Why Data is Sexy
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The document discusses strategies for analyzing customer data from multiple sources to improve marketing campaigns. It describes categorizing different types of data, defining analytics goals, de-duplicating data across channels, attributing conversions to specific channels, analyzing cross-channel impact, and combining on-site and off-site customer data to improve targeting. The overall aim is to develop a single view of the customer to optimize marketing performance.
![[
Why
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is
sexy
]
No
really,
it
is!](https://image.slidesharecdn.com/200908googledatalunchv2-121117120916-phpapp02/85/why-data-is-sexy-1-320.jpg)
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Main
data
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Campaign
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Consumer
Customer
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Compe5tor
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14/11/12
©
Datalicious
Pty
Ltd
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Why Data is Sexy
- 1. [ Why data is sexy ] No really, it is!
- 2. [ Main data categories ] Campaign data Consumer Customer data data Compe5tor data 14/11/12 © Datalicious Pty Ltd 2
- 3. Campaign data 14/11/12 © Datalicious Pty Ltd 3
- 4. [ Defining analy:cs strategy ] Search, display ads Web analy5cs Awareness Interest Desire Ac5on Sa5sfac5on Online surveys, site polls Social media Social media 14/11/12 © Datalicious Pty Ltd 4
- 5. [ Single source of truth ] data ROAS 14/11/12 © Datalicious Pty Ltd 5
- 6. [ De-‐duplica:on across channels ] Paid Bid Search Mgmt $ Banner Ad Ads Server $ Omniture PlaGorm Email Email Blast PlaGorm $ Organic Google Search Analy:cs $ 14/11/12 © Datalicious Pty Ltd 6
- 7. [ De-‐duplica:on across channels ] 14/11/12 © Datalicious Pty Ltd 7
- 8. [ Success aIribu: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 14/11/12 © Datalicious Pty Ltd 8
- 9. [ Campaign stacking by channel ] Chart shows percentage of channel touch points that lead to a conversion. Neither first nor last-‐click measurement would provide true picture 14/11/12 © Datalicious Pty Ltd 9
- 10. [ Search call to ac:on ] 14/11/12 © Datalicious Pty Ltd & Omniture Inc 10
- 11. [ Analyse channel overlap ] Total Ac:ons: 2,023 DM & EDMs Call Centre Paid Search $200 -‐ $400 Radio 301 / 11.1% CTA & $200 -‐ $500 CPA Other 112 (38%) ATL Display Media 189 (62%) 1669 (61.2%) Straight to Site 1208 (44.3%) $125 -‐ $200 CPA Organic Search 493 (18%) 587 (48%) 314 (63%) 621 (52%) 179 (37%) Partners 14/11/12 © Datalicious Pty Ltd 11
- 12. [ Cross-‐channel impact ] 14/11/12 © Datalicious Pty Ltd 12
- 13. [ Email iden:fica:on points ] @ Vodafone.com.au Phone Online Receipt Research Credit Check Confirma5on Conversion Fulfilment @ Adver5sing Vodafone.com.au Retail Online Receipt Research Credit Check Confirma5on Campaign Conversion Fulfilment @ Vodafone.com.au Online Online Order Online Receipt Research Confirma5on Credit Check Confirma5on Conversion Fulfilment Cookie ID 14/11/12 © Datalicious Pty Ltd & Omniture Inc 13
- 14. Customer data 14/11/12 © Datalicious Pty Ltd 14
- 15. [ Store locator searches ] 14/11/12 © Datalicious Pty Ltd 15
- 16. [ Reasons for store drop-‐out ] About a third of those dropping out intend to come back again. 1 in 5 will go away and think more about their purchase. Only 10% have inten5ons purely to use the store for research. 14/11/12 © Datalicious Pty Ltd 16
- 17. [ Targe:ng framework ] Reten5on Customer Profile Prospect Customer -‐12 -‐11 -‐10 -‐9 -‐8 -‐7 -‐6 -‐5 -‐4 -‐3 -‐2 -‐1 0 1 2 3 4 5 6 7 8 9 10 11 12 Considera5on Visitor Behaviour Weeks 14/11/12 © Datalicious Pty Ltd 17
- 18. [ Prospect targe:ng parameters ] 14/11/12 © Datalicious Pty Ltd 18
- 19. [ Affinity targe:ng in ac:on ] Different types of visitors respond to different ads. By using category affinity targe5ng, response rates are liced significantly across products. Click-‐Through Rate By Category Affinity Message Postpay Prepay Broadb. Business Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - + 14/11/12 © Datalicious Pty Ltd 19
- 20. [ Matching segments are key ] On-‐site Off-‐site segments segments On and off-‐site targe5ng plaeorms should use iden5cal triggers to sort visitors into segments 14/11/12 © Datalicious Pty Ltd 20
- 21. 14/11/12 © Datalicious Pty Ltd 21
- 22. [ Combining data sets ] Site Behaviour CRM Profile tracking of purchase funnel stage one-‐off collec5on of demographical data + browsing, checkout, etc age, gender, address, etc tracking of content preferences customer lifecycle metrics and key dates products, brands, features, etc profitability, expira:on, etc tracking of external campaign responses predic5ve models based on data mining search terms, referrers, etc propensity to buy, churn, etc tracking of internal promo5on responses historical data from previous transac5ons emails, internal search, etc average order value, points, etc UPDATED CONTINUOUSLY UPDATED OCCASIONALLY 14/11/12 © Datalicious Pty Ltd 22
- 23. Omniture Google DataWarehouse Analy5cs Online behavioural data stored Omniture in Omniture for anonymous DataWarehouse prospects and known customers Customer status and product affinity for prospects and customers passed into Test&Target as well as Eyeblaster for advanced on and off-‐site targe5ng (e.g. help/support messages for Omniture exis5ng customers, acquisi5on messages with customized plans for prospects, etc) Eyeblaster Test&Target Data on MyVodafone registra5on status to iden5fy non-‐users in order to encourage online self-‐service Omniture Discover OnPremise Data on MyVodafone self-‐service usage to iden5fy users with poten5al issues in order to increase customer sa5sfac5on Vodafone Terradata Customer data on self-‐service usage, campaign responses and product preferences are filtered against Terradata in Discover Data on campaign performance to iden5fy most suitable OnDemand, contact lists are message content and 5ming for each customer or segment generated and customized messages delivered through the most suitable channels Data on product preferences and research behaviour to customize product offering and feed into churn modelling
- 24. [ Where is the money ] $ $ $ $ $ $ $ $ $ $ [ october 2007 ] [ datalicious.com.au ]
- 25. [ Mosaic ] 14/11/12 © Datalicious Pty Ltd 25
- 26. Compe:tor data 14/11/12 © Datalicious Pty Ltd 26
- 27. [ Brand search volume ] 14/11/12 © Datalicious Pty Ltd 27
- 28. [ Market share trends ] 14/11/12 © Datalicious Pty Ltd 28
- 29. 14/11/12 © Datalicious Pty Ltd 29
- 30. 14/11/12 © Datalicious Pty Ltd 30
- 31. 14/11/12 © Datalicious Pty Ltd 31
- 32. [ Australia vs. New Zealand ] australia.com vs. newzealand.com [ september 2007 ] [ datalicious.com.au ]
- 33. Consumer data 14/11/12 © Datalicious Pty Ltd 33
- 34. [ Search term volumes ] 14/11/12 © Datalicious Pty Ltd 34
- 35. 14/11/12 © Datalicious Pty Ltd 35
- 36. 14/11/12 © Datalicious Pty Ltd 36
- 37. [ Media planning ] 14/11/12 © Datalicious Pty Ltd 37
- 38. [ Leading lifestyles, 45+ ] 14/11/12 © Datalicious Pty Ltd 38
- 39. [ Main data categories ] Campaign data Consumer Customer data data Compe5tor data 14/11/12 © Datalicious Pty Ltd 39
- 40. 14/11/12 © Datalicious Pty Ltd 40
- 41. 14/11/12 © Datalicious Pty Ltd 41