Smart Data Driven Marketing Metrics Framework
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The presentation discusses the concepts, principles and significance of data driven marketing.
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Smart Data Driven Marketing Metrics Framework
- 1. > SuperIQ Analy/cs < Smart data driven marke-ng
- 2. > Smart data driven marke/ng “Using data to widen the funnel” Media A;ribu/on & Modeling Op/mise channel mix, predict sales Targeted Direct Marke/ng Increase relevance, reduce churn Tes/ng & Op/misa/on Remove barriers, drive sales Boos/ng ROI June 2010 © Datalicious Pty Ltd 2
- 3. Clive Humby: Data is the new oil June 2010 © Datalicious Pty Ltd 3
- 4. Oil and data come at a price June 2010 © Datalicious Pty Ltd 4
- 5. > Google Ngram: Privacy June 2010 © Datalicious Pty Ltd 5
- 6. Collec/ng data for the sake of it or to add value to customers? June 2010 © Datalicious Pty Ltd 6
- 7. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > Metrics framework June 2010 © Datalicious Pty Ltd 7
- 8. > AIDA and AIDAS formulas Old media New media Awareness Interest Desire Ac/on Sa/sfac/on Social media June 2010 © Datalicious Pty Ltd 8
- 9. > Simplified AIDAS funnel Reach Engagement Conversion +Buzz (Awareness) (Interest & Desire) (Ac-on) (Sa-sfac-on) June 2010 © Datalicious Pty Ltd 9
- 10. > Marke/ng is about people People People People People reached 40% engaged 10% converted 1% delighted June 2010 © Datalicious Pty Ltd 10
- 11. > Addi/onal funnel breakdowns Brand vs. direct response campaign People People People People reached 40% engaged 10% converted 1% delighted New prospects vs. exis-ng customers June 2010 © Datalicious Pty Ltd 11
- 12. New vs. returning visitors June 2010 © Datalicious Pty Ltd 12
- 13. AU/NZ vs. rest of world June 2010 © Datalicious Pty Ltd 13
- 14. > 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 14
- 15. Exercise: Metrics framework June 2010 © Datalicious Pty Ltd 15
- 16. > Exercise: Metrics framework Level Reach Engagement Conversion +Buzz Level 1, people Level 2, strategic Level 3, tac/cal Funnel breakdowns June 2010 © Datalicious Pty Ltd 16
- 17. > Exercise: Metrics framework Level Reach Engagement Conversion +Buzz Level 1, People People People People people reached engaged converted delighted Level 2, Display strategic impressions ? ? ? Level 3, Interac/on tac/cal rate, etc ? ? ? Funnel Exis/ng customers vs. new prospects, products, etc breakdowns June 2010 © Datalicious Pty Ltd 17
- 18. > Establishing a baseline 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. June 2010 © Datalicious Pty Ltd 18
- 19. > Importance of calendar events Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless June 2010 © Datalicious Pty Ltd 19
- 20. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > Media a;ribu/on June 2010 © Datalicious Pty Ltd 20
- 21. > Campaign flows are complex = Paid media Organic PR, WOM, search events, etc = Viral elements = Sales channels YouTube, Home pages, Paid TV, print, blog, etc portals, etc search radio, etc Direct mail, Landing pages, Display ads, email, etc offers, etc affiliates, etc CRM Facebook program Twi;er, etc POS kiosks, Call center, loyalty cards, etc retail stores, etc June 2011 © Datalicious Pty Ltd 21
- 22. > Media channels overlap TV/Print audience Banner Search audience audience June 2011 © Datalicious Pty Ltd 22
- 23. > Indirect display impact June 2011 © Datalicious Pty Ltd 23
- 24. > Indirect display impact June 2011 © Datalicious Pty Ltd 24
- 25. > Success a;ribu/on models Banner Paid Organic Success Last channel Search Ad Search $100 $100 gets all credit Banner Paid Email Success First channel Ad $100 Search Blast $100 gets all credit Paid Banner Affiliate Success All channels get Search Ad Referral $100 $100 $100 $100 equal credit Print Social Paid Success All channels get Ad Media Search $33 $33 $33 $100 par/al credit June 2011 © Datalicious Pty Ltd 25
- 26. > First and last click a;ribu/on Chart shows percentage of channel touch points that lead Paid/Organic Search to a conversion. Neither first Emails/Shopping Engines nor last-‐click measurement would provide true picture June 2011 © Datalicious Pty Ltd 26
- 27. > Full path to purchase Introducer Influencer Influencer Closer $ SEM Banner Direct SEO Online Generic Click Visit Branded Banner SEO Affiliate Social Offline View Generic Click Media TV SEO Direct Email Abandon Ad Branded Visit Update June 2011 © Datalicious Pty Ltd 27
- 28. > Search call to ac/on for offline June 2011 © Datalicious Pty Ltd 28
- 30. > PURLs boos/ng DM response rates Text June 2011 © Datalicious Pty Ltd 30
- 31. > Offline sales driven by online Adver/sing Phone Credit check, campaign order fulfilment Retail Confirma/on order email Website Online Online order Virtual order research order confirma/on confirma/on Cookie June 2011 © Datalicious Pty Ltd 31
- 32. > Understanding channel mix June 2011 © Datalicious Pty Ltd 32
- 34. > Website entry survey De-‐duped Campaign Report Greatest Influencer on Branded Search / STS } Channel % of Conversions Channel % of Influence Straight to Site 27% Word of Mouth 32% SEO Branded 15% Blogging & Social Media 24% SEM Branded 9% Newspaper Adver-sing 9% SEO Generic 7% Display Adver-sing 14% SEM Generic 14% Email Marke-ng 7% Display Adver-sing 7% Retail Promo-ons 14% Affiliate Marke-ng 9% Referrals 5% Conversions aeributed to search terms Email Marke-ng 7% that contain brand keywords and direct website visits are most likely not the origina-ng channel that generated the awareness and as such conversion credits should be re-‐allocated. June 2011 © Datalicious Pty Ltd 34
- 35. > Adjus/ng for offline impact -‐5 -‐15 -‐10 +5 +15 +10 June 2011 © Datalicious Pty Ltd 35
- 36. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > Smart targe/ng June 2010 © Datalicious Pty Ltd 36
- 37. Targe/ng The right message Via the right channel To the right person At the right -me June 2010 © Datalicious Pty Ltd 37
- 38. > Increase revenue by 10-‐20% 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 June 2010 © Datalicious Pty Ltd 38
- 39. > New consumer decision journey The consumer decision process is changing from linear to circular. June 2010 © Datalicious Pty Ltd 39
- 40. > New consumer decision journey The consumer decision process is changing from linear to circular. Online research Change increases the importance of experience during research phase. June 2010 © Datalicious Pty Ltd 40
- 41. June 2010 © Datalicious Pty Ltd 41
- 42. > Coordina/on across channels Genera/ng Crea/ng Maximising awareness engagement revenue TV, radio, print, Retail stores, in-‐store Outbound calls, direct outdoor, search kiosks, call centers, mail, emails, social marke-ng, display brochures, websites, media, SMS, mobile ads, performance mobile apps, online apps, etc networks, affiliates, chat, social media, etc social media, etc Off-‐site On-‐site Profile targe/ng targe/ng targe/ng June 2010 © Datalicious Pty Ltd 42
- 43. > Integra/ng targe/ng planorms Off-‐site targe-ng Profile On-‐site targe-ng targe-ng June 2010 © Datalicious Pty Ltd 43
- 44. > Combining data sources Website behavioural data Campaign response data + The whole is greater than the sum of its parts Customer profile data June 2010 © Datalicious Pty Ltd 44
- 45. > Transac/ons plus behaviours CRM Profile Site Behaviour one-‐off collec-on of demographical data tracking of purchase funnel stage + age, gender, address, etc browsing, checkout, etc customer lifecycle metrics and key dates tracking of content preferences profitability, expira/on, etc products, brands, features, etc predic-ve models based on data mining tracking of external campaign responses propensity to buy, churn, etc search terms, referrers, etc historical data from previous transac-ons tracking of internal promo-on responses average order value, points, etc emails, internal search, etc Updated Occasionally Updated Con/nuously June 2010 © Datalicious Pty Ltd 45
- 46. > Sample customer level data June 2010 © Datalicious Pty Ltd 46
- 47. > Unique visitor overes/ma/on 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. June 2010 © Datalicious Pty Ltd 47 Source: White Paper, RedEye, 2007
- 48. > Maximise iden/fica/on points 160% 140% 120% 100% 80% 60% −−− Probability of iden-fica-on through Cookies 40% 20% 0 4 8 12 16 20 24 28 32 36 40 44 48 Weeks June 2010 © Datalicious Pty Ltd 48
- 49. > Customer profiling in ac/on Using website and email responses to learn a liele bite more about subscribers at every touch point to keep refining profiles and messages. June 2010 © Datalicious Pty Ltd 49
- 50. > Enhancing data sources Customer profile data Geo-‐demographic data + The whole is greater than the sum of its parts 3rd party data June 2010 © Datalicious Pty Ltd 50
- 51. > Geo-‐demographic segments June 2010 © Datalicious Pty Ltd 51
- 52. June 2010 © Datalicious Pty Ltd 52
- 53. > Combining ad planorms On-‐site Off-‐site segments segments CRM June 2010 © Datalicious Pty Ltd 53
- 54. > The Datalicious SuperTag Easily implement and update Ad any tag on any websites without Servers IT involvement. Media Paid A;ribu/on Search De-‐duplicate conversions and collect media aeribu-on data to boost return on ad spend. Web Affiliate Analy/cs SuperTag Programs Implement complex re-‐targe-ng strategies across plakorms to increase response rates. Live Behavioral Chat Targe/ng A/B Tes/ng Enable advanced features such Heat Maps heat maps, tes-ng and live chat to op-mise conversions. June 2010 © Datalicious Pty Ltd 54
- 55. June 2010 © Datalicious Pty Ltd 55
- 56. Apple iPhone 4 June 2010 © Datalicious Pty Ltd 56
- 57. Apple iPhone 4 June 2010 © Datalicious Pty Ltd 57
- 58. > Affinity re-‐targe/ng in ac/on Different type of visitors respond to different ads. By using category affinity targe-ng, response rates are liled significantly across products. CTR By Category Affinity Message Postpay Prepay Broadb. Business Blackberry Bold - - - + Google: “vodafone 5GB Mobile Broadband - - + - omniture case study” Blackberry Storm + - + + or h;p://bit.ly/de70b7 12 Month Caps - + - + June 2010 © Datalicious Pty Ltd 58
- 59. > Ad-‐sequencing in ac/on 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 June 2010 © Datalicious Pty Ltd 59
- 60. > Sample site visitor composi/on 30% new visitors with no 30% repeat visitors with previous website history referral data and some aside from campaign or website history allowing referrer data of which 50% to be segmented by maybe 50% is useful content affinity 30% exis/ng customers with extensive 10% serious profile including transac-onal history of prospects which maybe 50% can actually be with limited iden-fied as individuals profile data June 2010 © Datalicious Pty Ltd 60
- 61. > Search call to ac/on for offline June 2010 © Datalicious Pty Ltd 61
- 62. > PURLs boos/ng DM response rates Text June 2010 © Datalicious Pty Ltd 62
- 63. > Unique phone numbers 2 out of 3 callers hang up as they cannot get their informa-on fast enough. Unique phone numbers can help improve call experience. June 2010 © Datalicious Pty Ltd 63
- 64. Exercise: Client data journey June 2010 © Datalicious Pty Ltd 64
- 65. > Exercise: Client data journey To transac/onal data To reten/on messages From suspect to prospect To customer Time Time From behavioural data From awareness messages June 2010 © Datalicious Pty Ltd 65
- 66. June 2010 © Datalicious Pty Ltd 66
- 67. Exercise: Targe/ng matrix June 2010 © Datalicious Pty Ltd 67
- 68. > Exercise: Targe/ng matrix Purchase Segments: Colour, price, Media Data Cycle product affinity, etc Channels Points Default, awareness Research, considera/on Purchase intent Reten/on, up/cross-‐sell June 2010 © Datalicious Pty Ltd 68
- 69. > Exercise: Targe/ng matrix Purchase Segments: Colour, price, Media Data Cycle product affinity, etc Channels Points Default, Have you Have you Display, Default awareness seen A? seen B? search, etc Research, A has great B has great Search, Ad clicks, considera/on features! features! website, etc prod views Purchase A delivers B delivers Website, Cart adds, intent great value! great value! emails, etc checkouts Reten/on, Why not Why not Direct mails, Email clicks, up/cross-‐sell buy B? buy A? emails, etc logins, etc June 2010 © Datalicious Pty Ltd 69
- 70. > 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 70
- 71. > ClickTale tes/ng case study June 2010 © Datalicious Pty Ltd 71
- 72. Exercise: Tes/ng matrix June 2010 © Datalicious Pty Ltd 72
- 73. > Exercise: Tes/ng matrix Test Segment Content KPIs Poten/al Results June 2010 © Datalicious Pty Ltd 73
- 74. > Exercise: Tes/ng matrix Test Segment Content KPIs Poten/al Results New Conversion Next step, Test #1A prospects form A order, etc ? ? New Conversion Next step, Test #1B prospects form B order, etc ? ? New Conversion Next step, Test #1N prospects form N order, etc ? ? ? ? ? ? ? ? June 2010 © Datalicious Pty Ltd 74
- 75. Don’t wait for be;er data, get started now. June 2010 © Datalicious Pty Ltd 75
- 76. Contact me cbartens@datalicious.com Learn more blog.datalicious.com Follow me twi;er.com/datalicious June 2010 © Datalicious Pty Ltd 76
- 77. Data > Insights > Ac/on June 2010 © Datalicious Pty Ltd 77