Sarit Prajna Sahu_Management Report to The Nielsen Company

8/11/2019 Sarit Prajna Sahu_Management Report to The Nielsen Company

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0 | P a g e S a r i t P r a j n a S a h u

Management Report

USE OF BIG DATA FOR HELPING FMCG CLIENTS TO

DEVELOP A CUSTOMER-CENTRIC MODEL AND REDUCE

RISKS THROUGH OPEN INNOVATION METHODOLOGY

WARWICK BUSINESS SCHOOL

September 13, 2013

Authored by: Sarit Prajna Sahu


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EXECUTIVE SUMMARY

Among all the dimensions of Big Data, the value dimension of it will see special

focus in this report (See Annex 1). Huge volume of data is generated every second

from sources such as websites, social Media, cloud, internet of Things and

smartphones/tablets/mobile devices which are structured as well as unstructured in

the form of text messages, voice recordings, and social media content. Given that

there is enormous digital data available, it can be said that offline content is still

relevant. Thus, it has become essential that sources of data be tracked. Data from

all the sources should not be treated in a segregated manner but conjointly. The

biggest issue in handling Big Data and generating value out of it will be seen as theorganisational value network (Christensen, 1997). Ultimately it will be seen how

this value network of Nielsen should be such that it not only can suggest innovative

ideas to its clients but can also do it seamlessly. The work of the following authors:

Grant McCracken, Clayton M. Christensen and C.K.Prahalad & M.S.Krishnan have set

the foundation of the recommendations. The reason behind this is that all of them

convey that the organisational structure and culture are important to be innovative.

In a KPMG survey, 19% of the respondents globally and 24% of EMEA respondents

claim that Risk Management is one of the barriers to commercialising disruptive

technology innovations. Among the top 6 Market Research companies where

Nielsen holds position 1 as per honomichl top 50, 2013 report (See Annex 13), 3 of

them already have various analytics services already. Thus, Big Data Analytics,

Innovation and Risk Management have been brought together so that Nielsen can

provide a competitive edge for its clients. Since, companies are now facing the

issues related to their functions operating in silos, it has been recommended that

Data Analytics, Innovation and Risk Management should not only be just capabilities

of the company but should work interdependently and any data collected and

decisions made should through this combined capability and then pass through to

the clients such that customers and clients are also included in the eco-system.

Research data published by Data Blueprint this year says that more than 60% of the

organisations are planning to hire a CDO but since they are not very sure whom the

position should report, they are delaying the process. Since we are dealing with Big

Data, it has been suggested that Nielsen introduces this role in order to be a pioneer

in this industry in order to not only handle disruptive and breakthrough innovations

but also help create them for its clients.


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TABLE OF CONTENTS

Executive Summary ..................................................................................................... 1

1. Introduction ............................................................. ............................................. 3

1.1 Scope ........................................................................................................................... 3

1.2 Key Objectives ............................................................................................................. 4

1.3 Method ........................................................................................................................ 4

1.4 Limitations ................................................................................................................... 5

1.5 Assumptions ................................................................................................................ 5

2. Key Findings ............................................................. ............................................. 6

2.1 T+I Factor ..................................................................................................................... 7

2.2 The Risk Factor ................................................................ ........................................... 17

2.2.1 Data Risks ................................................................................................................. .. 17

2.2.2 Innovation Risks ............................................................................................... .......... 18

3. Discussion ................................................................ ........................................... 19

3.1 Could/Should Nielsen venture into Big Data Analysis on behalf of its clients? ......... 20

3.2 How can investing in Big Data Analytics boost Nielsens position as a Global

Marketing Research Leader? ..................................................................................... 21

3.3 Would investment in Big Data provide competitive and adaptive advantage to its

clients? ....................................................................................................................... 23

4. Recommendation and Implementation ............................................................. 24


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1. INTRODUCTION

This report has the purpose to address Big Data at a level beyond hype such that it can be used for

innovating such that it can help Nielsen to gain competitive and adaptive advantage for its FMCG

clients and that it can assure its Nmero Uno position in the marketing research industry.

The diagram below shows the roadmap for both Nielsen and its clients to use big data for innovation

in order to ensure that both competitive as well as adaptive advantage be achieved.

Fig 1 . 1 . A FR O M V I C I O US T O V I R T UO US C Y C L E

This is a crude representation of the route to success and the recommendation section will show the

actual picture of the solution.

1.1 SCOPE Within the scope of this project was to discover the strategic reasoning behind Nielsen

investing in Big Data and Analytics such that it can innovate for its FMCG clients and create

competitive advantage for them by helping them model their business as customer-centric

and managing their risks. Taking an independent perspective of Nielsens resources and

capabilities, this project aimed to confirm the organisational strength of the company to be

able to create analytics capability. Thus, the HOWs and WHATs of Big Data analytics were

to be addressed such that the overall impact comes out to be robust and long-term in

nature. This project did not intentionally expand upon (though the future research section

did touch upon) the operational aspects of Big Data Analytics for the purpose of extensive

analysis of the strategic front and avoiding it from appearing highly technical.


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1.2 KEY OBJECTIVES

1.2.1

CO U L D/ SHOULD N I E L S E N VE N T U R E I N T O BIG DA TA AN A LYSIS O N BE HA L F O F ITS

C L I E N T S?

Under this question, it will be covered whether Nielsen should make this strategic decision to

invest in Big Data infrastructure and analytics and what could be the implications of doing this.

1.2.2 HO W C AN I N VE S T I N G I N BIG DATA AN AL Y TICS BO O ST N IELSEN S P O S I T I O N AS A

G L O BAL MAR KE T I N G R E S E AR C H L E AD E R ?

The new capabilities need to be acquired will be discussed in the report such that benefits of Big

Data investments provide sustainable competitive advantage to Nielsen against its competitors

even though they make similar investments and have the same capabilities. Thus, the charisma

will remain in the implementation of the capability rather than the capability itself.

1.2.3

WO U L D I N VE S T ME N T I N BIG DATA P RO VIDE CO MPE TIT I VE AN D A DAPTI VE

ADVAN TAG E T O IT S CL I E N T S?

It is the adaptive nature of an organisation that can make it sustainably competitive as well.

Again, the question is how can Nielsen create it for the client? The answer to it will throw light on

the organisational structure of a market research company such that consumers, data sources,

suppliers, clients all belong to the same system and have to communicate with each other via the

new driving capability that will be suggested in the recommendation section.

1.3 METHOD

The methodologies adopted to carry out this dissertation required primary as well as secondary

researches. Primary research will involve conducting surveys targeting senior professionals from B2B

businesses, online conferencing with academicians and Nielsens senior executives. The B2B survey is

planned with the intention to capture information on Big Data and Analytics investments from

managers belonging to various functional areas such as Marketing, IT, Executive, HR and Operations.

The secondary research will involve information gathering from sources such as blogs, Newspaper

articles, Trade Press, Marketing and FMCG news, Social Media, corporate white papers, published

statistical data or surveys conducted by organisations such as KPMG, McKinsey & Company, PwC,

IBM, booz&co etc. related to the topics such as Strategic Marketing, Big Data and Analytics, Predictive

Analytics, Crowdsourcing, Global Advertising and Innovation.


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1.4 LIMITATIONS

As mentioned earlier, this report is limited to addressing strategic aspects of Big Data Analytics and

innovation using it rather than the operational aspects which should include the exacts tools, skills

and other resources required to conduct the same. Given that Nielsen already has business

partnership with companies such as Google, IBM, Facebook, Twitter, Microsoft; gathering knowledge

on tools from them is only a matter of time and negotiation but understanding the manner in which

these tools should be used is what needs to be taken care of in the initial stages before the major

investment is made since once a structure is made, it is highly difficult to restructure and could slow

down the organisation when everything else around it rapidly moving and changing at the same time.

Some of the methodology related limitations are:

Generalizability Because the no. of respondents is too low to even consider conducting a

quantitative analysis thus, it would be considered more than like opinions received and will face a

constraint of not being able to generalise the outcome.

Lack of B2C surveySince it is only B2B survey that has been taken as it addresses the business case

where Nielsen and its FMCG clients are involved, a probable scope of B2C survey has been ignored

which could have given better insights on some of the new marketing research techniques which can

be worth exploring.

Inaccessible Paid surveys or publications This dissertation lacks insightful information that can be

found from paid surveys or reports and hence may not be able to cover the business case in that in

depth as it is required.

Independent Research The nature of the dissertation was more like a desk-based dissertation,

access to some of the company related information from Nielsen was not always available but it was

well understood between the me and the company prior to working on the project.

1.5 ASSUMPTIONS

As mentioned in the limitation section, it has been assumed that Nielsen must be getting technology

related consulting from it business partners and that the company being the Worlds number 1

market research company already has the resources and capabilities required in order to be able toincorporate these new tools into the system. Considering that Neurofocus has been acquired, it is

assumed that Nielsen has already been working on Big Data and analytics since brain-imaging and

neuroscience already capture high insightful information about consumers in huge volumes. Initially it

was presumed that Nielsen does not have an analytics capability but after some basic research it was

found out that the innovation analytics is a new capability and will soon come up with solutions to be

provided to the customers.


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2.KEY FINDINGS

A reverse-order approach will be taken toward the key findings. The route to competitive advantage

will be provided immediately thereafter each of the factors associated with it will be synthesised and

key findings analysed.

Knowing that the macro & micro economic trends and risks will always prevail in the marketplace, an

equation to competitive advantage has been framed.

((T+I))R = A WHERE T = MAJOR TRENDS, I = INNOVATION, R = RISKS AND A = COMPETITIVE

ADVANTAGE

The equation explains that Innovation can be done in the presence of the major trends at both macro

and micro level and when the risks are identified and mitigated can provide the competitive

advantage to an organisation. The reason behind creating this equation is to stress upon the need for

Big Data to captures the trends and risks such that the unknown variables are resolved such that

the successful innovations can be created giving the competitive advantage to companies.

Findings related to the numerator and denominator of the left hand side of the equation will be

addressed one by one.

Before T+I is explained, the three major trends (T) identified have been shown in the diagram

below.

F I G 2.A MA J O R T R E N D S I N T H E P R E S E N C E O F W H I C H IN N O V A T I O N S H O U L D O C C U R

Major Trends

Macro & Micro-Economic Factors

Digitalisation Globalisation


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2.1 T+I FACTOR

Before the PESTLE is published, some of the forecasts about the FMCG industry will be explained

now. As per Fig 2.1.a, the market value forecast for almost all the types of product in the

industry is the highest in the APAC region whereas as per Fig 2.1.b, EMEA regions holds the

maximum percentage share across all the types of products.

F I G 2.1. A - R E G I O N A ND P R O DUC T T Y P E W I S E M A R KE T V A L UE F O R E C A S T B E T W E E N

2011/12 T I L L 2016/17 (MA R K E TL I N E , 2012)

-2.00

0.00

2.00

4.00

6.00

8.00

10.00

12.00

AlcoholicDrinks

BathandShower

Beer

CannedFood

C

hilledandDeliFood

Dairy

FacialCare

Fragrances

FunctionalDrinks

HotDrinks

Juices

MaleToiletries

OralHygiene

PersonalProducts

PotatoChips

SkinCare

SunCare

Wine

Market Value ForecastMarketValueForecastperProductType-CAGRofthe

marketintheperiod2011/12-2016/17

Product Type

Region versus Market Value forecast

from 2011/12 - 2016/17 per Product

Type

APAC

EMEA

AMER


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F I G 2.1.B - GE O G R A P H I C SE G M E NT A T I O N F O R T H E WO R L D P E R PR O DUC T T Y P E I N 2011

(MA R K E TL I N E , 2012)

The reason behind specifying this trend is that from the existing forecasts at the macro level the

growth trends can be seen and it can be forecasted where the major investments should be done in

the coming years.

As per Annex 2-4 show that the major distribution channels in all the three major regions shown are

still super/hyper markets and independent retailers. This information is important because the

digitalisation of these distribution channels can be a great scope for market research industry to

gather first-hand information.

Fig 2.1.c shown below discusses the important political, economic, social and technological trends in

the coming years. It is crucial to understand the macro factors should be kept in mind before

employing predictive Analytics to take decision upon the insights for innovation.

0

10

20

30

40

50

60

70

PercentageSharebyValue,

2011

Product Type

Geographic Segmentation Per Product Type in 2011

Europe Asia-Pacific United States Rest of the World


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F I G 2.1.C MA C R O- E C O NO M I C T R E NDS T H A T C A N P O T E NT I A L L Y A F F E C T T H E FMCG I N D U S T R Y

I N T H E Y E A R S T O C O M E (MC K I NS E Y & CO M P A NY , 2010)

Digitalisation of objects and usage of internet on mobile devices are the major technological trends now

and are to prevail for some years from now. The figure below will be analysed to understand what Nielsen

and its FMCG clients should be focusing on.

As can be seen in Fig 2.1.d, Big Data, Natural Language question answering, consumer 3D printing,

gamification, wearable user interfaces, content analytics etc. are placed at the peak of inflated

expectations but are going to take 5-10 years to reach the plateau of productivity. This implies that it will

take companies around 5-10 years time to create value out of these and successfully commercialise them.

Does this mean that market research companies become reactive in nature and wait for these

technologies to actually create value and then propose their FMCG clients to start using these or

benefitting from these. How can Nielsen be proactive by pioneering in investing in Big Data such as to be

able to find out the where these technologies are moving and start leveraging their benefits for their

clients? This will be addressed soon in the report.

Political instability in the emergingmarkets

Overregulation

Government's response to fiscal deficit/debt

Corruption as a road block

Political

Increased Trade Protectionism

Changing Tax regimes

Increasingly volatile input costs

Labour shortage in emerging markets

Modernisation and Concentration of trade

Economic

Changing Demographics

Consumers going green

Rise of the digital consumer

Billion new consumer

Rise of the value segment

Health and wellness conscious consumers

Social

Internet of Things - Objects becoming digiitalised andsensor based

Cloud - Huge amount of data now stored over the cloudand small to medium enterprises already choosing thecomparatively economical method of storing data

eCommerce - An effect of the rise of digital consumersMobile - Increased usage of mobile devices and

internet on it have changed the marketing dynamics

Technological


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F I G 2.1.D L I N E S B L U R R I N G B E T W E E N H U M A N A N D M A C H I N E S (Paul Taylor , 2013)

Mobile devices and access of internet, websites and social media through these devices are the

upcoming trends and FMCG marketers can benefit highly from these. The internet use pattern in the

US and the UK can be seen in Fig 2.1.e.

F I G 2.1.E MO B I L E P H O NE US A G E P A T T E R N B Y T H E US A ND UK

US E R S (FO R R E S T E R , 2013)


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A further robust research on consumer attitude towards mobile advertising where there are

significant differences in Gen Y from different countries namely United States, France and China, can

be seen in the table below.

TA B L E 2.1. A D E S C R I P T I V E S T A T I S T I C S F O R I T E M S W I T H S I G NI F I C A NT DI F F E R E NC E S

A M O NG G R O UP S (WE L L S , E T A L . , 2012, P . 15)

TA B L E 2.1.B D E S C R I P T I VE S T A T I S T I C S F O R I T E M S M E A S U R I N G B E H A V I O UR A L I N T E N T I O N S

T O W A R D M O B I L E M A R KE T I NG (WE L L S , E T A L . , 2012, P . 17)


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Moving over to Social media, I would like to display the various social technologies that can be used

by consumers and enterprises instead of trying to influence why Social media is important. As

mentioned earlier, this report is more about the WHATs and HOWS rather than the WHYs.

F I G 2.1. F A P P L I C A T I O NS O F SO C I A L TE C H NO L O G I E S

(MC K I NS E Y G L O B A L IN S T I T U T E , 2012, P . 5)

The implications of these data related to digitalisation, mobile and social media especially the

influencing and word-of-mouth factors in conjunction with the concepts related to Permission-Based

location-aware mobile advertising, Customer Engagement Behaviour, War-gaming framework, REAN

model, IBM Social media analytics framework and Whole Nine-Yard methodology while dealing with

data analytics will be discussed soon. See Annex 5 10 to learn about these concepts such that the

application of these concepts in the discussion section will become clearer.

The questions asked during the survey conducted by me on Big Data Analytics are shown in the table

below. The respondents held positions such as CEO, CMO, Senior VP, Head of business units,

managers and IT specialists. The respondents not from the C-suite held positions in various functions

such as Analytics, R&D, Sales, Strategy & Innovation and supply chain. Interestingly, they were from a

good mix of industries such as automotive, FMCG, electronics, healthcare, IT, life Sciences,

Manufacturing, telecommunications and retail where their organisations were either B2B, B2C or

both and organizational size ranged from few 100s to more than 50,000 employees.


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Which one of these best describes your job role?

What is functional area?

Please specify your industry

Are you a B2B business or a B2C business or both?

How many employees are employed by your organisations?

What are the sources of data for your organisations?

Does your organisation plan to set up Big data infrastructure and analytics in future or is it

already set up?

If not yet set up or delayed setup then what are the possible reasons?

If already set up, What does your analytics department do with both structured and

unstructured data?

Rate these analytics from 1-5 as which ones do you think is much important to track for your

business currently? Where 1 - Least and 5 - Most

What describes the best use of data analytics results?

In what time frame will the analytics investments yield or expected to yield a positive return?

Which of these analytics would you want to be an in-house capability or outsourced?

If analytics is an in-house capability then who owns it?

Do you think investing in Big data initiatives alone can give your organisations will give you

competitive advantage?

If Yes (given that the right skills are available), then why?

If no, then what are the skills or resources required to give you the edge?

Where does your organisation currently find these skills?

Initiatives taken by your organisation to resolve the analytics skill gap issue

Where do the business/data analytics employees go after resigning the company?

Now, the difference between this survey and the surveys we find published in the web is that they

always are targeted to professionals holding a position such as only CEOs or CMOs. Since data can

come from anywhere and can and should be owned by all departments for the organisation to have

an agile operation technique, it is important that views from people at different positions and

departments are captured.

Majority of the respondents were from the FMCG, Retail and IT/Technology industry from

organisations with more than 10,000 employees or more. Interestingly, most respondents still say

that the major sources of data are the traditional media and retailers. Again, more than 50% of the

respondents say that they already have set up Big Data infrastructure in their organisation but rest do

not plan to have it yet. For those who have no plans to set the Big Data Infrastructure consider the

costs to be more than benefits, cannot see how Big Data can provide a competitive advantage or have

no clue. Out of the various kinds of analytics, organisations which already have set up Big Data

infrastructure or want to, rate Predictive analytics, Simulation, Business Rules, Optimisation and Data

mining to happen in-house while the ones they want to outsource are: Geo-spatial, text, video and

voice analytics.


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In a discussion with Andrew Bradford, VP, Marketing and Advertising, Nielsen, UK, the following

questions were asked.

The summary of the discussion is presented here.

He feels that the biggest change that has become a challenge in the current marker research industry

is the Data Quality. The panel based surveys conducted by Nielsen provided rich insights with less

junk information but again this has given lot of organisations to enter a market in a cheap way due to

the available of information for cheap. But again, with the lack of proper analytics it is difficult to say

which data has been generated by machines and which my human. Basically the issue lies behind not

knowing in what way the digital data is representing consumers. It was also interesting to know that

Nielsen already has partnerships with Google, Facebook, Twitter, Microsoft and such companies alike.

Not only that, Nielsen has acquired Neurofocus and SocialGuide which shows the how much value is


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provided to consumer brain psychology by understanding brain-images as well as trying to extract

emotional content out of the data available on the internet. The solution that Google is providing now

through Multi-Channel funnel claims to provide the various steps a customer took before making a

purchasing decision for the marketers to learn and focus their promotional effort accordingly. This

could be a probable answer to aggregation of offline and online data. But again, it will be seen that

the more than the data aggregation, it is the manner of flow of data inside-out and outside-in that

matters. He feels that the actual drivers of innovation are still developed economies which could be

conflicting with what C.K.Prahalad and M.S.Krishnan have to say in their book The New Age of

Innovation. As per his opinion, the consumer behavioural information through contextual

questioning is what matters and not the number of pages hits and tags etc. Regarding the

collaboration between the marketing function of the FMCG clients and Nielsens consultants, he had

to say that the companies like Unilever have understood this to be innovative in nature.

Innovation as we know can be incremental, breakthrough or disruptive in nature. As captured in a

webinar conducted by Nielsen, companies should have a good balance between Incremental and

breakthrough innovations. The reason behind this is nothing but Risk. If this is managed

intelligently, commercialisation of innovation will have a greater success rate. The various kinds of

Risks that an organisation has to deal with will be covered in the next section. But, how to integrate

the Risk Management into the system such that it creates a competitive advantage will be the key to

success. Again, understanding consumers hidden and latent needs are also important which can be

found out through sentiment analytics, emotion mining, brain-imaging, panel based surveys, mobile

access during shopping etc.

Globalisation faces multiple arguments from proponents and opponents and still does not have a

definitive side to it. FMCG industry reaped the benefits of Globalisation and has now understood that

they can survive in a different culture not by going against it but by taking the culture with it and is

smartly termed as Glocalisation. In this section few examples of Glocalisation from this industry will

be cited and discussed. From the launch of Dove Elixir Hair oil in India, it can be seen that the industry

has understood the concept of Glocalisation.

The three tensions of Globalisation are mentioned below (Rothenberg, 2003):

Individual choice versus societal choice

The free market versus government intervention

Local authority versus supra-local authority

Interestingly, the above three tensions can be achieved from Big Data especially website and social

media data. Analytics upon these kinds of data can provide information on the upcoming trends and

behaviour.


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As per the 9th

Annual CEO Survey by PWC, the figure below will state the challenges of Globalisation in

developed and developing economy perspective.

F I G 5.1.1.3. A GL O B A L I S A T I O N CH A L L E NG E S : DE V E L O P E D VE R S U S EM E R G I NG E C O NO M I E S

(PWC, 2005)


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2.2

THE RISK FACTOR

2.2.1 DA T A RIS KS

F I G 2.2.1. A TH E T O P B A R R I E R S T O C O M M E R C I A L I S E DI S R UP T I V E T E C H NO L O G Y

(CL O UD A ND MO B I L E ) I NNO V A T I O NS (KPMG, 2012)

Data confidentiality, Data Privacy, Data Quality are the various kinds of Data related risks that a

market research organisation will have to deal with. With this Data ethics should be discussed and the

four important elements of Big Data ethics are shown in the Fig 2.2.b.

Fig 2.2.1.B FO UR C O M M O N E L E M E NT S T O F R A M E B I G DA T A E T H I C S

(DA V I S & PA T T E R S O N , 2012, P . 3)


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2.2.2 IN N O VA T IO N R IS KS

The five thumb rules to understand the trade-off between risk and return of innovation

(Merton, 2013, p. 51):

Recognize that you need a model for making judgements about risk and return.

Acknowledge your models limitations

Expect the unexpected

Understand the use and the user

Check the infrastructure

In order to understand innovation risks it is essential that the terms Normal Profit and Economic

Rent be understood. (See Annex 12 to know more).

So, conceptually it is breakthrough and disruptive innovations that create economic rents whereas

incremental risks only generate normal profit. This justifies the reason why there has been a balance

between these kinds of innovations. Since, at the end of the day it is the competitive advantage that

is necessary, let us quickly switch to the Discussion section to understand the same.


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3.DISCUSSION

Having understood that innovative ideas can be created in the presence of macro and micro factors

i.e. beginning from Political, economic, technological to industry, competition be it incumbents or

new entrants, we will see how Big Data becomes the driving force behind innovation. Though IBMs

social media analytics framework speaks specifically about social media, it can be applied to any kind

of data analytics. The four steps are Assess, Segment, Relate and Discover.

Assess - the step where it is expected to simply listen to what consumers have to say without being

judgemental about the data quality or exact consumer purchasing behaviour. It is to simply know

what is going on, who is talking what and talking about whom etc. This step does not expect to take

any action yet.Segment - the step where the demographics, interests, geography, influencers, recommenders,

detractors etc. can be found out from the data and analytics. This is a very important step because it

will help figure out the cultural, generational, geographical differences and this is important to know

because this is will be the first step for marketers to reinvent their 4Ps which is more than Product,

Price, Place and Promotion but are Solution, Value, Access and Education (Ettenson, et al., 2013).

This is also called as the SAVE framework. As can be seen in the case of Mobile advertising, Gen Yers

from different countries had big differences in their opinions. In the case mobile usage, the usage

pattern was quite different among the consumers in the US and the UK.

Relate - The qualitative technique begins with Relate where the sentiments and emotions of the

customers should be recorded and judged. But again, the question goes back to how robust the

model can be to understand the correlation and causation effects.

Discover - is equally important from the above steps since at this step the Unknown Unknowns are

to be figured out. This step can become more effective with the use of Predictive Analytics and Risk

Management.

Analytics on mobile data requires two levels of data gathering i.e. PBLAMA (See Annex 5) and

Customer Engagement Behaviour (See Annex 6). Before bombarding customers with location based

advertising, it is important to understand the various influencing factors and then target the customer

accordingly.

The below data analytics framework can help set up an end to end process for data collection till

implementation of decision.


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F I G 3.A - A M A L G A M A T I O N O F REAN M O DE L A ND WE B A NA L Y T I C S P R O C E S S T O C R E A T E A

R O B US T W E B A NA L Y T I C S P R O C E S S

Even though processes are in place, companies still face issues in implementing Big Data analytics for

innovations. The reason behind this is the same Value Network as specified by Christensen. Thus, eventhough a company has enough resources and capabilities is the structure of the company which becomes

the barrier to successful innovation. Thus, let us now focus on the answers to the key objects specified in

the beginning of this report.

3.1 COULD/SHOULD NIELSEN VENTURE INTO BIG DATA ANALYSIS ON

BEHALF OF ITS CLIENTS?On that basis, the companys resources and capabilities are listed in the table below.


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Thus, the next important question that needs answering is Is there a new capability if merged with

in-house Big Data Analytics that can provide value to itself and the clients?

Based on the topics covered in the previous sections which talked about various macro and micro-

economic trends, sources of data, various analytics that can be applied on the data in order to

innovate and reduce the risks associated with data and innovation, it can be said that Nielsen has the

capabilities required by an FMCG client to operate competitively in this dynamic marketplace. But

there is one capability which is not very evident in the list of portfolios and that is the Risk

Management. Though managing risk is a part or a sub-department of every function in a company, it

is essential that a separate independent but flexible function be made focusing on risks and mitigating

the same. The reason behind proposing this is that the R factor can reduce the competitive

advantage value as the equation discussed above. This is the most simplistic way of justifying why

Risk Management is a necessity. Another important reasoning behind this is the re could be two

kinds of risks: Known Unknowns and Unknown Unknowns. If Nielsen which has this huge volume of

data, experienced/learned machines and tools and the huge exposure to suppliers and customers, it is

much more capable of finding the unknown unknowns for the clients. Secondly if Nielsen becomes

the interface between the data and the client then any negative impact will first affect Nielsen before

its clients get affected. This suggestion might sound horrendous but this is how Nielsen can prove the

increase in Economic Rent for its clients and gain the trust from them letting them handover the

analytics part of their strategy office. This would justify the R=G variable which is one of the pillar of

Innovation house of pillars suggested by Prahalad & Krishnan (See Annex 17). It means that

companies do not have to be necessarily vertically integrated and doing all the things. The only way

companies can operate nowadays is by outsourcing the capabilities which slows down their business

results and is not a part of the value proposition.

3.2 HOW CAN INVESTING IN BIG DATA ANALYTICS BOOST NIELSEN S

POSITION AS A GLOBAL MARKETING RESEARCH LEADER?As per honomichl top50 report of 2013 (See Annex 13), Nielsen holds position 1 in the market

research industry. In the United States, Nielsens metrics are an indicator of market share,

customer satisfaction, device share, service quality, revenue share, content audience and other

key performance measures. An important statement made on this report Its analytical services

are organized to follow clients business development processes. Suggestion here is to not have

the analytical services to follow but to lead or to initiate the business development process by

being an integral part of this process. It should be more proactive rather than reactive thereby

making its analytics capability more action-oriented. comScore Inc. has been intentionally added

to this list to show that they have organised their services on the basis of various analytics

services they provide.

The analytics and innovation should be driven by each other such that Risk Management is also

one of the gears in this central machine.


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F I G 3.2. A INNO V A T I O N C A P A B I L I T Y GE A R

Catching the momentum of Big Data to direct it towards the innovation capability gear will sustain its

position as a Global Marketing Research leader.

So, the final question is: how should Nielsen as an organisation function like in order to bring in the

Innovation capability Gear into action? For this I will be modifying Fig 1.1.A to make it more robust

structure by placing this gear at the centre of the organisation. This will be explained in the next

section.

F I G 3.2.B N I E L S E N O R G A NI S A T I O NA L C UL T UR E I NC L UDI NG INNO V A T I O N CA P A B I L I T Y GE A R


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3.3

WOULD INVESTMENT IN BIG DATA PROVIDE COMPETITIVE AND

ADAPTIVE ADVANTAGE TO ITS CLIENTS?Up till the evidences provided justify and prove that Big Data investment will definitely provide

Nielsens clients the competitive advantage which will help them redefine their 4Ps in order to create,

communicate and deliver to this customers. But it is about knowing or being prepared for the

unknown unknowns is what is going to provide adaptive advantage. But for that data fl ow between

the clients and Nielsen should be seamless. Not only that the data flow within each of these

organisations should also be seamless which can reduce the time for taking action as a result of

business insights from the analytic engine or more appropriately innovation capability gear. As rightly

said by Prof. Tobias Preis (interview call), not investing in Big Data will create strategic disadvantage

to the organisations.


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4.RECOMMENDATION AND IMPLEMENTATION

Form the Innovative Capability gear by building and including Risk Management portfolio in it along

with Innovation and Analytics

Nielsen Company already has an Innovation capability that includes The Cambridge Group, NM Incite

and Innovation Analytics. From this it can be seen that the two new buckets of marketing research

have already been included (Refer to Annex 15) such that it analyses data coming from various

sources and also capture and analyse unprompted customer feedback through the neuroscience

expertise. Hence out of the three capabilities in the Innovation capability gear, only Risk Management

is not included. Risk Management is one on the major barriers to commercialise Disruptive

Technology innovations. Also, as mentioned inAnnex 16 one of the critical capabilities that Need

Seekers and Technology Drivers should share is Technical Risk Assessment. Thus, for Nielsen to be

able to support its FMCG clients with insights to come up with Breakthrough and Disruptive

innovations, it is essential that a Risk Management Portfolio be built in this capability.

But while trying to incorporating Risk Management portfolio in the Innovation capability gear, there

would certain implementation barriers. Since the Innovation Analytics is a new capability is still

requires maturity including a Risk Management portfolio could slow down the development of the

innovation analytics as well as the short term goals set up by the company. But the benefits have the

potential to outweigh the issues because as per the R=G rule, if the Risk Assessment report along with

Customer Insights are provided to the Client, the client definitely gain speed in commercialising

innovative solutions.

The Analytics Engine should have a strategic focus on N=1 and R=G such that it functions as per

War-gaming optimising frameworkwith an analytical focus on causation and correlation

Focus of Analytics There should be three kinds of focuses of Analytics and those are Strategic,

Analytical and Operational in nature. The strategic focus should be on N=1 and R=G (See Annex 17),

the analytical focus should be on Causation and Correlation and the operational focus should be on

war-gaming optimising framework (Refer to Annex 8).When we say strategic focus should be on N=1,

for Nielsen it means that it can create personalised services for its FMCG clients based on their vision,

mission, business model, geographic locations they are operating from, business and marketing

strategies, their competitors and their target customers with the help of Big Data Analytics. With

strategic focus on R=G would mean that it is not necessary for Nielsen to be highly vertically

integrated by building all the capabilities. E.g. Information Technology resources which can support

the Analytics engine of Nielsen do not necessary need to be owned by the company and can be

outsourced to IT industry leaders.


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Moving on from strategic to analytical aspect of Analytics it is important that the whole nine yard

methodology be employed (Refer toAnnex 10). As per this methodology, the questions asked in the

analytical phase are What data do I need to Analyse and What data I have, what do I miss, what is

relevant. At the very point correlation and causation should be introduced. While correlation

definitely suggests an occurrence of event when another event occurs, it is causation that strongly

brings up the factors which cause the event to happen. The event could be e.g. personalised social

media advertising to customers and correlations will still not provide the necessary insights thereby

reducing the return on investment on marketing efforts by a client. As a result the machine learning

of the analytic engine will be richer with better insights. This triggers the need for various qualitative

research especially ethnographic, sentiment analysis, emotion mining, brain imaging, eye tracking,

and heat maps etc.

Finally talking about the operational aspect of the Analytics Engine, the questions that need to be

asked are Which actions to take, with which customers and for what products? In order to be able

to answer this question the War-Gaming optimising framework should be applied. But since the

customer responses and the business outcomes mentioned in this methodology cannot be gained

independently by Nielsen, the client has to be brought in, in this process for which the below

recommendation is important that it is implemented.

Make your organisational structure (Refer to Fig 3.2.b) in order to become innovative and global

leader in the marketing research industry

For the analytics engine to have effective strategic focus, it should operate effectively. In order to be

able to operate effectively it will need to bring in the customers and the clients into its structure

which will also help eradicate the issues related to silo approach while being able to Attribute,

Optimise and Allocate (Refer to Annex 8). Also as suggested McCracken (2006) focusing on the

culture and the consumers can make an organisation innovative as well as competitive because it

forces an organisation to repeatedly keep asking What business are we in? Thus, it can be said that

the suggested organisational structure aligns with that of a leading organisation as per the

Customer centric maturity model shown in Annex 11. But as simple it is to recommend restructuring

an organisation, it is equally difficult to make it happen. Barriers to this are innumerable which would

require hiring change managers on a contract basis who could put a one-time effort to restructure

without disrupting the daily activities.


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Create a Chief Data Officer position in the company who can be responsible of bringing the CMO

and the CIO together at a strategic level as well as holding the ownership of Data

Due to the heavy ethical issues related to Big Data, it is important to create a CDO position. Even

though managing data should be a responsibility of each function but with the increasing

responsibilities on the CIO and the CTO offices, it has become essential that such a role be created. As

mentioned in Annex 14, one of the drivers behind creating a CDO role is Strategic Importance of Big

Data. Nielsen would be pioneering in its industry if it employs a CDO. This is a very new role and has

recently caught a lot of attention but it needs to go a long way over the life cycle of a job position for

it mature fully and organisations reap the necessary benefits out of this role. The barrier would be

less at the implementation level and more at the strategic level such that the executives and directors

need to be convinced with its prospective competitive advantage.


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Annex 1 Dimensions of Big Data (IBM,2012)

ANNEX 2 APAC Region Market Distribution methods Product wise in the year

2011/12

0

10

20

30

40

50

60

70

MarketDistribution(%Shareperchanne

l)

Product Type

Market Distribution versus Product Type in APAC region

Super/ hypermarkets

Independent

Retailers

Specialist Retailers

On-Trade

Convenience Stores

Service stations


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ANNEX 3 EMEA Region Market Distribution methods Product wise in the year

2011/12

Annex 4 APAC Region Market Distribution methods Product wise in the year

2011/12

0

10

20

30

40

50

60

7080

AlcoholicDrinks

BathandShower

Beer

CannedFood

ChilledandDeli

Dairy

FacialCare

Fragrances

FunctionalDrinks

HotDrinks

Juices

MaleToiletries

OralHygiene

PersonalProducts

PotatoChips

SkinCare

SunCare

Wine

MarketDistribution(%Shareperchanne

l)

Product Type

Market Distribution versus Product Type in EMEA RegionSuper/

hypermarkets

Independent

Retailers


On-Trade

Convenience Stores

Service stations

Department Stores

(incl. Duty-Free

Shops)

0

10

20

30

40

50

60

70

80

90

100

MarketDistribution(%Shareperchannel)

Product Type

Market Distribution versus Product Type in AMER Region

Super/

hypermarkets

Independent

Retailers


On-Trade

Convenience

Stores

Service stations

Department Stores

(incl. Duty-Free

Shops)


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ANNEX 5 Conceptual model depicting influencing factors for PBLAMA (R ICHARD &

MEULI,2013,P .705)

1

1PBC Perceived Behavioural Control is an individual perception of the ease or

difficulty of performing a specific task or action (Ajzen, 1991).


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A N N E X 6 C onc eptual Model of C ustomer Eng ag ement B ehaviour (D OORN,ET AL., 2010)

A N N E X 7 IBM Social Media Analytics helps organisations act upon social media

insig hts to solve business problem (IBMB,N .D.)


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Annex 8 Framework for optimising (War-Gaming2) Ad vertising (Nichols , 2013, p.

63)

Annex 9 Four Factors in REAN Model ( Jac kson, 2009, p . 26)

2War-Gaming is a process in which team members define marketing goals (such as

a certain revenue target, share goal, or margin goal), often across multiple productsand markets. Optimization software can then be used to crunch the data so as to

answer as many what-if business scenarios(Nichols, 2013, p. 66).


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Annex 10 T he whole nine-yard methodolog y(TE E R L I N K, 2011)

A N N E X 11 Customer-Centric Maturity Model (Ernst & Young, 2013, p. 8)


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A N N E X 12 The three effects on a firm due to breakthrough and incremental

innovations (Soresc u & Spanjol , 2008, p . 114)

Impact Description

Normal Profit The minimum compensation that investors require to

purchase stock in a company. They equal the interest

rate investors could earn in a treasury bond plus an

additional risk premium (Marshall 1920). When a

project generates only normal profits, its net present

value (NPV) is always zero, implying that investors

earn no more than what is a fair compensation for

risk.

Economic Rent Economic rents are profits earned above those

required as compensation for risk and time value of

money. Determinants of economic rents are muchmore valuable, albeit elusive, than those of normal

profits; uncovering these determinants is crucial to

the understanding of the true financial value of a

corporate strategic action.

Total Firm Risk If the risk to pursue a certain innovation can

compensate to gain above normal rate of return, then

shareholders may welcome the project. However the

increase in risk may endanger the firms survival

prospects and have negative consequences for

management, employees, and other stakeholders.

A N N E X 13 Top 6 Market Research companies (H O NO M I C H L , 2013)

Name of

the

Company

Nielsen

Holdings N.V.

Ipsos Information

Resources Inc.

GfK The NPD

Group Inc.

comScore

Inc.

Revenue

(2012)

$2,651.0

million

$590.0

million

$478.7 million $330.9 million $191.8

million

$183.4

million

Change

from 2011

+4.0% -5.5% +2.9% +0.5% +1.5% +5.1%

NonU.S.

Revenue

$2,778.0

million

$1,710.0

million

$285.1 Million $1,616.0

Million

$80.2 Million $71.8

Million

From

Outside

U.S.

51.2% 74.3% 37.3% 83% 29.5% 28.1%

Research

Business

Area

Nielsen aligns

its research

business into

two

segments:

Consumer

Watch (media

audience

measurement

and analytics)

and

It provides

services in six

areas of

specialization:

advertising,

customer

loyalty,

marketing,

media, public

affairs

research and

IRI operates

under two

interconnected

business

segments: IRI

Market

Measurement:

Market

Measurement

provides

CPG/FMCG

These services

are delivered in

the following

practice areas

and services:

Market insights

and growth

opportunities,

Product design

and

optimization,

NPD offers

retail,

consumer

and

distributor

tracking.

Retail

tracking

services were

enhanced

and new

cS services

fall into

four

primary

segments

of digital

analytics:

Audience

Analytics,

Advertising

Analytics,


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Consumer

Buy

(consumer

purchasing

measurement

and

analytics).

survey

management.

and retail

markets

tracking

information

and insights via

access and

visualization

tools. Industry

Vertical

Solutions: This

business

segment offers

vertical

experience and

customized

products and

services for

multiple

industryverticals,

including

healthcare and

wellness,

center store,

confections,

beer, wine,

spirits,

tobacco,

bakery

and dairy.

Brand value

optimization,

Advertising and

communication

optimization,

Channel and

category

optimization,

Price

optimization,

Customer

experience and

loyalty

management

account

level reports

were

introduced in

2012

following the

addition

of Wal-Mart

and other

retailers. The

firm

continues to

expand its

portfolio of

analytic

services to

address

specific

businessneeds,

including

category

management,

forecasting,

price

evaluation,

market

evaluation

and testing.

Mobile

Operator

Analytics,

Digital

Business

Analytics,

A N N E X 14 A FR A M E W O R K F O R T H E CDO C O N S T R U C T (L E E , E T A L . , 2012)


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ANNEX 15 T he four buc kets of data for marketing researc h

( M I C U , E T A L . , 2011, P .

215)


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ANNEX 16 Critical Innovation Capabilities

(booz&c o b, 2012)

ANNEX 17 Business Insights (C.K.P RAHALAD & M.S.K RISHNAN , 2008, P . 85)


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Documents

Sarit Prajna Sahu_Management Report to The Nielsen Company