims to deliver targeted and personalised content rather than the reach that can be achieved by celebrity partnerships. Alone these executions cannot deliver thescalerequiredtoachievesignificantbusinesseffects.However,campaigns

suchas‘FacebookCouples’showthatamplifyinginfluencercontentthroughpaid media to deliver reach can also amplify business results.

Kerry Foods worked with YouTube gaming celebrity The Syndicate Project to reverse the sales decline of meat snack Fridge Raiders by positioning it as the ideal after-school snack for teenage gamers.

ObjectivesIn2012,FridgeRaiders’saleswereindecline.Anewmarketing strategy was developed to grow sales by increasing penetration and purchase frequency. Communications was required to position Fridge Raiders as a better after-school snack choice than crisps, using separate approaches for two target audiences: mums and teenagers.

StrategyA humorous TV campaign aimed at mothers of teenagers aired in the summer of 2012 supported bysignificantpromotionalactivity.Asocialmediacampaign followed in early 2013, working with YouTube gaming celebrity The Syndicate Project (aka Tom Cassell) to target teenage gamers.The Syndicate Project challenged his community of over 6m YouTube subscribers, 1m Twitter followers and 645,000 Facebook fans to invent a hands-free snacking and gaming device that would allow gamers to eat the Fridge Raiders product without disrupting their gaming. Some 15,000 sent in suggestions via Facebook, YouTube and Twitter and several ideas were turned into prototypes. To maintain fan engagement levels during the process, daily Facebook updates were posted and weekly video updates broadcast on YouTube.


Case study | Mattessons Fridge Raiders

MMM 3000


Case study | Mattessons Fridge Raiders

MMM 3000

ROI of

£2.44

ImpactFridge Raiders Facebook fans went from zero to 127,000 and Facebook media achievedaCTRof0.6%,versusthe industryaverageof0.04%.Videocontentwas viewed over 3 million times, with 291,000 YouTube likes. The campaign generated 126 million impressions with a reach of 31 million.

Market mix models isolated the sales impact of social media from other factors, including promotions, seasonality, competitor activity, TV campaigns and distribution. After allowing for all other sales drivers, base sales that can be attributed to the socialmediacampaignroseby20%duringtheeight- week campaign period. The ROI was £2.44 for every £1 invested,makingthecampaignapproximately40%moreefficientthanthebrand’snextbest-performingmedia channel, TV. Brand metrics also improved, though social media’scontributiontothisimpactcannotbeisolated from other factors.

Agency Credits


In a market where shaving was out of fashion and beards were the norm, Wilkinson Sword used a micro-influence campaign to encourage trials of its new razor.

ObjectivesChanging fashions have led to a decline in the UK shaving market andagrowingindifferenceamongstmentowardschangingtheirshaving routines. In this shrinking, but highly competitive category, WilkinsonSwordneededtoreachandpersuade18to30-year-oldmen to try its Hydro 5 Groomer razor.

StrategyToachievethereachandscalerequiredtodrivesignificantsalesimpact, the team used Facebook as a key campaign launch platform before investing in activations across other media. Social media listening analysis showed that, unlike women who dressed to impress friends, men dressed to impress their partners (or prospective partners). The team applied this insight to develop a Facebook app which enabled partners of their target audience to visualise and share a new look for their bearded partners, encouraging the men to try it out. Participation in a trial was encouragedbytheofferofafree razor and style guide and a chance to win a £2,000 style makeover for the triallist and his partner.

Case study | Wilkinson Sword

Facebook Couples


To ensure the product was seeded with the right people, the male applicantswereaskedtofilloutasurveytodeterminetheirlevel ofsocialinfluenceintheirimmediatesocialcircles.Thebrandthensenttheproducttothe1,000mostinfluentialapplicantstotrialit.These triallists shared their experiences and new looks with friends, familyandcolleaguesofflineandthroughsocial.Paidmediawasused to amplify the best social stories to drive incremental reach, generating nearly 20 million impressions.

ImpactSurveys of the 1,000 triallists showed that by the end of the campaign90%ofpeoplewhohadpreviouslyusedGillettesaidthey planned to switch to Wilkinson Sword. The triallists and their partners reported having told an average of 6.3 people about their involvement in the campaign, reaching 12,600 individuals directly throughofflinewordofmouth.

The total sales impact and ROI of the Facebook element of the campaign was not measured in isolation. However, following the integrated product launch campaign, theHydromalesystemsvalueshareexceeded9%inthe UKforthefirsttimeever,reachingarecordhighof9.2%.

Agency Credits 90%of people who had previously used Gillette said they planned to switch to Wilkinson Sword

Case study | Wilkinson Sword

Facebook Couples


Moment marketing (behaviour and intent)

Context vs. Demographics

Contextual data is a complement to, rather than a replacement for, existing targeting strategies based on demographics and other attributes. It enables marketers to reach individuals based on needs, intent and interests atspecificmomentsandinrealtime,aiming to deliver the right message at the right time and contribute to the desired outcome. It can also broaden the reach of a campaign to individuals that would not otherwise have been targeted throughdemographicprofilingalone.

Google research33 into the incremental reach and impact of targeting mobile intent over demographics shows that brandsfocusingonlyontheprofileof their target consumers could be missingouton70%ofpotentialmobileshoppers.Abrand’stargetaudienceis not always representative of who

purchases its products. For example, only31%ofmobilesearchersforvideogamesaremenaged18-34.Similarly,40%ofallbabyproductspurchasers live in households without children. Advertising baby products onlytofamilieswouldmissasignificantproportion of this market.

Contextual relevance can be based on either external or internal factors. External factors include targeting by time of day, location, weather and othervariablesrelatingtotheuser’senvironment. This contextual data is increasingly commonly used across media. Examples include sending personalisedlocation-basedoffersto customer mobiles as they enter a geo-fenced area through to tailoring outdoor mass media campaigns to the weather. As an example of the latter, in thesummerof2015,Pimm’slauncheda digital out of home campaign which activated once the temperature reached 21°C, a time when consumers would bemoreresponsivetothedrinkbrand’sadvertising.

5.3


Internal factors are more personal and influencehowwereceiveandreacttomessaging. For example, internal data canreflectourmoodsandhowwefeelat a given moment. Social media is a key data source and channel here since it is able to provide rich information across boththeuser’sexternalenvironment,location and actions, as well as his or her thoughts and moods, and provide this information in real time. A Nielsen study34 of UK Twitter users reported that 55%usetheplatformtotalkaboutwhattheyaredoing‘rightnow’.

By mining social media data, brands are able to identify those moments that matter to their consumers and to their purchase journey and create relevant and timely interactions. This presents an opportunity to be more emotional, more relevant, and to cut through the cluttered media environment.

By mining social media data, brands are able to identify those moments that matter to their consumers and to their purchase journey and create relevant and timely interactions.

of UK Twitter users talk about what they are doing‘rightnow’

55%


1. Events and shared cultural moments, or macro-moments. Reaching consumers in a shared moment or event, for example a sporting event, national holiday, festival, or political event. These moments offertheopportunityforbrandstotapinto massive reach and highly emotive contexts, and attract premium prices. Social media has successfully been used in these contexts to achieve reach and resonance without expensive TV placements or sponsorships. Activations use contextual information to create relevance at scale rather than todeliverpersonalisationtorefinedgroups or on a one-to-one basis.

2. Everyday moments, or micro-moments. Reaching consumers in real time toanticipateand/orfulfilaneed. This has long been possible through search campaigns. Consumers using searchenginestofindaproducttheyintend to purchase are met with paid andorganicsearchresultsfordifferentbrands’marketinginformationonproductspecifications,priceandoptionsto buy. The rise of mobile connectivity and digital and social media increases the potential value of these strategies and our ability to deliver increasingly personalised experiences.

Micro and macro-moments

Two types of contextual targeting, or moment marketing35, frequently discussed are:


Forrester, the research company, has describedtheincreasingsignificancebeing assigned to micro-moments in a series of research papers36. Forrester maintains that the ability to engage consumers with short but relevant interactions at conducive moments will help brands cut through media clutter and improve relationships with customers.

Social media is a key enabler of micro-moment interactions. It is a rich source of data about both external factors (location, weather etc.) and internal ones such as conversations, which can be used to create moment targeting. For example, many brands consider targeting by social conversation attractive since social listening data can span factors such as mood, attitude and explicitintentwhichcanbedifficulttogather elsewhere. This social data can beminedforinsightintotheconsumer’spath to purchase and engagement with a brand or product category. The resulting insights can help a brand uncover those moments where information or engagement from a brand will be mostlikelytoinfluenceconsumers’behaviour or attitude.

The charity Melanoma Patients Australia used text, image and location data from Twitter and Instagram posts to identify and engage with social media users on the topic of skin damage in the moment they were putting themselves at greatest risk through sunbathing on a beach (see case study, p93). Similarly, Dove used text data to identify women who were posting negative comments on Twitter relating to beauty and body image and used this to respond in real time with messages aimed at inspiring a change in attitude and behaviour (see case study, p95).

...many brands consider targeting by social conversation attractive since social listening data can span factors such as mood, attitude and explicit intent which can be difficult to gather elsewhere.


As a communication channel to engage with consumers in such moments, social media provides not only the capability for real-time, cross-device targeting but alsooffersaninteractiveandpersonalplatform that delivers at scale. Indeed, brands frequently build their social media communities with the stated purpose of developing a more direct and multi-dimensional relationship with the public.

With the number of connected devices only likely to rise in future, brandswillincreasinglyfindtheconceptof micro-moments attractive as a waytocutthroughnoiseand‘speak’directly and authentically to customers. However, the variety of devices, screen sizes and functionality available to digital consumers will make delivering micro-moments complex.

With the number of connected devices only likely to rise in future, brands will increasingly find the concept of micro-moments attractive as a way to cut through noise and ‘speak’ directly and authentically to customers.

Case study | Melanoma Patients Australia

Melanoma Likes Me

Melanoma Patients Australia used one-to-one targeting on Instagram and Twitter to generate awareness and provide information on the dangers of skin cancer.

ObjectivesMore than 1,500 Australians die from melanoma each year. It is the most lethal cancer for 15-30 year-olds. The ‘Melanoma LikesMe’campaignwascreatedtoraiseawarenessofthedangersassociated with sunbathing amongst this audience and provide information on how to check for melanomas.

StrategyThe campaign used social media to reach this younger and harder to reach audience and in real time – at the time when they were most in danger of causing their skin damage. An online persona was created for melanoma on Instagram and Twitter. The team used social listening tools to identify popular hashtags the target audience used on social media when sunbathing such as #sunkissed, #tanned and #beachside. By tracking Instagram and Twitter posts with these hashtags or relevant geo-located images, the team was able to respond in real timewith‘likes’,‘follows’andshockingcommentsfrom persona @_melanoma to remind people of the dangers of sunbathing. The team provided links to the Skin Check mobile site to point the sunbathers to information on how to check for melanomas.


The team used social listening to remind people of the dangers of sunbathing in real-time

Case study | Melanoma Patients Australia

Melanoma Likes Me


ImpactThousands of individually tailored messages were sent. The campaign saw high engagement from the target audience as well as local identities who retweeted and shared the messages and comments, increasing reach and awareness. The click-through rate to the Skin Check mobile site was the highest level of daily web trafficMelanomaPatientsAustraliahadeverseen.Basedonthesuccess of this campaign, Melanoma Prevention Australia plans to roll out the campaign across Australia.

Agency Credits

Case study | Dove

#SpeakBeautiful


Dove and Twitter launched #SpeakBeautiful to encourage women to speak positively about themselves and others on social media.

ObjectivesIn2014womenpostedfivemillionnegativetweetsaboutbeautyandbodyimage.Dove’s#SpeakBeautifullaunchedin2015toencouragewomentounderstandtheeffectonlinewordshaveonconfidenceand self-esteem and to inspire them to think and speak positively about themselves and others every day.

StrategyThereweretwophasestothecampaign.Thefirstphaselaunchedon Oscar night, a time when audiences were likely to comment on body image as stars walked the red carpet. A 30-second ad aired on the night and showcased real-life negative tweets. Partnering with Twitter, the Dove team used a tool to identify relevant negative tweets and responded with messages to encourage women topostonlinewithmoreconfidence,positivityand kindness. Real-time data visualisations showed positive and negative beauty words being used on the night, and paid media was used to amplify the reach of positive messages. The activation extended beyond the Oscars to other events and across everyday moments, with the community managers continuing to engage one-to-one with women tweeting about body image.

Case study | Dove

#SpeakBeautiful


In 2016 Dove announced a new phase, using a tool which could createapersonalisedanalysisofawoman’stweetsonbodyimageand compare this analysis to that of other women. Users simply retweetedDove’sinvitationtoparticipateandDoverespondedwithalink to a custom microsite displaying the personalised analysis of the woman’stweets.Thisanalysisshowedwhetherthewomanheldapositive or negative attitude towards body image, compared to other tweetedtopics,andidentifiedthemostcommonemotionexpressedin related tweets. Users were encouraged to share their analysis and foster positive conversation online, and could track changes in their own behaviour over time.

ImpactOverthecourseof2015over168,000tweetsweresentusing#SpeakBeautiful,driving800millionimpressions.Communitymanagers were able to connect with women on a personal level, responding one-to-one to over 3,000 negative tweets. The campaign formed part of a broader shift in conversation. The number of negativetweetsaboutbeautyorbodyimagedroppedby36.8%year-on-year, from 5.3 million in 2014 to 3.4 million in 2015.

36.8%year-on-year drop in negative tweets about beauty or body image

6. Approaches (II): personalised experiences | 97

6. Approaches (II): personalised experiences

6.1 Social CRM

6.2 Integrated customer experience


Interests, attitudes and emotions

1 2 3Relationships and networks

Behaviour and intent

These strategies are often executed in channel silos. There is no integration with the centrally held customer view or with other contact strategies. As the media landscape fragments and the volume and variety of touchpoints increases, we risk customers having a fragmented and inconsistent brand experience.

Brands are at different levels of maturity in tackling this:Social CRM is a concept attracting growing interest. Brands are seeking to understand how to leverage social media across the customer lifecycle as part of their CRM strategy. This involves integrating the use of social media data and targeting alongside traditional direct marketing channels such as email to create a consistent andmoreeffectiveexperience.

Delivering fully integrated customer experiences using social media data is still nascent. There is evidence of social media data now being integrated into centrallyheldcustomerprofilestofuelmore relevant experiences cross-channel and cross-platform, but industry and organisational challenges mean that these cases tend to be pilot programmes in experimental phases.

We have explored how social media data is being used to deliver personalised marketing through social media data,specificallydatarelatingto:

As the media landscape fragments and the volume and variety of touchpoints increases, we risk customers having a fragmented and inconsistent brand experience.


Social CRM

Many organisations are looking to integrate social media targeting with traditional CRM channels such as email, using social as a complementary form of direct marketing. Used in this way, social provides opportunities to

• Reachcustomersacrossmultipleor preferred channels (for example, targeting customers who did not open a campaign email)

• Reachthosewhohaveoptedout of other channel communications

6.1

Example

adidas

The sportswear brand used social to build a loyal community and CRM database. Its Team Messi initiative brought 23,000 Messi fans to its central database,60%ofwhomwerenewacquisitions. Based on sales conversion rates in subsequent direct marketing campaigns, these socially engaged fans proved to be of higher value than other customersinthebrand’sdatabase.


The more mature social platforms have developed advertising products, such asFacebook’sCustomAudiencesandTwitter’sTailoredAudiences,whichallowbrandstotargetspecificindividualsbased on their email or phone number.


Although the subject of integrating social and CRM has attracted much attention, relatively few brands have made progress in this area. Structures within both the brand and its agencies can be barriers to achievingsuchintegration,sinceownershipofdifferentchannel strategies and their execution can be split across multiple teams. In addition, without integrating a feedback loop for interactions with these direct marketing messages the ability for organisations to continuetobuilduponexistingcustomerprofilesislimited. Collaboration across the brand organisation and its partners will be needed to achieve social CRM integrationandfulfiltheoverallgoalofprovidingthecustomer with an improved and consistent experience.

Example

O2

O2usedFacebook’sCustomAudiencesproduct to reach customers at the right time and purchase journey stage (early upgraders, upgraders, out of contract etc.) with the right creative in order to drive repeat sign-ups. This targeted approach resulted in an average49%decreaseinthecostperorder across all three user segments, compared to untargeted approaches.



Integrated customer experience

The use of social media data to fuel cross-channel and cross-platform experiences is still relatively nascent. As data and personalisation strategies mature, the value of social datasets anddifferentapproachesto integratingtheseintoabrand’s overall view of the customer are coming under greater scrutiny.

6.2

The ability to deliver an integrated experience across touchpoints and devices rests on having the following in place:

• The capability to identify customers across touchpoints and devices

• The ability to augment the single customer view with data from across the business and different touchpoints, and to surface this data in a way that it can be used by the organisation

• A feedback loop which ensures that this single customer view is continually updated with data from across touchpoints to improve the effectiveness of the brand’s activities


This category comprises data which the social media user agrees can be openly viewed. On Twitter, this often includes username, bio and location, and other data points that can derived from public Twitter activity such as sentimentandinfluence.

To date, brands have had limited ability to match this data to their internal CRM databases at a customer level. Third-party matching services do exist. These services access and match large amounts of publicly available social information,suchascustomers’publiclystatedinterestsonFacebookprofiles,Twitter hashtags they interact with, and the organisations they work for from LinkedInprofiles.However,ascustomershave not explicitly given their consent to this data collection, success rates in matching social and CRM data can be low.Self-evidently,suchdatareflectsonly what customers want to reveal about themselves publicly on social. In addition, for some brands the risks

of undermining consumer trust from such practices are a concern. For this reason, there are few public case studies in this area.

This category comprises data where thesocialmediauserhassettheirprofileand activity so that only connections oradefinedgroupofuserscanseethem.Forexample,Facebookprofiledatasuchasauser’sbirthdayandhometown and interactions such as status updates are often set to private by users so that only friends can see them.

If brands ask users for access to this data – making it clear what users will get in return – some of these data points can be accessed at a customer level through authentication and social login.

1. Public profile and interaction data

2. Private profile and interaction data

Theserequirementscanbeparticularlydifficulttomeetinsocial media because customer-level data can be held by multiple owners in the social space, and matching this social data with the individual customerrecordsheldbythebrand’sorganisationcanbechallenging.

To recap, there are two levels of access to social data:


The social data available varies by platform (see section 4 for further detail). While these data points can help organisations gain greater insight into their customers, there is relatively little evidence that brands are making full use of the data associated with social authentication. The primary motivation forofferinguserstheopportunitytoregister for services using their social ID is currently to make sign-up as

frictionless as possible. In addition to the practical problems caused by the speed, volume and fragmentation of social data already alluded to, some brands view relying on social platforms for a critical part of their customer data strategy as too much of a risk. These complexities around data ownership have held back the spread of social media data integrations that could underpin greater use of personalisation.

Example

Channel 4

Channel 4 enables audience members to register for access to its on-demand catalogue either through email and password creation or by social login using Facebook, Twitter or Google credentials. In exchange for the social data provided (which for Facebook includes access totheindividual’spublicprofile,friendlist and email address), Channel 4 signpoststhebenefitsofregistration,including access to the back catalogue, personalised reminders for favourite programmes, and exclusive content.

“Through registration, our audience gains a deeper, more rewarding viewing experience…and in turn, via the social login information, we have a greater insight into our audience which in turn informs our viewer engagement strategy.”

Steve Forde, Head of Viewer Relationship Management, Channel 437

Case study | adidas

Team Messi Movement


A three-year programme, including a social CRM drive, helped adidas recruit new and higher-spending customers and generate increased value from its sponsorship of football star Lionel Messi.

ObjectivesArgentinianfootballstarLionelMessihasbeenanadidasassetforhis entire professional career and yet in 2013 attribution between the brand and the player was low. In response, adidas decided to build brand equity by streamlining all of its Messi activations into a single, clearly positioned platform with the aim of:

Increasing adidas-Messi attribution amongst the target consumer by

up to 40% by the end of 2015

Converting 10% of fans who ‘followed’ or ‘liked’ Messi into a Team Messi movement and social community

Driving CRM acquisition and sales conversion from the

Team Messi social community

Increasing consumer engagement and supporting key product launches with

unique content and experiences


StrategyThe target audience comprised 14 to 19-year-old digital natives, who expected their favourite brands to be on social media, serving up entertaining and exclusive content. Team Messi provided a clear value exchange for consumers by creating a platform for overt celebration of the star player, with the promise that fans would get unprecedented access to Messi in return for their time, attention and following.

The execution included:

Case study | adidas

Team Messi Movement

Aseriesofhigh-profilecampaignsforproduct launches and key calendar Messi moments, such as the ‘Speed ofLight’bootlaunchandLeo’sfourthwinoftheBallond’Orprize,toassistin generating buzz, demand and sell-through,drivingtraffictothebrand’ssocial ecosystem, ecommerce and retail stores.

An always-on social media programme across Twitter, Facebook, Instagram and Vine with the community manager workingcloselywithLeo’ssportmarketing manager at adidas to provide inside scoops, exclusive behind the scenes images, and real-time approvals.

1 2


Impact• 483,000Twitterfollowers,1.8millionFacebook

‘likes’and220,000mobileappdownloadswithin thefirst12months

• EngagementlevelsonTeamMessisocial platforms higher than main adidas football platforms (disproportionate to community size)

• 23,000TeamMessifansintheadidasCRM databaseattheendofyearone,60%ofwhom were new acquisitions

• TeamMessifansinthedatabasewereworth an average of €10 more than other consumers in the database

• AveragesalesconversionlevelsfortheTeamMessi fans were higher than the football category norm

• GlobalrevenueforecastforMessiiconproducts surpassedinyearoneby17%,leadingtothebusiness unitbuildingacaseforapportioning10%of2015 revenue forecast into the Team Messi platform

• Andcrucially,adidaswason-coursebytheend of year one to surpass its three-year attribution objectives,withsignificantupliftinSpain,Korea, France, Italy, Brazil, Russia, Germany, UK, Japan, China and USA

Agency Credits

Case study | adidas

Team Messi Movement

Global revenue forecast for Messi icon products surpassed by

17%

Case study | O2

Personalised Marketing at scale


How O2 uses personalisation to drive brand love, loyalty and sales.

With over 24 million customers, O2 runs 2G, 3G and 4G networks acrosstheUK,aswellasoperatingO2Wifiandowninghalf of Tesco Mobile, running 450 retail stores and sponsoring the England rugby union team.

O2 creates a clear value exchange for customers who receive more tailoredoffersandbettervalueformoney,inreturnfortheinsightsthe brand is able to draw from their customer data and behaviour. The challenge for the business has been how to leverage mass personalisation to meet the customer demand to be treated as an individual as opposed to a segment.

Case study | O2



O2 uses personalisation to deliver on three objectives:

As part of its broader #WearTheRose campaign to support the England Rugby team in the run- up to the 2015 Rugby World Cup, O2 transformed the roof of The O2 into a giant canvas for projecting fan tweets.

To be in with a chance of having their tweet projected onto the roof, users needed to tweet messagesofsupportusingO2’s#WearTheRose hashtag. The winners received an exclusive image andGIFoftheirprojectionfromO2’sdedicatedsportshandle, @O2sports. The activation ran for seven weeks with thousands oftweetsprojectedontotheroof.Althoughtheeffectivenessof the‘RoseontheRoof’activationwasnotisolated,O2wasable to measure the impact of the overall #WearTheRose campaign usingmarketmixmodellingtodemonstrateapositiveprofitROI.

1 Brand love and consideration


O2’sloyaltyprogramme,Priority,bringscustomersawiderangeofexclusiveoffersandticketsspanningfoodanddrink,travel,shopping, entertainment, sports, and health and beauty. To surface themostrelevantoffersanddeliverthegreatestvaluetoindividualcustomers, O2 personalises message delivery based on both explicit preferences (for example, those who have expressed an interestinsportsoffersintheirprofile)andimplicitbehaviour (for example, past redemptions).

2 Loyalty and retention

Explicit preferencesImplicit behaviour

Precision targeted offers and experiences

Deep-linked experience

Case study | O2


Case study | O2



The team uses its CRM database to power Facebook Custom Audiences, reaching the right customers at the right time with the rightcreative.Customersareshowndifferentcreativesaccordingtotheir purchase journey stage: early upgraders, upgraders, and out of contract.Thetailoredapproachhasledtoa49%decreaseincostper order on average across all three segments.

Personalised creative for each lifecycle stage:

3 Re-sign and selling

Early upgraders Upgraders Out of contract

49%decrease in cost per order


Launchedin2014,theBBC’spersonalisation programme, myBBC, is enabling the corporation to better understand its audience members in order to create relevant, compelling and innovative content and services. Social is used both as a source of insight and a communication channel in its own right.

The BBC is reinventing public service broadcasting through data.

The BBC is home to a huge range of content spanning news, weather, sport, television and radio. It originates and distributes content which is consumedby96%oftheUKpopulation each week. For audience members this means a vast range of highly relevant but untapped content and serviceswhichcanbedifficultto discover. For the BBC this means a wealth of opportunities to create valuable experiences for audience members across products and platforms.

Case study | BBC

myBBC

Case study | BBC

myBBC


• LaunchingaBBCIDwhichprovidesa single sign in for all BBC services to enable personalised experiences cross-product and platform. The BBC ID has enabled the introduction of BBC iPlayer features, such as the ability to tag programmes as favourites and track the release of new episodes, and to pause content on one device and resume it on another.

• Launchingarangeofpersonalisedfeatures for non-signed-in audience members, including:

– A myNews feature on the BBC News app enabling users to receive news tailored to their interests and local area

– Anautomaticlocationfinderin theBBCWeatherapptofindauser’slocationandprovidelocation-based weather updates

– The option to receive personalised BBC Sports updates about favoured teams and sports

Success stories for the myBBC programme to date include:


The myBBC team has used a test and learn approach in small pilot projectstogaugethemeritsofdifferentpersonalisationapproachesandshare learnings across the organisation before scaling up and adding complexity. This includes pilots to understand the role and value of social media and social media data within the myBBC strategy.

Pilot 1

BBC Masterbrand ProfilesObjective The BBC masterbrand social accounts were created to bring together relevant content from across the organisation foraudiencemembersforthefirsttime.ThesenewFacebook,Twitter and Instagram accounts were designed with personalisation in mind due to the sheer breadth of BBC content available for them to promote.

StrategySeveraltestaudiencesegmentswereidentifiedusingsocial data attributes such as age, location and interests. Respective content strategies and calendars were developed to deliver consistent messaging featuring BBC content relevant to each audience segment. For instance, wildlife enthusiasts might be targeted with BBC Earth content as well as BBC iWonder features

and previews of BBC One wildlife programmes. In addition to targeted content, the team delivered a broadcast strategy across

each account as a benchmark.

ImpactTargetedorganicFacebookpostsachieved20%-30%higher

engagement than non-targeted posts and achieved equal reachtopaidpostsduetotheviraleffectsofaudienceengagement.Theselearningswereusedtorefinethe

strategy for the masterbrand accounts and inform the BBC’sbroadersocialmediaactivities.

Case study | BBC

myBBC


BBC Three Social CRM PilotObjectivesWhen BBC Three announced it would be available online only from Autumn 2015 onwards, the team set up a pilot social CRM programme for engaging with BBC Three audiences on Twitter. The objectives were to build brand advocacy and help retain a loyal online viewership while testing the value of social CRM capabilities for other BBC brands and products.

StrategyTraditionally,BBCThree’ssocialmediateamhadrespondedto

its audience on Twitter in an ad hoc manner due to time and resource constraints. Social segmentation analysis showed

that BBC Three had a core base of enthusiasts, but that over90%oftheteam’sresponsestodatewerewith

audience members who had relatively low engagement with the brand. This approach meant that both highly

engaged audiences, and those with a higher social following and reach, were not being rewarded,

thereby limiting the potential for brand visibility through audience advocacy. A custom app was built to help the

social media team identify and prioritise Twitter users who were highly engaged with the brand, as well as to segment these users by the size of their following.

ImpactUsing the new segmentation and app to prioritise responses, the team achieved a two-fold increase in engagement levels and a three-fold increase in impressions for its replies. The functionality has been fed into the feature roadmap for the marketing platform which is being rolled out across the wider organisation.

Pilot 2

2xincrease in engagement level

Case study | BBC

myBBC

Case study | BBC

myBBC


The next step: Integrating social media to deliver a unified audience experience

Small pilots have been critical to understanding whether and howsocialmediacancontributetotheorganisation’sbroaderpersonalisation agenda, and how these capabilities can be scaled up. The evidence from the pilots to date appears largely positive, deliveringsocialeffectssuchasreachandengagementandengageduser journeys from social media referrals to the BBC website.

InseekingtodeliveraunifiedaudienceexperienceoftheBBC,theongoingprogrammeislookingatthepotentialbenefitsandchallenges of integrating social media and the BBC ID. The central focusanddrivingforcefortheteaminitsapproachtodefiningthefuture roadmap and addressing these challenges remains the end goal of delivering the greatest value to audience members, while ensuring continued audience trust and a clear understanding of data use and permissions.

7. Approaches (III): customisation, co-creation and community | 116

7. Approaches (III): customisation, co-creation and community

7.1 Customisation and co-creation

7.2 User-generated content

7.3 Data-driven creatives

7.4 Community and social

recommendation


FutureFoundation’sstudy,‘The Future of Marketing and Agencies: The Next 10 Years for Consumer Engagement’,(see section 1.1) describes emerging models wherebrandsprovideself-sufficientconsumers with the tools and platforms to achieve their full potential. For example, the brand that takes the role of a valued friend or coach, providing the stage for the consumer to be the star (Me and the Brand Next Door Model) and the brand which acts as a toolbox, providing more functional engagement where the consumer is in charge of their experience (iControl Model).

The trend towards consumer enablement is central to the topic of relevance and, in particular, to the role that social media will play in its creation. Brands are using customisation, co-creation and peer-to-peer recommendations to leverage the effectsofearnedmediaandadvocacyand move relationships from one-way messaging to two-way collaboration. Whereas personalisation typically describes the use of data to tailor experiences, brands are increasingly enabling consumers to tailor and share brand content and recommendations themselves. This raises the question: to what extent will the future of relevance be consumer-enabled rather than brand-led?

Whereas personalisation typically describes the use of data to tailor experiences, brands are increasingly enabling consumers to tailor and share brand content and recommendations themselves.

To what extent will the future of relevance be consumer-enabled rather than brand-led?


Customisation and co-creation

1. User-Generated ContentEnabling individuals to tailor and share brand content through co-creation.

Successattractsattentioninthisfield.One of the best known success stories isCoca-Cola’s‘ShareaCoke’campaignwhich has run in several international markets (see case study, p125). At the heart of the campaign, which was designedtotapintoteenagers’socialbehaviours, was the decision to replace thebrand’slogotemporarilyonitsbottles with 250 popular teenage names.

7.1

2. Data-Driven CreativesEnabling individuals to generate tailored content based on their social media data,forexample,theindividual’sprofiledata, networks or past interactions.

Social media strategies are increasingly incorporating elements of customisation into creative implementations in order to drive engagement and leverage sharing behaviours and earned media.

These include the use of two approaches:

Teens were then encouraged to share aCokewithafriend’snameonit,andcreate and share virtual bottles with customised labels. Other FMCG brands have followed with similar campaigns, includingMarmite’s‘PersonaliseYourMarmite’,Nutella‘YourNutella,YourWay’andOreo‘Colorfilled’,eachdrivingcustomerstothebrands’respectiveecommerce proposition.


Customisation vs. Personalisation

The‘ShareaCoke’campaignhassparked debate over the distinction betweentheterms‘customisation’and‘personalisation’whichareoftenusedinterchangeably.Commondefinitionsfor each term suggest a distinction in the one-to-one brand-led relationship implied by personalisation:

Customisation involves modifying something to suit a particular individual or task. The product, service or experiencewasnotmadeforaspecificindividual and does not imply a one-to-one relationship between the brand and the user. In customisation, something which has been produced for the mass marketorforadifferentpurposeisadapted to suit the needs of individuals.

Personalisation involves designing or producing something for a specificindividual’srequirements,or incorporating attributes that areidentifiableasbelongingtoaspecificperson,suchastheiridentity,past behaviour or preferences. Personalisation indicates a relationship betweenthebrandandaspecificindividual, a one-to-one connection.

But this connection does not have to bereflectedinacompletelyuniqueexperience for the individual from beginning to end. Instead, it can be the case that one or more unique attributes of the individual have been embodied into the product or service.

Both approaches can deliver greater relevance for the consumer than untargeted experiences but can be usedtodeliverondifferentobjectives.Customisation strategies will typically see a brand act as an enabler providing the platform for individuals to customise and share content at scale. These strategies remain closer to the realms of mass marketing compared to personalisation, and the often higher engagement rates and the increased propensity for consumers to share tailored content can make these strategieseffectiveindrivingKPIssuchas brand awareness and advocacy.

Customisation strategies will typically see a brand act as an enabler providing the platform for individuals to customise and share content at scale.


User-generated content

Enabling users to tailor and share content has gained momentum as a brand tactic. Witness the popularity of GIFs and other related social media platform features. These can help drive interactions with brands and encourage sharing to achieve viral reach and peer-to-peer endorsement.

Co-creation is an increasingly attractive option for brands within this area. Co-creation is typically used as a feature in a broader campaign to drive engagement andtoattainviraleffectsforthebrand,sincetheseeffectscanhelp deliver reach at a lower cost than other media. It has become common for brands to concentrate their co-creation initiatives around popular live events – such as major sportsfixtures–inabidtoengageconsumers, without incurring the heavy costs that can be associated with sponsoring such sports properties.

7.2

Example

Gatorade

Gatorade’ssponsoredlensfortheSuper Bowl enabled Snapchat users to dunk a virtual cooler of Gatorade over theirownvideoselfiesandsharethesecustomised videos. The sponsored lens generated 160 million impressions.


Co-creation aims to attain viral effects for the brand… these effects can help deliver reach at a lower cost than other media


Campaigns can also be designed to betailoredby,andsharedwith,specificindividuals.Themicro-influencerstrategies and case studies discussed in section 5.2 exemplify this approach.

Creative execution needs to be compelling and tap into the underlying motivations for consumer sharing

Example

EA Sports

EA Sports developed a real-time GIF generator to support sales of itsNFL-basedvideogame,‘Madden’.The Giferator dynamically generated GIFs in real time, layering headlines and statsfromNFLgamesastheyhappenedwith video game artwork featuring the relevant players. Fans were able to create their own GIFs to share online with other fans and rivals.


(for example, self-expression, peer validation, humour, utility etc.) to ensure success. The creative in question must also be easy to tailor and share, and it needs to deliver a consistent brand message despite the tailoring.

Creative execution needs to be compelling and tap into the underlying motivations for consumer sharing.


Data-driven creatives

The use of data to drive the creative itself has been gaining traction recently following some widely celebrated campaigns. One of the best known hasbeentheBritishAirways’‘MagicofFlying’digitalbillboards.Thesebillboards showed a child standing up andpointingwheneveraBAplaneflewoverhead.Informationabouttheflightinquestion,suchasflightnumberandcityof origin, also appeared on the billboard screen. To power the campaign, BA used a special type of antenna to pull in transponder data from every plane within a 200km radius, with the ad triggered everytimeaBAplaneflewoverhead.

Similarly,JWT’s‘TheNextRembrandt’used big data and machine learning toenablea‘new’paintinginthestyleof Rembrandt to be created. These campaigns provide examples of fresh ways of combining data and creativity. However, the areas seeing greatest interest and fastest growth relate to personalisation. Social media data is of particular interest as it enables brands to provide a hyper- relevant and memorable message that the audience will more easily identify with and share with peers.

7.3

Social media data is of particular interest as it enables brands to provide a hyper- relevant and memorable message that the audience will more easily identify with and share with peers.


Thesecreativeconceptsusedabroadrangeofsocialprofiledata.It was data that was both likely to be available for a brand to access andrichenoughontheaverageuser’sprofiletoformthebasisofacompelling message.

Examples

Amnesty International

AmnestyInternational’s‘TrialbyTimeline’appscanneddatafromauser’sFacebookTimelinetodeliveravery personal and relatable message about the importance of human rights and the persistence of illiberal regimes round the world. Based on an individual’sprofiledata,connectionsandpast interactions, the app showed users what could have been the implications of their behaviour, such as swearing or drinking alcohol, in less liberal countries.


Ubisoft

Createdforthereleaseof‘WatchDogs’,ahackervideogame,Ubisoft’s‘DigitalShadow’apppulledinformationfromauser’sFacebookactivitytobuildacomprehensiveprofileoftheuserasifheorshewereanassassin’starget.This data included surfacing any public photos and information on who the user most interacted with. It showed when users were most active on Facebook, where they were most likely to be found, their estimated salary, and a list of possible passwords to use in hacking their accounts.



Community and social recommendation

Peer-to-peer communities and social recommendations are likely to be integral to the future customer experience and will become more important to personalisation strategies as we move further towards consumer-enabled engagement models.

Communities have already changed the nature of the relationship brands can hold with the consumer by providing an always-on, two-way conversation and collaboration. Many brands now see community as integral to the delivery oftheirproducts/services.Forexample,communitiessuchasLegoIdeasandGE First Build enable consumers to be directly involved in new product developmentandinnovation.Giffgaffisawell known example of customer service communities where customers provide the front-line customer support in return for kudos points.

Fashion retailer Anthropologie provides an example of fostering a customer community which can share and discuss more detailed product reviews and recommendations on its ecommerce site. In addition to standard rating and review features, community members append their age range, height, body

type (e.g. hourglass) and style (e.g. feminine – soft and romantic) to enable other members to draw from the most relevant recommendations for themselves. Members are also able to start conversation threads against reviews to thank members for their submissions, corroborate views or experiences, give their opinions, and ask for follow-up advice.

The move towards consumer-enablement will make this peer-to-peer element more important, but the key to success will lie in the two-way collaborative experiences. Brands have experimented with using social recommendations through social login to support personalisation with limited success. For example, Netflixexperimentedwithusingrecommendations based on what your friends are watching, only to remove the feature. As part of a one-way communication strategy social recommendations will have a limited application and in many cases have proven to come across as forced and invasive.

7.4


The ‘Share a Coke’ campaign used earned media to drive an increase in sales and consumption. First launched in Australia, the campaign has been rolled out to over 50 countries, including the US.

ObjectivesIn2013thecarbonatedsoftdrinksmarketwasdown3.3%andhalfof all US teens had not enjoyed a Coke in the past year. To increase its sales and nurture a more personal connection with US teenagers, Coca-Colaimportedthe‘ShareaCoke’campaigntotheUS.

StrategyThere is nothing more personal than your name. Thatiswherethe‘ShareaCoke’conceptwasborn: taketheCoca-Colabrandnameoff20-ouncebottles and replace it with 250 of the most common teen names. It was a simple idea designed to reconnect teens to Coca-Cola.

The campaign intentionally launched small, allowing teens to discover the programme on their own terms and feel ownership of it. Custom 20-ounce packaging with 250 popular teen names generated instant interest as word of mouth spread through the social sphere. The only paid media was search, used to react to organicinterestanddrivetraffictoshareacoke.com.Afteranorganiclaunch, the team built a base of teen-targeted TV and online video. It layered in mass digital takeovers as well as a digital outdoor plan centred on key teen hangouts. The team invented ways for teens to participate and share through the use of personalised creatives. This included creating interactive human-sized Coke bottles on bus shelters where teens could type in their names, see these names on thebottleandshareapictureofit.Theywerealsoofferedtheabilityto create and share virtual name bottles on social media.

50+The campaign has rolled out to

countries


Share a Coke US


ImpactSalesofparticipatingCoca-Colapackagesroseby11%intheUS.‘ShareaCoke’wasmoresuccessfulindrivingsalesintheUSthan inanypreviousmarket.Modellinganalysisidentifiedthatoverall, paidmediadrove10%ofincrementalsales. Social buzz and interactions were also key to driving sales, with earned mediadirectlycontributing5%toincremental sales, with Instagram proving a lead platform.

Thebrand’skeyperformanceindicatoris measured as ‘at least one Coke consumedinthelastfourweeks’andistypically a hard-to-move metric. ‘Share a Coke’helpedthisKPIincreasefivepointsamong teens in just eight weeks from 2nd Juneto28thJuly2014.Thattranslatedtoabout 1.25 million more teens having tried a Coke that summer.

Agency Credits


Share a Coke US


Gatorade’s Snapchat Super Bowl campaign enabled audiences to personalise and share videos of themselves being dunked in the product.

ObjectivesIn 2016 Gatorade partnered with Snapchat to connect with the drink brand’stargetaudienceofyoungcompetitiveathletesduringtheSuper Bowl, creating an ad campaign to play on the Super Bowl tradition of the Gatorade dunk.

StrategyGatorade created a sponsored lens, which enabled Snapchat users to dunk a virtual cooler ofGatoradeovertheirownvideoselfiesandsharethese personalised videos which featured only the Gatorade‘G’asbranding.Theteampromotedthelens with a video of Gatorade ambassador Serena Williams being virtually dunked.

ImpactThe isolated business impact of the campaign was not measured. However, the campaign delivered social reach with 160 million impressions.

impressionsm160

Case study | Gatorade

The Super Bowl Dunk

Case study | EA Sports

Madden Giferator


EA Sports developed a real-time GIF generator to engage with American football fans in real time.

ObjectivesThemakersofMadden,theNFL-based video game, had seen its popularity dip. The business was seeking to reverse this by tapping into the excitementandteamrivalryoftheNFLseason,andreachthe 157millionNFLfansinareal-timeandrelevantmanner.

StrategyThe team created the Giferator feature which dynamically generated GIFs, layering headlines andreal-timestatsfromNFLgameswith video game artwork featuring selected players. These GIFs were segmented by team loyalties and distributed across games-related fan sites. Audiences were also given the opportunity to create their own GIFs to share with other fans and rivals.

ImpactMore than 420,000 GIFs were created and shared online by fans and players with widespread media coverage. Sales have not been attributed to particular media activations ortheGiferator,but‘MaddenNFL15’wasthetop-selling title in the US for its release month, and the second bestselling in 2014.

Agency Credits

GIFs were created and shared online

420,000+


Amnesty International used Facebook Timeline data to deliver a personalised and relatable campaign generating awareness for its human rights work.

ObjectivesIn January 2013, New Zealand was named the country where people enjoyed the greatest freedom. Since the causes of freedom and human rights were not well-understood by New Zealanders or seenassufficientlyrelevanttothem,AmnestyInternationalwantedto increase awareness and support for its work by giving New Zealandersafirst-handexperienceoflifeincountrieswherehumanrightsaren’twellestablished.

StrategyAmnesty is a charity and there was no paid media budget to spend. Consequently, the campaign needed to be something that people wouldtalkaboutandshare.Thebrand’ssolutionwastocreate ‘TrialbyTimeline’,aFacebookapplicationwhichscanneddata fromusers’FacebookTimelinestoshowthemwhattheirbehaviourwould have cost them in other countries around the world. Theapplicationlookedatusers’personalprofiledatasuchas age, nationality, relationship status, religious and political views, friends, and everything they had ever liked, posted or written on the social platform.

Case study | Amnesty International

Trial by Timeline


Users received a summary of punishments their behaviour would have incurred in various countries. Users were encouraged to take action by joining the global movement and sharing their results with friends to create viral reach for the cause.

ImpactThe campaign sought to increase awareness, with a target of generating 100,000 unique visits to the Amnesty microsite. The campaign exceeded targets by driving 340,00038 unique visits, with an average visit duration of 7min 27secs. These visits extended beyond New Zealand, originating from over 200 countries. The Amnesty International New Zealand Facebook community grewbyover500%from3,720to20,000,creatingabigger base for the charity to communicate with on an ongoing basis.

Agency Credits

unique visits340,000

Case study | Amnesty International

Trial by Timeline


To support the launch of hacker video game, ‘Watch Dogs’, Ubisoft’s app pulled information from a user’s Facebook activity to build a profile of the user that could be used by an assassin.

ObjectivesTo drive interest in the 2014 launch of its newhackervideogame‘WatchDogs’, Ubisoft created an app that showed users just how much information about themselves they shared online.

StrategyTheteamtappedintothepublic’sfearsaboutonline privacy by creating an app, ‘Digital Shadow’,whichpulledinformationfromauser’sFacebookactivitytobuildacomprehensive dossier of an individual asifheorshewereanassassin’starget. This information included:

• Whoyouare–surfacinganyphotostaggedaspublic• Whoyoucareabout–includingwhoknowsthemostinformation

about you that could be used against you, based on who the user interacts with and frequency of interaction

• Whatmakesyoutick–acharacterprofile,basedonactivity• Timesofvulnerability–thebesttimetostrikebasedon

when you are most active on Facebook • Location–whereintheworldauserwasmostlikelytobe

found, if location services or geo-tagging are turned on• Estimatedsalary–basedoneducationandjobtitle• Alistofpossiblepasswordstouseinhackingaccounts,

generated from frequently used words, names of pets and other information

Case study | Ubisoft Watch Dogs

Digital Shadow


Basedonalltheinformationscraped,‘DigitalShadow’predictedhowmuchaperson’sdatawasworth.Userswere then given the opportunity to share the details revealed with their friends online.

ImpactMore than two million unique visitors went to the‘DigitalShadow’site,withusertimeaveraging overfiveminutes.Thehighlypersonalexperiencesparked controversy beyond the gamer crowd, with news organisations and privacy advocates fuelling the widespread debate.

Agency Credits

2m+unique visitors

Case study | Ubisoft Watch Dogs

Digital Shadow

8. Looking ahead: the hype cycle, IoT and AI | 133

III. What next?8. Looking ahead: the hype cycle, IoT and AI

8.1 Thehypecycle:it’sstillearlydays8.2 TheInternetofThings8.3 ArtificialIntelligence

9. Future challenges

9.1 How can we balance relevance and reach?9.2 What does brand experience mean in the

age of personalisation?9.3 Data savvy, not data driven: what are the

limitations of data and analytics?9.4 Whose data is it anyway?9.5 What is the future role for agencies?


8. Looking ahead: the hype cycle, IoT and AI

8.1 The hype cycle: it’s still early days

8.2 The Internet of Things

8.3 ArtificialIntelligence

Gartner Hype Cycle


The hype cycle: it’s still early days

It is usual for new technologies to follow a similar trajectory. Gartner, the technology research group, describes this as a hype cycle characterised by fivedistinctphases39. Typically, the cycle begins with a phase of early adoption of and experimentation in the new technology. The publicity generated by success stories associated with the technology causes more companies to take note and join these early adopters, inflatingexpectationsaboutthetechnology’spotential.Thiswillleadtoaperiod of disillusionment in which early experiments fail to meet the unrealistic expectations placed on them.

This is often followed by a period in which users gain a more concrete and robust understanding of the business benefitsofthenewtechnologyandtechproviders release improved versions of the product. These developments improve understanding and working processes, bringing the technology to the point where it has developed sufficientadoptionandmaturitytocontributesignificantlytobusinessbenefitsandproductivity.

Source: Gartner

8.1

Technology trigger

Trough of disillusionment

Slope of enlightenment

Plateau of productivity

Peakofinflatedexpectations

Vis

ibili

ty

Maturity


In this context, personalisation can be considered to be at the stage where it hasattractedasufficientnumberofearlyadopters to experiment in this area and the results have raised expectations. In many cases, organisations have been able to identify success stories from early tests. However, the complexities around data and technology and the investment required serve as barriers to more meaningful and wider uptake by organisations.

Othertechnology‘triggers’,suchastheInternetofThingsandArtificialIntelligence, are at earlier stages in the cycle and will continue to drive new opportunities and experimentation. In this section, we explore these developments and the implications these have for future trends in personalisation.

...personalisation can be considered to be at the stage where it has attracted a sufficient number of early adopters to experiment in this area and the results have raised expectations.


The Internet of Things

What is the Internet of Things?

The Internet of Things (IoT) is the networking of physical objects — such as wearable devices, vehicles, andbuildings—thatarefittedwithsensors and network connectivity which permit these objects to gather and exchange information. It creates a new set of touchpoints for consumers and brands to interact. It has potential to be both a driver and enabler of personalisation, as well as to change how the industry thinks about peer- to-peer networks and data.

The new possibilities already created byfitnesswearables,connectedcoffeemachines and smart energy meters provide potential added value and convenience for consumers. These devices typically make it easier to complete goals or carry out tasks. Theycansolveproblemsand/or create new demand. (For example, Nespresso’sconnectedcoffeemachine,Prodigio, enables customers to schedule a brew through a smartphone app as wellasmonitorandre-ordercoffeecapsules to ensure supplies never run out.)

In the context of social media and communications, IoT provides new possibilities for consumers to share information with one another, and with brands. Early applications currently rangefromfitnesswearableswhich allow you to share your new personal bestwithpeers,to‘smart’clothingcontaining connected sensors enabling you to interact with peers and loved ones remotely (examples include CuteCircuit’sHugShirtand Durex’sFundawear).

For brands, IoT has the potential to change sales models and the nature of brand-consumer relationships, particularly when innovation is already redefiningtheboundariesbetweenproducts and services. For example, Grazeoffersasubscriptionsnackingservice in a market traditionally dominated by stand-alone products, and change is evident in many other categories from shaving (the subscription-based Dollar Shave Club), to music (Spotify), to groceries (Hello Fresh).

8.2


How will the IoT shape the future of personalisation?

The IoT could shape the future of personalised consumer experiences in three ways:

A.Brands will be able to personalise experiences in ways which were not previously possible. The increase in devices and touchpoints will provide more opportunities for delivery and engagement.

B.Asproduct/servicedeliverytransforms and the number of brand touchpoints increases, marketers will have access to more data to fuel personalisation, including sensory and contextual information.

C.In an ever more complex environment, providing consistent and relevant experiences for consumers will become increasingly important, allowing brands to cut through the clutter.


ArtificialIntelligence

Many case studies in this guide demonstrate examples of increased personalisationtodefinedgroupsratherthan unique experiences for individual customers.Thisreflectstheoperationaland cost challenges involved in trying to tailor experiences to increasingly granular audience segments on a larger scale.

It may be possible to predict what is appropriate and relevant personalisation for an individual or a small group. But will it be viable to do this for 100 discrete groups of customers? How about 10,000 groups or 100,000? Common sense would suggest that it would not becost-effectiveforanorganisationto tailor propositions beyond a certain granularity. However, developments suchasArtificialIntelligence(AI) may in future make it more viable forbrandstoofferahigherdegree of personalisation at scale.

WhatisArtificialIntelligence?AIisafieldofstudyseekingtocreatemachines capable of processes or behaviour which would usually require human intelligence. It encompasses machine learning techniques with the ability to detect patterns in data and learn from experiences to modify processing based on new information.

One area of AI attracting increasing attention relates to chatbots. These are an application of AI and machine learning techniques in which bots are used to provide responses to customers via social media and mobile messaging apps. There have been some successful pilot exercises with chatbots, but also some widely publicised failures. Of course, a chatbot will be limited by the quality of the dataset from which it learns. And there is certainly a need for further development to limit brand risk. In some instances, the public knowingly

8.3

Developments such as Artificial Intelligence may in future make it more viable for brands to offer a higher degree of personalisation at scale.


trickedbotsintomakingoffensivecomments.Coca-Cola’sautomated#MakeItHappy campaign in 2015 was suspended after it was tricked into tweetinglinesfromAdolfHitler’s‘Mein Kampf’.Similarly,in2016Microsoft’sAIchatbot,Tay,wastakenofflineforposting racist and genocidal tweets. However,thetechnology’sabilitytoprocess high volumes of responses at speed makes it an attractive option for use in customer service.

The Chinese market provides good use cases in this respect. An estimated 600m Chinese people use messaging app WeChat, to do a variety of tasks frombookingdoctor’sappointmentstopaying utility bills without having to go via a third-party website or app.

Theairline,KLM,wasoneofthefirstEuropean brands to launch a chatbot for Facebook Messenger. The service enablesKLMpassengerstoreceivetheiritinerary,flightupdates,check-innotifications,getboardingpasses,andrebookflightsallfromonethreadwithinFacebook Messenger. There is no need togototheKLMwebsiteordownloadits app.

The opportunity for bots to take the lead in managing the customer experience is obvious. Humans are very comfortable communicating via text, so combining a text user interface with capabilities that anticipatetheuser’sneedscouldformthe basis of a powerful proposition to consumers.ExamplesincludefitnessappLark,andfintechappssuchasDigit and Penny. These services are designed to help consumers arrive at desirableoutcomes(e.g.betterfitness/increased savings). They use text-based, conversational interfaces, integrating withbrands’existinginfrastructures.

KLM passengers can receive their itinerary, flight updates, check-in notifications, get boarding passes, and rebook flights all from one thread within Facebook Messenger.


How will AI shape the future of personalisation?

If machine learning can enable organisations to deliver personalised customer service responses or recommendations at scale, then this has huge implications for personalisation.

This scenario touches on questions voiced throughout this report. Will consumersofservicesbasedonAIfindtoo much personalisation creepy and will theiraffinitywiththebrandstarttofallintoan‘uncannyvalley’?Iforganisationsrely on AI to personalise interactions with consumers on an individual basis, then how will they ensure that consumers still have a coherent and consistent experience of their brand? Notwithstanding these concerns, it is hard to imagine that AI will not be more widely used in future to manage the customer experience.

If organisations rely on AI to personalise interactions with consumers on an individual basis, then how will they ensure that consumers still have a coherent and consistent experience of their brand?

9. Future challenges | 142

9. Future challenges

9.1 How can we balance

relevance and reach?

9.2 What does brand experience

mean in the age of personalisation?

9.3 Data savvy, not data driven: what are

the limitations of data and analytics?

9.4 Whose data is it anyway?

9.5 What is the future role for agencies?


As brands and agencies, we need to challenge ourselves and address areas which are critical to the successful future development of relevant, integrated and consistent personalised experiences for customers.

The principal questions we face in this regard are:

How can we balance relevance and reach?

9.1

9.49.4

9.2 9.3What does it mean

to deliver brand experience in the age of personalisation?

What are the limitations of data

and analytics?

Whose data is it anyway?

What is the future role for agencies?


How can we balance relevance and reach?

As marketers, we often debate the tension between relevance and reach. Advances in technology and data availability have enabled brands totargettheireffortswithgreatergranularity, including down to the level of individual customers.

Brands such as O2 and EE that have adopted targeted, data-led strategies report that these have produced increased engagement and business benefitssuchasimprovedconversionrates and lower costs, compared to untargeted approaches.

Yettheevidencefrommanyyears’analysis by the Ehrenberg-Bass Institute – popularised in books such as Prof ByronSharp’s,‘How Brands Grow’40 – encourages us to prioritise breadth andreachasthemosteffectiveways to grow and maintain brands.

ProfSharp’scentralargumentisthatadvertising needs to deliver broad reach tobetrulyeffective.Inthisview,brandshave often taken a more limited view of their potential buyers and their potential competitor brands than is actually the case.Forexample,apremiumcoffeebrand may view its main rivals as other premium products. In fact, it is also competing with value products, aswellastea,energydrinks,caffeinepills and even a morning jog or swim in its ability to refresh and wake us up. In many cases, by aiming for broad reach (including outside the immediate target audience), brands will be able to influencebuyerswhoarenotcurrently in their target market but who could be in future.

9.1

...brands have often taken a more limited view of their potential buyers and their potential competitor brands than is actually the case.


Furthermore, a brand that reaches out beyond its target market with a consistency of messaging and creative can engender a widely held perception of that brand and its values. Wastage in this respect is necessary to create the shared cultural meanings associated with a brand. For example, in their article, ‘AdMap Mythbuster: Waste Not’41 ,LesBinetandSarahCarter, of adam&eveDDB, contend that broadcast advertising has the ability to change the way society feels about a brand through creating shared experiences and talk-ability.

This is particularly applicable for brands that are frequently consumed in public, such as newspapers, clothes, drinks or credit cards, where the value of the goods is in part derived from how those outside the target market perceive the brand. Think of how the aspirational perception of luxury fashion brands is in part shaped by those consumers who cannotaffordtobuythem,butwhoareexposed to them when other consumers wear those brands in public.

Today, personalised marketing is typically used to deliver highly targeted messages with relevant content to limited groups. However, by consistently narrowing our target market, are we reducing our ability to reach potential customers and verging towards deliveringefficienciesandshort-termgains as opposed to long-term brand effects,suchasfame?

Whileshort-termeffectscanleadtoimmediate gains to the top and bottom line, the IPA publication ‘The Long and the Short of It’42 demonstrates that the sumofshort-termeffectsdoesnotequalthatoflong-termeffects,andshort-termeffectsarelesslikelytogenerateimprovements in key indicators of brand health, such as brand value and price inelasticity. If brands focus too much on short-termeffects,theycanriskerodingthese critical brand health indicators and base sales over the longer-term.

...by consistently narrowing our target market, are we reducing our ability to reach potential customers and verging towards delivering efficiencies and short-term gains as opposed to long-term brand effects, such as fame?


Rather than take up polarised positions in this debate, we should be asking, what is the appropriate balance of relevance and reach for each brand? How can relevance and reach be used both separately and together to achieve theorganisation’soverallshortand long-term goals, and when is it better to prioritise one type of objective over the other?

This report argues that personalisation candeliverbenefitstotheconsumerand the business. However, it is vital that if brands are to make a success of personalisation that they are clear about why and how they are deploying personalisation, and how they will measurethebenefitsofpersonalisingactivities. Organisations need to findandreviewthe‘right’amountofpersonalisation for their particular brand tooffer,sincetoomuchmayputoffconsumers or be too costly or onerous to sustain. They need to be equally clear about when personalisation should not be used (or not used yet), because other channels or approaches would bemoreappropriate,moreeffective or more scalable.

As data and technology provide organisations with more ways to invest their money and resources, they need to ensure they do not jettison without good cause the existing evidence of how mass marketing can work to build brands. It is therefore prudent for organisationstoadopta‘testandlearn’approach to personalisation to ensure that they can develop the learnings on which to build going forward, but also to ensure that these learnings feed into theorganisation’swiderknowledge base about how to balance relevance and reach across all its activities, new and old.

Brands need to be clear about why and how they are deploying personalisation and how they will measure the benefits.


What does brand experience mean in the age of personalisation?

Anorganisation’sbrandrepresentsaconsistent expression of its creative identity,itsraisond’etreanditsvalues.It gives consumers and organisations ameansofdifferentiatingbetweenproviders and lays the foundations forthepublic’sexpectationsof,andrelationshipwith,thebrandowner’sorganisation. Branding communicates the promise of functional and emotional benefitsthatthebrandorganisation aims to deliver in reality.

By using broadcast media to communicate a consistent set of imagery and other creative assets, brands can construct and maintain what Byron Sharp calls ‘positive memorystructures’whichshapetheperception of the brand and its potential benefitssharedbyaudiencesandthewider market. It is little wonder then that some see personalisation as a challenge to this way of thinking about brand building.

For, if I am receiving a unique brand experience tailored to me, will I have the same consistent idea of the brand as the next person? Furthermore, I may receive a personalised experience of a brand via one of its touchpoints, but not via others creating inconsistencies in my own experience. Even if personalisation were not potentially contributing to the fragmentation of the brand experience, the growth of digital media and peer-to-peer networks has already given consumers greater power to shape perceptions of a brand that are at odds with the carefully crafted messages andimagerythatthebrand’sowner has developed.

In such a context, the role of brand and the way in which it is managed become more important. We are moving from an age of marketing a carefully crafted external image to one where we must deliver a consistent and authentic, end-to-endexperience.Abrand’s values and what it stands for needs to be central to delivery of every aspect of the customer journey and experience, enabling the brand owner to continue toreapthebenefitsthatconsistentbranding provides.

9.2

We are moving from an age of marketing a carefully crafted external image to one where we must deliver a consistent and authentic end-to-end experience.


Data savvy, not data driven: what are the limitations of data and analytics?

Personalisation is just one example of the shift towards a data-driven culture which is taking place within many organisations. Increasingly, organisations are using data and analytics to underpin significantinvestmentdecisionsandprocesses previously more reliant on human creativity and judgment. But whilst acknowledging the potential of data and analytics to play a critical roleindeliveringeffectivebrandexperiences, we must not ignore their fallibilities and strategic limitations.

The insight we draw from data and the uses we can put this insight to rely on the availability, integrity and completeness of the underlying dataset, as well as the way in which we treat and analyse it. Marketers should acknowledge any limitations to ensure that data informs their decisions, but does not dictate them.

For instance, if a brand has no data on a new customer, how can it create the most relevant customer experience? If the user shares an account with her partner, how can the brand ensure it surfaces relevant content for her as an individual?

Algorithms can be powerful tools for predicting preferences based on the individual’spastbehaviourorthoseofothercustomersthat‘looklike’theindividual. But as section 2.3 argued, there is a risk that algorithms can simply act to reinforce existing behaviours, limitingtheuser’sdiscoveryoraccess to other information, products and voices,andofferingonlyarestrictedexpression of the brand.

9.3

Marketers should acknowledge any limitations to ensure that data informs their decisions, but does not dictate them.


The speed, volume, accuracy and seeming completeness of the data available to modern organisations can in itself pose additional challenges to the integrity of insight. The US data expert Nate Silver43 suggests that our predictions may be actually more prone to failure in the era of big data:

“We face danger whenever information growth outpaces our understanding of how to process it. The last forty years of human history imply that it can still take a long time to translate information into useful knowledge and that, if we are not careful, we may take a step back in the meantime.”

Since there is an exponential increase in the amount of available information, there is also an accompanying increase in the number of hypotheses to investigate and a commensurate growth in the number of false positives generated.

It has long been recognised that, given a large enough sample size, most data pointswillshowstatisticallysignificantcorrelations. At some level, everything is related to everything else.

However, this can lead us to believe there are real relationships in the data whereas these linkages might be trivial. The risk is the data is simply generating a larger number of false positives – findingsthatarestatisticallysignificantbut manifestly wrong.

In these circumstances, validation processes, through analytical techniques, but also through human input and contextual expertise, become ever more critical. Although commentators may downplay the role of human judgement in data analytics, it remains central to our ability to extract value from data.

It is easy to ignore the challenges and limitations of data and analytics. We all want to assume that the answers will be straightforward and the interpretations objective. But we need to address these challenges head on. Otherwise, we run the risk of failing to capitalise on the much promised opportunities that data and analytics can deliver.


Whose data is it anyway?

Data ownership is a thread connecting many of the challenges associated with personalisation and the use of social media data. There is a tension between the value generated for brands (and consumers) by collecting personal data and the perceived and real threats to privacy that this data collection can spark. Consumers have complex and changing attitudes towards their personal data. Personalisation strategies needtofindthesweetspot,deliveringa customer experience led by demand and customer value rather than by the mushrooming supply of data.

There is also a tension between the roles of advertisers, media owners (including social media platforms) and consumers as to who should own this data. Access to many social media data points is dependentupontheplatformowners’strategies, which can be subject to change with little warning. As a result, brands have limited how much they rely on and integrate social data.

We are increasingly seeing consumers empowered to own and control use of their own data. Brands are emerging that allow consumers to curate the data about their own lives and use services

which leverage the value of this data. Commentators, such as the writer and academic Doc Searls, describe this as a move from CRM (customer relationship management) to VRM (vendor relationship management)44. In this environment, it is argued, the balance of power will subtly shift from the vendor to the individual who is increasingly able to aggregate what the brand knows about them.

A good example of the way this area is developing is Citizenme, an app that “helps you take control of your data andgainvaluefromit”.Theappoffers a range of ways in which users can collect data about themselves and generate value from it. In this shift, marketing will no longer be something that gets done to consumers but instead will empower consumers to take more control and exert more agency in the marketplace.

9.4

We are increasingly seeing consumers empowered to own and control use of their own data.


Personal insightsWiththeuser’spermission,socialmediaprofilesareanalysedtoshowhowtheirpersonalitytraitsvaryacrossdifferentnetworks. E.g. Are you an extrovert on Facebook but more introverted on Twitter?

Cash rewardsCitizenme’sbrandpartnerscanmakecashofferstoCitizenmeusersfortheirpersonal data. All exchanges of data remain anonymous for market research and analytics purposes.

Thelong-termaimofCitizenmeistoapplyArtificialIntelligence tosupportusers’lifedecisionsinareassuchasfinancialmanagement and health. It believes that when people better understand themselves, businesses better understand people.

These types of organisation are reframing the discussion around data in ways which have the potential to widen the possibilities for personalisation, so that in addition to brands choosing to introduce more personalisation for consumers, consumers would be able to take the lead in personalising their relationship with brands.

Altruistic donationsCitizenme users can donate their data, anonymously, to charities for research and analytics purposes.

FunThe app also includes quick polls and quizzes that users can take part in to see how their opinions compare to others.

The types of value currently offered via the Citizenme app fall into four categories


What is the future role for agencies?

New capabilities and partnershipsTypically, creativity and data have been kept separate with agencies specialising in one or the other area.

To some practitioners, data is seen as a barrier to creativity, a straitjacket which limitsideasandstiflesrisk-taking.Theyfear that when data is applied to the creative process, it can overwhelm other elements such as creative talent, and become over relied upon in decision-making. In addition, the data may simply require new skills that the agency does not possess.

However, many agencies are now recognising the importance of data and technology to creative executions and are looking to address the knowledge and skills gap to meet changing client demands. Some larger creative agencies are starting to build dedicated data and analytics teams.

Other agencies are also setting up innovation labs and similar structures to help further their tech propositions and build relationships with the proliferation of niche tech players and start-ups that are getting industry and client attention. Agencies that are not helping clients accessandbenefitfromtechinnovationrisk being disintermediated, increasing the burden on clients of managing multiple suppliers.

9.5

Agency models need to adapt to support brands in using data and technology to improve their relationships with consumers.

To some practitioners, data is seen as a barrier to creativity, a straitjacket which limits ideas and stifles risk-taking.


The trusted adviser roleAgencies need to become the trusted advisers to their clients in order to remain relevant and avoid the risk of disintermediation. Close partnerships with clients will help agencies understandthemarket’schangingrequirements and assist them in ensuring theagency’spropositionandcapabilitiesarefitforpurpose.Ascapabilities,such as personalisation and social media,exertinfluenceacrossbroaderswathesoftheclient’sbusiness,itisimportant that agencies aim to develop relationships that are broad as well as deepwithintheclient’sorganisation.

Some clients will also take more capabilities in-house to equip their organisations to handle larger volumes of data in real time. This trend is developingatdifferentratesdependingon the type, maturity and appetite of the individual organisation.

While there remains a clear need for agencies to maintain their existing roles and functions, it is also important that agencies prepare to act in future as trusted advisers in supporting clients with the set-up of such in-house capabilities.

Close partnerships with clients will help agencies understand the market’s changing requirements and assist them in ensuring the agency’s proposition and capabilities are fit for purpose.


Contributors and references


The content of this book draws on a series of in-depth interviews with contributors from agency, client-side and social media platforms, and a thorough review of industry case studies and academic research.

A huge thank you to all of the project contributors listed below. They have been instrumental in shaping the insight and advice in this guide. And a particular thank you to my co-author Colin Strong of Ipsos and to the rest of the #IPASocialWorks team for their valuable input and guidance.

Celina Burnett

#IPASocialWorks Team• StephenMaher,MBA&Chairman,

The Marketing Society• JanetHull,IPA• ChristianWalsh,TheMarket

Research Society• MichaelPiggott,TheMarketing

Society• FranCassidy,CassidyMedia

Partnership• SimeonDuckworth,GroupM• JamesDevon,MBA• CelinaBurnett,ASOS• RayPoynter,TheFuturePlace• MarkEarls,Herdmeister• ChristopherWellbelove,BT• ChloeHarper,TfL• JessicaSalmon,O2• DominicGrounsell,Travelex• SandraHughes,Facebook• JakeSteadman,Twitter• NatGreywoode,Twitter• AndyPang,Snapchat

‘One Not Everyone’ Contributors• CharliePalmer,ManagingEditor,

Channel 4• GarethPrice,HeadofInsight,

The Social Partners• HannahFisher,

Head of Marketing, RSA• JakeSteadman,ResearchDirector,

International, Twitter• JoelWindels,VPMarketing,

Brandwatch• KristianLorenzon,HeadofSocial

Media, Telefonica• MattRebeiro,AssociateDirector

Digital Intelligence, Iris Concise• PeterKirk,HeadofMarketing&

Audiences, Personalisation, BBC• RobBlackie,GlobalHeadofSocial

Products, Ogilvy & Mather • RussellLoarridge,Director,Janrain• SandraHughes,MeasurementLead

Northern, Central & Eastern Europe, Facebook

• SimeonDuckworth,HeadofData& Analytics Strategic Development, GroupM

• SimonLuff,StrategyDirector,Wunderman


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