Effective Targeting
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The presentation discusses the impact of data driven targeting to marketing campaigns.
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Effective Targeting
- 1. > Effec've Targe'ng < Coordinate the user experience to boost conversions
- 2. > 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
- 3. > 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 3
- 4. > Wide range of data services Data Insights Ac'on PlaKorms Analy'cs Campaigns Data collec'on and processing Data mining and modelling Data usage and applica'on Web analy'cs solu'ons Customised dashboards Marke'ng automa'on Omniture, Google Analy'cs, etc Tableau, SpoKire, SPSS, etc Alterian, SiteCore, Inxmail, etc Tag-‐less online data capture Media a>ribu'on models Targe'ng and merchandising End-‐to-‐end data plaKorms Market and compe'tor trends Internal search op'misa'on IVR and call center repor'ng Social media monitoring CRM strategy and execu'on Single customer view Customer profiling Tes'ng programs June 2010 © Datalicious Pty Ltd 4
- 5. > Clients across all industries June 2010 © Datalicious Pty Ltd 5
- 6. Ques'ons? Tweet @datalicious June 2010 © Datalicious Pty Ltd 6
- 7. Targe'ng The right message Via the right channel To the right person At the right ?me June 2010 © Datalicious Pty Ltd 7
- 8. > 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 8
- 9. > New consumer decision journey The consumer decision process is changing from linear to circular. June 2010 © Datalicious Pty Ltd 9
- 10. > 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 10
- 11. June 2010 © Datalicious Pty Ltd 11
- 12. > The consumer 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 12
- 13. > 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 13
- 14. > Integra'ng targe'ng plaKorms Off-‐site targe?ng Profile On-‐site targe?ng targe?ng June 2010 © Datalicious Pty Ltd 14
- 15. > 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 15
- 16. > 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 16
- 17. > Sample customer level data June 2010 © Datalicious Pty Ltd 17
- 18. > 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 18 Source: White Paper, RedEye, 2007
- 19. > 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 19
- 20. > Customer profiling in ac'on Using website and email responses to learn a lifle bite more about subscribers at every touch point to keep refining profiles and messages. June 2010 © Datalicious Pty Ltd 20
- 21. > Combining ad plaKorms On-‐site Off-‐site segments segments CRM June 2010 © Datalicious Pty Ltd 21
- 22. > 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 afribu?on data to boost return on ad spend. Web Affiliate Analy'cs SuperTag Programs Implement complex re-‐targe?ng strategies across plagorms 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 22
- 23. June 2010 © Datalicious Pty Ltd 23
- 24. Apple iPhone 4 June 2010 © Datalicious Pty Ltd 24
- 25. Apple iPhone 4 June 2010 © Datalicious Pty Ltd 25
- 26. June 2010 © Datalicious Pty Ltd 26
- 27. June 2010 © Datalicious Pty Ltd 27
- 28. > Affinity re-‐targe'ng in ac'on Different type of visitors respond to different ads. By using category affinity targe?ng, response rates are lihed 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 28
- 29. > 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 29
- 30. > 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 30
- 31. > Search call to ac'on for offline June 2010 © Datalicious Pty Ltd 31
- 32. > PURLs boos'ng DM response rates Text June 2010 © Datalicious Pty Ltd 32
- 33. > 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 33
- 34. > Developing a 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 34
- 35. > 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 35
- 36. > Google Ngram: Privacy June 2010 © Datalicious Pty Ltd 36
- 37. Collec'ng data for the sake of it or to add value to customers? June 2010 © Datalicious Pty Ltd 37
- 38. Contact me cbartens@datalicious.com Learn more blog.datalicious.com Follow me twi>er.com/datalicious June 2010 © Datalicious Pty Ltd 38
- 39. Data > Insights > Ac'on June 2010 © Datalicious Pty Ltd 39