ANZ Marketing Analytics
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The presentation discusses the significance of data in marketing campaigns.
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ANZ Marketing Analytics
- 1. > Marke(ng Analy(cs < Using data to boost return on marke1ng investment
- 2. > Short but sharp history § Datalicious was founded in late 2007 § Strong Omniture web analy1cs history § 1 of 4 preferred Omniture partners globally § Now 360 data agency with specialist team § Combina1on of analysts and developers § Carefully selected best of breed partners § Driving industry best prac1ce (ADMA) § Turning data into ac1onable insights § Execu1ng smart data driven campaigns December 2011 © 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 ROMI December 2011 © Datalicious Pty Ltd 3
- 4. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > Media a<ribu(on December 2011 © Datalicious Pty Ltd 4
- 5. > The ideal media dashboard Channel Investment ROMI Return Brand equity ($100) n/a $40 Baseline Offline $7 330% $30 TV, print, outdoor, etc Direct $1 400% $5 Direct mail, email, etc Online $2 1150% $25 Search, display, social, etc December 2011 © Datalicious Pty Ltd 5
- 6. > Duplica(on across channels Paid Bid Search Mgmt $ Banner Ad Ads Server $ Email Email Blast PlaNorm $ Organic Google Search Analy(cs $ December 2011 © Datalicious Pty Ltd 6
- 7. > Cookie expira(on impact Paid Bid Search Mgmt $ Banner Banner Ad Ad Click Ad View Server $ Email Email Expira(on Blast PlaNorm $ Organic Google Search Analy(cs $ December 2011 © Datalicious Pty Ltd 7
- 8. > De-‐duplica(on across channels Paid Search $ Banner Ads $ Central Analy(cs PlaNorm Email Blast $ Organic Search $ December 2011 © Datalicious Pty Ltd 8
- 9. > 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 December 2011 © Datalicious Pty Ltd 9
- 10. > Media channels feed each other TV/Print/DM audience Banner Search audience audience December 2011 © Datalicious Pty Ltd 10
- 11. > Ad server exposure test Banner TV/Print Search Impression Response Response $ Banner Search Direct Impression Response Response $ Users are segmented before 1st ad is even Exposed group: 90% of users get branded message served Control group: 10% of users get non-‐branded message Banner Search Direct Impression Response Response $ December 2011 © Datalicious Pty Ltd 11
- 12. > Indirect display impact December 2011 © Datalicious Pty Ltd 12
- 13. > Indirect display impact December 2011 © Datalicious Pty Ltd 13
- 14. > Indirect display impact December 2011 © Datalicious Pty Ltd 14
- 15. > 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 December 2011 © Datalicious Pty Ltd 15
- 16. > 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 December 2011 © Datalicious Pty Ltd 16
- 17. > Full purchase path tracking Introducer Influencer Influencer Closer $ Paid Display Social Direct Online search ad clicks referrals site visits sales Display Affiliate Social Retail Offline ad views clicks buzz store visits sales TV/print Organic Website Direct mail, Life(me ad views search events emails profit December 2011 © Datalicious Pty Ltd 17
- 18. > Search call to ac(on for offline December 2011 © Datalicious Pty Ltd 18
- 21. > Personalised URLs for direct mail VickyCarroll.myspaday.com > redirect to > myspaday.com? CampaignID=DM:123& Demographics=F|35& CustomerSegment=A1& CustomerValue=High& CustomerSince=2001& ProductHistory=P1|P2& NextBestOffer=P3& ChurnRisk=Low [...] December 2011 © Datalicious Pty Ltd 21
- 22. > Offline sales driven by online Adver(sing Phone Credit check, campaign order fulfilment Retail Confirma(on order email, 1st login Website Online Online order Virtual order research order confirma(on confirma(on Cookie December 2011 © Datalicious Pty Ltd 22
- 23. > Event ROI extrapola(on Product Applica(on Applica(on Offline Campaign view start complete conversion @ @ Campaign $10 $30 $60 $100 Campaign $10 $30 $100 Campaign $10 $100 December 2011 © Datalicious Pty Ltd 23
- 24. > Single source of truth repor(ng Insights Repor(ng December 2011 © Datalicious Pty Ltd 24
- 25. > Where to collect the data Ad Server Web Analy(cs Banner impressions Referral visits Banner clicks Social media visits + Organic search visits Paid search clicks Paid search visits Email visits, etc Lacking organic visits Lacking ad impressions More granular & complex Less granular & complex December 2011 © Datalicious Pty Ltd 25
- 26. > Raw a<ribu(on data Web Analy(cs data sample (AD IMPRESSION >) AFFILIATE > SEARCH > $$$ SEARCH > SOCIAL > EMAIL > DIRECT > $$$ Ad Server data sample 01/01/2011 12:00 AD IMPRESSION 01/01/2011 12:05 PAID SEARCH 07/01/2011 17:00 EMAIL 08/01/2011 15:00 $$$ December 2011 © Datalicious Pty Ltd 26
- 27. > Purchase path for each cookie Mobile Home Work Tablet Media Etc December 2011 © Datalicious Pty Ltd 27
- 28. > Understanding channel mix December 2011 © Datalicious Pty Ltd 28
- 30. > 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 Adver1sing 9% SEO Generic 7% Display Adver1sing 14% SEM Generic 14% Email Marke1ng 7% Display Adver1sing 7% Retail Promo1ons 14% Affiliate Marke1ng 9% Referrals 5% Conversions aoributed to search terms Email Marke1ng 7% that contain brand keywords and direct website visits are most likely not the origina1ng channel that generated the awareness and as such conversion credits should be re-‐allocated. December 2011 © Datalicious Pty Ltd 30
- 31. > Adjus(ng for offline impact -‐5 -‐15 -‐10 +5 +15 +10 December 2011 © Datalicious Pty Ltd 31
- 32. > Custom a<ribu(on models Introducer Influencer Influencer Closer $ Even 25% 25% 25% 25% A<rib. Exclusion 33% 33% 33% 0% A<rib. ? ? ? ? Custom A<rib. December 2011 © Datalicious Pty Ltd 32
- 33. > Path across different segments Introducer Influencer Influencer Closer $ Product Channel 1 Channel 2 Channel 3 Channel 4 A vs. B New Channel 1 Channel 2 Channel 3 Channel 4 prospects Exis(ng Channel 1 Channel 2 Channel 3 Product 4 customers December 2011 © Datalicious Pty Ltd 33
- 34. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > Experience op(misa(on December 2011 © Datalicious Pty Ltd 34
- 35. > Increase revenue by 10-‐20% Capture internet traffic Capture 50-‐100% of fair market share of traffic Increase consumer engagement Exceed 50% of best compe1tor’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 December 2011 © Datalicious Pty Ltd 35
- 36. > New consumer decision journey The consumer decision process is changing from linear to circular. December 2011 © Datalicious Pty Ltd 36
- 37. > 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. December 2011 © Datalicious Pty Ltd 37
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- 41. December 2011 © Datalicious Pty Ltd 41
- 42. > Seamless research experience TV, print, Display direct mail, etc ads Ad Server / SuperTag Organic, paid Display ad search re-‐targe(ng AdWords Ad Server / SuperTag Test&Target / SuperTag Customised landing pages Test&Target / SuperTag ANZ.com Applica(on re-‐targe(ng process Fall-‐out email follow-‐up December 2011 © Datalicious Pty Ltd 42
- 43. > Network wide re-‐targe(ng Frequent Flyer campaign Access Advantage campaign Home Loans campaign Card Access Loan prospect prospect prospect Card Access Loan customer customer customer Loan Loan Access prospect prospect prospect December 2011 © Datalicious Pty Ltd 43
- 44. > Network wide re-‐targe(ng Group wide campaign with approximate impression targets by product rather than hard budget limita(ons Card Access Loan prospect prospect prospect Card Access Loan customer customer customer Loan Loan Access prospect prospect prospect December 2011 © Datalicious Pty Ltd 44
- 45. Targe(ng before tes(ng December 2011 © Datalicious Pty Ltd 45
- 46. > Developing a targe(ng matrix Segmenta(on based on: Search keywords, display ad clicks and website behaviour Purchase Data Cycle Points Access Frequent Etc Advantage Flyers Research, Acquisi(on Acquisi(on Acquisi(on Ad clicks, considera(on message #A1 message #A3 message #A5 prod views Conversion Acquisi(on Acquisi(on Acquisi(on Applica(on intent message #A2 message #A4 message #A6 starts Reten(on, Reten(on Reten(on Reten(on Email clicks, cross-‐sell message #R1 message #R2 message #R3 logins, etc December 2011 © Datalicious Pty Ltd 46
- 47. > Combining data sources Website behavioural data Campaign response data + The whole is greater than the sum of its parts Customer profile data December 2011 © Datalicious Pty Ltd 47
- 48. > Transac(ons plus behaviours CRM Profile Site Behaviour one-‐off collec1on 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 predic1ve models based on data mining tracking of external campaign responses propensity to buy, churn, etc search terms, referrers, etc historical data from previous transac1ons tracking of internal promo1on responses average order value, points, etc emails, internal search, etc Updated Occasionally Updated Con(nuously December 2011 © Datalicious Pty Ltd 48
- 49. > Maximise iden(fica(on points 160% 140% 120% 100% 80% 60% −−− Probability of iden1fica1on through Cookies 40% 20% 0 4 8 12 16 20 24 28 32 36 40 44 48 Weeks December 2011 © Datalicious Pty Ltd 49
- 50. > Developing a tes(ng matrix Test Segment Content Success Difficulty Poten(al New Acquisi(on Clicks, Test #1A prospects offer A orders, etc Low $50k New Acquisi(on Clicks, Test #1B prospects offer B orders, etc Exis(ng Up-‐sell Clicks, Test #2A customers offer A orders, etc High $75k Exis(ng Up-‐sell Clicks, Test #2B customers offer B orders, etc December 2011 © Datalicious Pty Ltd 50
- 51. > The holy trinity of tes(ng 1. The headline – Have a headline! – Headline should be concrete – Headline should be first thing visitors look at 2. Call to ac(on – Don’t have too many calls to ac1on – Have an ac1onable call to ac1on – Have a big, prominent, visible call to ac1on 3. Social proof – Logos, number of users, tes1monials, case studies, media coverage, etc December 2011 © Datalicious Pty Ltd 51
- 52. > Best prac(ce tes(ng roadmap § Phase #1: A/B test – Test the same landing Element #1: Prominent headline page content in completely different layouts § Phase #2: MV test Suppor1ng Element #2: – Then test different content Call to ac1on content element combina1ons within the winning layout Element #3: Social proof / trust § Phase #3: Challenge – Con1nue tes1ng and introducing layout and Terms and condi1ons content challengers December 2011 © Datalicious Pty Ltd 52
- 53. > Use unique phone numbers 2 out of 3 callers hang up as they cannot get their informa1on fast enough. Unique phone numbers can help improve call experience. December 2011 © Datalicious Pty Ltd 53
- 54. December 2011 © Datalicious Pty Ltd 54
- 55. Contact me cbartens@datalicious.com Learn more blog.datalicious.com Follow me twi<er.com/datalicious December 2011 © Datalicious Pty Ltd 55
- 56. Data > Insights > Ac(on December 2011 © Datalicious Pty Ltd 56