Data Driven Marketing - the Key to an Effective Marketing Campaign
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3 likes•1,281 views
The presentation discusses the impact of data driven targeting in marketing campaigns.
![[
Data
driven
marke.ng
]
Data
to
help
create
highly
targeted
and
engaging
campaigns](https://image.slidesharecdn.com/201009campaignpalaceshowandtellv1-121117123442-phpapp02/85/Data-Driven-Marketing-the-Key-to-an-Effective-Marketing-Campaign-1-320.jpg)
![[
Quick
company
history
]
§ Datalicious
was
founded
in
2007
§ Strong
Omniture
web
analy<cs
history
§ 1
of
4
global
Omniture
Preferred
Partners
§ Now
360
data
agency
with
specialist
team
§ Combina<on
of
analysts
and
developers
§ Evangelizing
smart
data
driven
marke<ng
§ Making
data
accessible
and
ac<onable
§ Driving
industry
best
prac<ce
(ADMA)
September
2010
©
Datalicious
Pty
Ltd
2](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Data Driven Marketing - the Key to an Effective Marketing Campaign
- 1. [ Data driven marke.ng ] Data to help create highly targeted and engaging campaigns
- 2. [ Quick company history ] § Datalicious was founded in 2007 § Strong Omniture web analy<cs history § 1 of 4 global Omniture Preferred Partners § Now 360 data agency with specialist team § Combina<on of analysts and developers § Evangelizing smart data driven marke<ng § Making data accessible and ac<onable § Driving industry best prac<ce (ADMA) September 2010 © Datalicious Pty Ltd 2
- 3. [ Clients across all industries ] September 2010 © Datalicious Pty Ltd 3
- 4. [ Using data to reduce waste ] Media a>ribu.on Op.mising channel mix Targe.ng Increasing relevance Tes.ng Improving usability $$$ September 2010 © Datalicious Pty Ltd 4
- 5. [ 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 September 2010 © Datalicious Pty Ltd 5
- 6. [ 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 September 2010 © Datalicious Pty Ltd 6
- 7. [ Combining targe.ng plaKorms ] Off-‐site targe<ng Profile On-‐site targe<ng targe<ng September 2010 © Datalicious Pty Ltd 7
- 8. h>p://ww.wesKield.com?data=zimbio,promo.on September 2010 © Datalicious Pty Ltd 8
- 9. [ Search and media planning ] September 2010 © Datalicious Pty Ltd 9
- 10. cookie: zimbio, promo.on, chris.ne, fashion September 2010 © Datalicious Pty Ltd 10
- 11. [ Affinity targe.ng in ac.on ] Different type of visitors respond to different ads. By using category affinity targe<ng, response rates are lied 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 - + - + September 2010 © Datalicious Pty Ltd 11
- 12. h>p://ww.wesKield.com?data=chris.ne,promo.on September 2010 © Datalicious Pty Ltd 12
- 13. [ Customer profiling in ac.on ] Using website and email responses to learn a li_le bite more about customers at every touch point in order to keep refining customer profiles and customising future communica<ons. September 2010 © Datalicious Pty Ltd 13
- 14. [ Developing a targe.ng matrix ] Phase Segment A/B Channels Data Points Social, display, Awareness Seen this? Default search, etc Social, search, Download, Considera.on Great feature! website, etc product view Search, site, Cart add, Purchase Intent Great value! emails, etc checkout, etc Direct mail, Email response, Up/Cross-‐Sell Add this! emails, etc login, etc September 2010 © Datalicious Pty Ltd 14
- 15. [ Quality content key to success ] 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.” September 2010 © Datalicious Pty Ltd 15
- 16. [ Combining data sets ] Website behavioural data Campaign response data + The whole is greater than the sum of its parts Customer profile data September 2010 © Datalicious Pty Ltd 16
- 17. [ Behaviours plus transac.ons ] Site Behaviour CRM Profile tracking of purchase funnel stage one-‐off collec<on 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 predic<ve models based on data mining search terms, referrers, etc propensity to buy, churn, etc tracking of internal promo<on responses historical data from previous transac<ons emails, internal search, etc average order value, points, etc Updated Con.nuously Updated Occasionally September 2010 © Datalicious Pty Ltd 17
- 18. [ Sample customer level data ] September 2010 © Datalicious Pty Ltd 18
- 19. [ Social media as data source ] Facebook Connect gives your company the following data and more with just one click Email address, first name, last name, gender, birthday, interests, picture, affilia<ons, last profile update, <me zone, religion, poli<cal interests, a_racted to which sex, why they want to meet someone, home town, rela<onship status, current loca<on, ac<vi<es, music interests, tv show interests, educa<on history, work history, family, etc Need anything else? September 2010 © Datalicious Pty Ltd 19
- 20. September 2010 © Datalicious Pty Ltd 20
- 21. Appending social data to customer profiles Name, age, gender, occupa.on, loca.on, social profiles and influencer ranking based on email (influencers only) (all contacts) September 2010 © Datalicious Pty Ltd 21
- 22. [ Social media data poten.al ] § Large Australian consumer brand § 20% of customer emails had social profiles § Each profile had an average of 8 friends § 2% of profiles had an influencer score § 0.5% of social had a score of over 10 § For a database of 500,000 that would mean § Poten<al addi<onal reach of 100,000 friends § Includes 2,500 influen<al individuals September 2010 © Datalicious Pty Ltd 22
- 23. [ Overall volume and influence ] Data from September 2010 © Datalicious Pty Ltd 23
- 24. [ Influence and media value ] US Data from UK AU/NZ September 2010 © Datalicious Pty Ltd 24
- 25. [ Google data in Australia ] Source: h_p://www.hitwise.com/au/datacentre September 2010 © Datalicious Pty Ltd 25
- 26. [ Search at all stages ] September 2010 © Datalicious Pty Ltd 26 Source: Inside the Mind of the Searcher, Enquiro 2004
- 27. [ Search and brand strength ] September 2010 © Datalicious Pty Ltd 27
- 28. [ Search and the product lifecycle ] Nokia N-‐Series Apple iPhone September 2010 © Datalicious Pty Ltd 28
- 29. [ Search driving offline crea.ve ] September 2010 © Datalicious Pty Ltd 29
- 30. [ Mapping out campaign flows ] = Paid media Organic PR, WOM, search events, etc = Viral elements = Coupons, surveys 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 C1 C2 CRM Facebook program Twi>er, etc C3 POS kiosks, Call center, loyalty cards, etc retail stores, etc September 2010 © Datalicious Pty Ltd 30
- 31. [ Developing a metrics framework ] Media and search data Website, call center and retail data People People People People Reached 40% Engaged 10% Converted 1% Delighted Quan<ta<ve and qualita<ve research data Social media data Social media September 2010 © Datalicious Pty Ltd 31
- 32. [ De-‐duplica.on across channels ] Paid Bid Search Mgmt $ Banner Ad Ads Server $ Central Analy.cs PlaKorm Email Email Blast PlaKorm $ Organic Google Search Analy.cs $ September 2010 © Datalicious Pty Ltd 32
- 33. [ 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 September 2010 © Datalicious Pty Ltd 33
- 34. [ Search call to ac.on for offline ] September 2010 © Datalicious Pty Ltd 34
- 35. September 2010 © Datalicious Pty Ltd 35
- 36. [ Understanding channel mix ] September 2010 © Datalicious Pty Ltd 36
- 37. [ Target Denim ] § 51,737 Visitors § 521,857 Unique Page Views § 11,402 people shared on Facebook (Most from emails or Facebook) § 6,821 TVC Views § 82% New Visits (Target average 73%) § 2,005 Wins § Average Time on site is 2.25 minutes (Target average 1.07 minutes) September 2010 © Datalicious Pty Ltd 37
- 38. [ Key traffic drivers ] § The campaign had a huge first day before paid media began which built momentum early NB: Removed data from Friday Feb 11th as due to extreme skew September 2010 © Datalicious Pty Ltd 38
- 39. [ YouTube ] § 13,084 YouTube views, 70 comments, 636 ra<ngs (490 bad, 136 good) – Silvia Pfeiffer from Vquence found that males aged 15 – 25 were more likely to comment than any other demographic Engagement compared to videos of similar length § Higher than average engagement from viewers compared to videos of a similar length § Honourable men<ons for the week ending the Feb 21st September 2010 © Datalicious Pty Ltd 39
- 40. [ Campaign comparison ] § Campaign traffic was almost that of Christmas and much higher than the very successful ‘Colours’ campaign Christmas = Nov 20 to Dec 30, 2009 Colours = Aug 7 – Sep 16, 2009 § Looking only at campaign Site Visits incep<on, it did drive 56% of Visits occur in the higher daily average traffic; first 4 days 34k to 32k respec<vely September 2010 © Datalicious Pty Ltd 40
- 41. Email me cbartens@datalicious.com Follow us twi>er.com/datalicious Learn more blog.datalicious.com September 2010 © Datalicious Pty Ltd 41