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The presentation discusses the concepts, principles and significance of data driven marketing.
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> Short but sharp history
§ Datalicious was founded late 2007 § Strong Omniture web analy-cs history § Now 360 data agency with specialist team § Combina-on of analysts and developers § Carefully selected best of breed partners § Driving industry best prac-ce (ADMA) § Turning data into ac-onable insights § Execu-ng smart data driven campaigns
June 2010 © Datalicious Pty Ltd 2
> Smart data driven marke(ng
June 2010 © Datalicious Pty Ltd 3
Media A;ribu(on & Modeling
Op(mise channel mix, predict sales
Tes(ng & Op(misa(on Remove barriers, drive sales
Boos(ng ROI
Targeted Direct Marke(ng Increase relevance, reduce churn
“Using data to widen the funnel”
> Wide range of data services
June 2010 © Datalicious Pty Ltd 4
Data PlaIorms Data collec(on and processing Web analy(cs solu(ons Omniture, Google Analy(cs, etc Tag-‐less online data capture End-‐to-‐end data plaIorms IVR and call center repor(ng Single customer view
Insights Analy(cs Data mining and modelling Customised dashboards Tableau, SpoIire, SPSS, etc Media a;ribu(on models Market and compe(tor trends Social media monitoring Customer profiling
Ac(on Campaigns Data usage and applica(on Marke(ng automa(on Alterian, SiteCore, Inxmail, etc Targe(ng and merchandising Internal search op(misa(on CRM strategy and execu(on Tes(ng programs
> Today
§ Capturing data – Op-ons, limita-ons, innova-ons
§ Genera-ng insights – Process, metrics, examples
§ Taking ac-on – Media, targe-ng, tes-ng
June 2010 © Datalicious Pty Ltd 6
Marke(ng
Mix
Product
Price
Place
Promo(on
Physical Evidence
People
Process
Partners
June 2010 © Datalicious Pty Ltd 12
> Capturing data
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
June 2010 © Datalicious Pty Ltd 13
> Digital data is plen(ful and cheap
June 2010 © Datalicious Pty Ltd 14
Source: Omniture Summit, MaV Belkin, 2007
> Digital metric categories
June 2010 © Datalicious Pty Ltd 15
Source: Accuracy Whitepaper for web analy-cs, Brian CliYon, 2008
+Social
> What plaIorm to use
June 2010 © Datalicious Pty Ltd 16
Time, Control
Soph
is-ca-o
n
Stage 1: Data Stage 2: Insights Stage 3: Ac(on
Third par-es control most data, ad hoc repor-ng only, i.e. what happened?
Data is being brought in-‐house, shiY towards insights genera-on and data mining, i.e. why did it happen?
Data is fully owned in-‐house, advanced predic-ve modelling and trigger based marke-ng, i.e. what will happen and making it happen!
> Governance and data integrity
June 2010 © Datalicious Pty Ltd 17
Source: Omniture Summit, MaV Belkin, 2007
> Tag-‐less data capture
June 2010 © Datalicious Pty Ltd 18
Google: “atomic labs” www.atomiclabs.com
> Google data in Australia
June 2010 © Datalicious Pty Ltd 19
Source: hVp://www.hitwise.com/au/resources/data-‐centre
> Search at all stages
June 2010 © Datalicious Pty Ltd 20
Source: Inside the Mind of the Searcher, Enquiro 2004
> Unique phone numbers
§ 1 unique phone number – Phone number is considered part of the brand – Media origin of calls cannot be established – Added value of website interac-on unknown
§ 2-‐10 unique phone numbers – Different numbers for different media channels – Exclusive number(s) reserved for website use – Call origin data more granular but not perfect – Difficult to rotate and pause numbers
June 2010 © Datalicious Pty Ltd 24
> Unique phone numbers § 10+ unique phone numbers – Different numbers for different media channels – Different numbers for different product categories – Different numbers for different conversion steps – Call origin becoming useful to shape call script – Feasible to pause numbers to improve integrity
§ 100+ unique phone numbers – Different numbers for different website visitors – Call origin and -me stamp enable individual match – Call conversions matched back to search terms
June 2010 © Datalicious Pty Ltd 25
> Bad experience: 67% hang up
June 2010 © Datalicious Pty Ltd 27
2/3 of callers hang up the phone as they cannot get what they want fast enough.
> Poten(al calls to ac(on § Unique click-‐through URLs § Unique vanity domains or URLs § Unique phone numbers § Unique search terms § Unique email addresses § Unique personal URLs (PURLs) § Unique SMS numbers, QR codes § Unique promo-onal codes, vouchers § Geographic loca-on (Facebook, FourSquare) § Plus regression analysis of cause and effect
June 2010 © Datalicious Pty Ltd 28
Calls to ac(on can help shape the customer experience not just evaluate responses
> Cookie based tracking process
June 2010 © Datalicious Pty Ltd 29
Source: Google Analy-cs, Jus-n Cutroni, 2007
What if: Someone deletes their cookies? Or uses a device that does not support JavaScript? Or uses two computers (work vs. home)? Or two people use the same computer?
> Duplica(on across channels
June 2010 © Datalicious Pty Ltd 30
Banner Ads
Email Blast
Paid Search
Organic Search
$ Bid Mgmt
Ad Server
Email PlaIorm
Google Analy(cs
$
$
$
Central Analy(cs PlaIorm
$
$
$
> De-‐duplica(on across channels
June 2010 © Datalicious Pty Ltd 31
Banner Ads
Email Blast
Paid Search
Organic Search
$
> Datalicious SuperTag
June 2010 © Datalicious Pty Ltd 32
Ad Sever, Paid Search SuperTag Web
Analy-cs
Use the same business rules to trigger conversions across all plaIorms to reduce discrepancies
The study examined data from two of the UK’s busiest ecommerce websites, ASDA and William Hill. Given that more than half of all page impressions on these sites are from logged-‐in users, they provided a robust sample to compare IP-‐based and cookie-‐based analysis against. The results were staggering, for example an IP-‐based approach overes-mated visitors by up to 7.6 -mes whilst a cookie-‐based approach overes(mated visitors by up to 2.3 (mes.
> Unique visitor overes(ma(on
June 2010 © Datalicious Pty Ltd 33
Source: White Paper, RedEye, 2007
> Maximise iden(fica(on points
20%
40%
60%
80%
100%
120%
140%
160%
0 4 8 12 16 20 24 28 32 36 40 44 48
Weeks
−−− Probability of iden-fica-on through Cookies
June 2010 34 © Datalicious Pty Ltd
> Customer profiling in ac(on
June 2010 © Datalicious Pty Ltd 35
Using website and email responses to learn a liVle bite more about
subscribers at every touch point to keep
refining profiles and messages.
> Online form best prac(ce
June 2010 © Datalicious Pty Ltd 36
Maximise data integrity Age vs. year of birth Free text vs. op-ons
Use auto-‐complete wherever possible
> Research online, shop offline
June 2010 © Datalicious Pty Ltd 37
Source: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School
> Offline sales driven by online
June 2010 © Datalicious Pty Ltd 38
Website research
Phone order
Retail order
Online order
Cookie
Adver(sing campaign
Credit check, fulfilment
Online order confirma(on
Virtual order confirma(on
Confirma(on email
> Summary: Capturing data
§ Plenty of data sources and planorms § Especially search is great free data source § Maintaining data integrity takes effort § Cookie technology has its limita-ons § New tag-‐less technologies emerging § Maximise iden-fica-on points § Offline can be -ed to online
June 2010 © Datalicious Pty Ltd 39
> Genera(ng insights
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
June 2010 © Datalicious Pty Ltd 40
> Corporate data journey
June 2010 © Datalicious Pty Ltd 41
Time, Control
Soph
is-ca-o
n
Stage 1
Data Stage 2
Insights Stage 3 Ac(on
Third par-es control most data, ad hoc repor-ng only, i.e. what happened?
Data is being brought in-‐house, shiY towards insights genera-on and data mining, i.e. why did it happen?
Data is fully owned in-‐house, advanced predic-ve modelling and trigger based marke-ng, i.e. what will happen and making it happen!
“Followers”
“Leaders”
“Laggards”
> Process is key to success
June 2010 © Datalicious Pty Ltd 42
Source: Omniture Summit, MaV Belkin, 2007
Awareness Interest Desire Ac(on Sa(sfac(on
> AIDA and AIDAS formulas
June 2010 © Datalicious Pty Ltd 43
Social media
New media
Old media
Reach (Awareness)
Engagement (Interest & Desire)
Conversion (Ac-on)
+Buzz (Sa-sfac-on)
> Simplified AIDAS funnel
June 2010 © Datalicious Pty Ltd 44
People reached
People engaged
People converted
People delighted
> Marke(ng is about people
June 2010 © Datalicious Pty Ltd 45
40% 10% 1%
People reached
People engaged
People converted
People delighted
> Addi(onal funnel breakdowns
June 2010 © Datalicious Pty Ltd 46
40% 10% 1%
New prospects vs. exis-ng customers
Brand vs. direct response campaign
> Poten(al funnel breakdowns § Brand vs. direct response campaign § New prospects vs. exis-ng customers § Baseline vs. incremental conversions § Compe--ve ac-vity, i.e. none, a lot, etc § Segments, i.e. age, loca-on, influence, etc § Channels, i.e. search, display, social, etc § Campaigns, i.e. this/last week, month, year, etc § Products and brands, i.e. iphone, htc, etc § Offers, i.e. free minutes, free handset, etc § Devices, i.e. home, office, mobile, tablet, etc June 2010 © Datalicious Pty Ltd 49
> Conversion funnel 1.0
June 2010
Conversion funnel Product page, add to shopping cart, view shopping cart, cart checkout, payment details, shipping informa-on, order confirma-on, etc
Conversion event
Campaign responses
© Datalicious Pty Ltd 50
> Conversion funnel 2.0
June 2010
Campaign responses (inbound spokes) Offline campaigns, banner ads, email marke-ng, referrals, organic search, paid search, internal promo-ons, etc
Landing page (hub)
Success events (outbound spokes) Bounce rate, add to cart, cart checkout, confirmed order, call back request, registra-on, product comparison, product review, forward to friend, etc
© Datalicious Pty Ltd 51
> Addi(onal success metrics
June 2010 © Datalicious Pty Ltd 52
Click Through
Add To Cart
Click Through
Bounce Rate
Click Through $
Click Through
Call back requests
Store Searches > ... $
$
$ Cart Checkout
Pages Per Visit
?
Avg Cart Value
How many survey responses do you need if you have 10,000 customers?
How many email opens do you need to test 2 subject lines if your subscriber base is 50,000?
How many orders do you need to test 6 banner execu(ons if you serve 1,000,000 banners
Google “nss sample size calculator” June 2010 © Datalicious Pty Ltd 54
How many survey responses do you need if you have 10,000 customers?
369 for each ques(on or 369 complete responses
How many email opens do you need to test 2 subject lines if your subscriber base is 50,000? And email sends? 381 per subject line or 381 x 2 = 762 email opens
How many orders do you need to test 6 banner execu(ons if you serve 1,000,000 banners?
383 sales per banner execu(on or 383 x 6 = 2,298 sales
Google “nss sample size calculator” June 2010 © Datalicious Pty Ltd 55
Level Reach Engagement Conversion +Buzz
Level 1, people
Level 2, strategic
Level 3, tac(cal
Funnel breakdowns
> Exercise: Metrics framework
June 2010 © Datalicious Pty Ltd 57
Level Reach Engagement Conversion +Buzz
Level 1, people
People reached
People engaged
People converted
People delighted
Level 2, strategic
Display impressions ? ? ?
Level 3, tac(cal
Interac(on rate, etc ? ? ?
Funnel breakdowns Exis(ng customers vs. new prospects, products, etc
> Exercise: Metrics framework
June 2010 © Datalicious Pty Ltd 58
> Establishing a baseline
June 2010 © Datalicious Pty Ltd 59
Switch all adver-sing off for a period of -me (unlikely) or establish a smaller control group that is representa-ve of the en-re popula-on (i.e. search term, geography, etc) and switch off selected channels one at a -me to minimise impact on overall conversions.
Campaign response data
> Combining data sources
June 2010 © Datalicious Pty Ltd 60
Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
> Transac(ons plus behaviours
June 2010 © Datalicious Pty Ltd 61
+ one-‐off collec-on of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira(on, etc predic-ve models based on data mining
propensity to buy, churn, etc historical data from previous transac-ons
average order value, points, etc
CRM Profile
Updated Occasionally
tracking of purchase funnel stage
browsing, checkout, etc tracking of content preferences
products, brands, features, etc tracking of external campaign responses
search terms, referrers, etc tracking of internal promo-on responses
emails, internal search, etc
Site Behaviour
Updated Con(nuously
Geo-‐demographic data
> Enhancing data sources
June 2010 © Datalicious Pty Ltd 63
3rd party data
+ The whole is greater than the sum of its parts
Customer profile data
> Hitwise Mosaic segment swing
australia.com vs. newzealand.com australia.com vs. bulafiji.com
June 2010 © Datalicious Pty Ltd 66
Source: Hitwise, 2006
> Importance of calendar events
June 2010 © Datalicious Pty Ltd 74
Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless
> Summary: Genera(ng insights
§ Right resources and processes are key § Define a standardised metrics framework § Maintain framework to enable comparison § Combine data sets for hidden insights § Establish a single (data) source of truth § Think outside the box and across channels § Data does not equal significance
June 2010 © Datalicious Pty Ltd 75
> Taking ac(on
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
June 2010 © Datalicious Pty Ltd 76
> Smart data driven marke(ng
June 2010 © Datalicious Pty Ltd 77
Media A;ribu(on & Modeling
Op(mise channel mix, predict sales
Tes(ng & Op(misa(on Remove barriers, drive sales
Boos(ng ROI
Targeted Direct Marke(ng Increase relevance, reduce churn
“Using data to widen the funnel”
Direct mail, email, etc
Facebook Twi;er, etc
> Campaign flow and calls to ac(on
June 2010 © Datalicious Pty Ltd 78
POS kiosks, loyalty cards, etc
CRM program
Home pages, portals, etc
YouTube, blog, etc
Paid search
Organic search
Landing pages, offers, etc
PR, WOM, events, etc
TV, print, radio, etc
C2
C3
= Paid media
= Viral elements
Call center, retail stores, etc
= Coupons, surveys
Display ads, affiliates, etc
C1
> Success a;ribu(on models
June 2010 © Datalicious Pty Ltd 79
Banner Ad $100
Email Blast
Paid Search $100
Banner Ad $100
Affiliate Referral $100
Success $100
Success $100
Banner Ad
Paid Search
Organic Search $100
Success $100
Last channel gets all credit
First channel gets all credit
All channels get equal credit
Print Ad $33
Social Media $33
Paid Search $33
Success $100
All channels get par(al credit
Paid Search
> First and last click a;ribu(on
June 2010 © Datalicious Pty Ltd 80
Chart shows percentage of channel touch points that lead to a conversion.
Neither first nor last-‐click measurement would provide true picture
Paid/Organic Search
Emails/Shopping Engines
Closer
SEM Generic
Banner View
TV Ad
> Full path to purchase
June 2010 © Datalicious Pty Ltd 81
Influencer Influencer $
Banner Click Online
SEO Generic
Affiliate Click Offline
SEO Branded
Direct Visit
Email Update Abandon
Direct Visit
Social Media
SEO Branded
Introducer
The right message Via the right channel To the right person At the right -me
Targe(ng
June 2010 © Datalicious Pty Ltd 85
Capture internet traffic Capture 50-‐100% of fair market share of traffic
Increase consumer engagement Exceed 50% of best compe-tor’s engagement rate
Capture qualified leads and sell Convert 10-‐15% to leads and of that 20% to sales
Building consumer loyalty Build 60% loyalty rate and 40% sales conversion
Increase online revenue Earn 10-‐20% incremental revenue online
> Increase revenue by 10-‐20%
June 2010 © Datalicious Pty Ltd 86
> New consumer decision journey
June 2010 © Datalicious Pty Ltd 87
The consumer decision process is changing from linear to circular.
> New consumer decision journey
June 2010 © Datalicious Pty Ltd 88
The consumer decision process is changing from linear to circular.
Change increases the importance of experience during research phase.
Online research
> Coordina(on across channels
June 2010 © Datalicious Pty Ltd 90
Off-‐site targe(ng
On-‐site targe(ng
Profile targe(ng
Genera(ng awareness
Crea(ng engagement
Maximising revenue
TV, radio, print, outdoor, search marke-ng, display ads, performance networks, affiliates, social media, etc
Retail stores, in-‐store kiosks, call centers, brochures, websites, mobile apps, online chat, social media, etc
Outbound calls, direct mail, emails, social media, SMS, mobile apps, etc
Off-‐site targe-ng
On-‐site targe-ng
Profile targe-ng
> Combining targe(ng plaIorms
June 2010 © Datalicious Pty Ltd 91
On-‐site segments
Off-‐site segments
> Combining technology
June 2010 © Datalicious Pty Ltd 95
CRM
> Datalicious SuperTag
June 2010 © Datalicious Pty Ltd 96
§ One tag for all sites and planorms § Hosted internally or externally § Fast tag implementa-on/updates § Eliminates JavaScript caching § Enables code tes-ng on live site § Enables heat map implementa-on § Enables redirects for A/B tes-ng § Enables network wide re-‐targe-ng § Enables live chat implementa-on § Plus mul--‐channel media aVribu-on
> Affinity re-‐targe(ng in ac(on
June 2010 © Datalicious Pty Ltd 97
Different type of visitors respond to different ads. By using category affinity targe-ng, response rates are liYed significantly across products.
Message CTR By Category Affinity
Postpay Prepay Broadb. Business
Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - +
Google: “vodafone omniture case study” or h;p://bit.ly/de70b7
> Ad-‐sequencing in ac(on
June 2010 © Datalicious Pty Ltd 98
Marke-ng is about telling stories and
stories are not sta-c but evolve over -me
Ad-‐sequencing can help to evolve stories over -me the more users engage with ads
> Sample site visitor composi(on
June 2010 © Datalicious Pty Ltd 99
30% exis(ng customers with extensive profile including transac-onal history of which maybe 50% can actually be iden-fied as individuals
30% new visitors with no previous website history aside from campaign or referrer data of which maybe 50% is useful
10% serious prospects with limited profile data
30% repeat visitors with referral data and some website history allowing 50% to be segmented by content affinity
Purchase Cycle
Segments: Colour, price, product affinity, etc
Media Channels
Data Points
Default, awareness
Research, considera(on
Purchase intent
Reten(on, up/cross-‐sell
> Exercise: Targe(ng matrix
June 2010 © Datalicious Pty Ltd 101
Purchase Cycle
Segments: Colour, price, product affinity, etc
Media Channels
Data Points
Default, awareness
Have you seen A?
Have you seen B?
Display, search, etc Default
Research, considera(on
A has great features!
B has great features!
Search, website, etc
Ad clicks, prod views
Purchase intent
A delivers great value!
B delivers great value!
Website, emails, etc
Cart adds, checkouts
Reten(on, up/cross-‐sell
Why not buy B?
Why not buy A?
Direct mails, emails, etc
Email clicks, logins, etc
> Exercise: Targe(ng matrix
June 2010 © Datalicious Pty Ltd 102
> Quality content is key
Avinash Kaushik: “The principle of garbage in, garbage out applies here. [… what makes a behaviour
targe;ng pla<orm ;ck, and produce results, is not its intelligence, it is your ability to actually feed it the right content which it can then target […. You feed your BT system crap and it will quickly and efficiently target crap to your
customers. Faster then you could ever have yourself.”
June 2010 © Datalicious Pty Ltd 103
Test Segment Content KPIs Poten(al Results
Test #1A New prospects
Conversion form A
Next step, order, etc ? ?
Test #1B New prospects
Conversion form B
Next step, order, etc ? ?
Test #1N New prospects
Conversion form N
Next step, order, etc ? ?
? ? ? ? ? ?
> Developing a tes(ng matrix
June 2010 © Datalicious Pty Ltd 105
> Summary
§ There is no magic formula for ROI § Focus on the en-re conversion funnel § Media aVribu-on is hard but necessary § Neither first nor last click method works § Create a coordinated targeted experience § Content is always king no maVer what § Test, learn and refine con-nuously
June 2010 © Datalicious Pty Ltd 106
June 2010 © Datalicious Pty Ltd 108
Contact me [email protected]
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