Data Driven Marketing - Advanced Analytics and Targeting
•
0 j'aime•538 vues
The presentation discusses the impact of data driven targeting in marketing campaigns.
![[
Data
driven
marke.ng
]
Reducing
waste
and
increasing
relevance
through
targe3ng](https://image.slidesharecdn.com/201009nabadvancedanalyticstargetingv1-121117124153-phpapp01/85/Data-Driven-Marketing-Advanced-Analytics-and-Targeting-1-320.jpg)
![[
Quick
company
history
]
§ Datalicious
was
founded
in
2007
§ Strong
Omniture
web
analy3cs
history
§ 1
of
4
global
Omniture
Preferred
Partners
§ Now
360
data
agency
with
specialist
team
§ Combina3on
of
analysts
and
developers
§ Evangelizing
smart
data
driven
marke3ng
§ Making
data
accessible
and
ac3onable
§ Driving
industry
best
prac3ce
(ADMA)
September
2010
©
Datalicious
Pty
Ltd
2](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommandé
Contenu connexe
Tendances
Tendances (20)
En vedette
En vedette (20)
Similaire à Data Driven Marketing - Advanced Analytics and Targeting
Similaire à Data Driven Marketing - Advanced Analytics and Targeting (19)
Plus de Datalicious
Plus de Datalicious (20)
Dernier
Dernier (20)
Data Driven Marketing - Advanced Analytics and Targeting
- 1. [ Data driven marke.ng ] Reducing waste and increasing relevance through targe3ng
- 2. [ Quick company history ] § Datalicious was founded in 2007 § Strong Omniture web analy3cs history § 1 of 4 global Omniture Preferred Partners § Now 360 data agency with specialist team § Combina3on of analysts and developers § Evangelizing smart data driven marke3ng § Making data accessible and ac3onable § Driving industry best prac3ce (ADMA) September 2010 © Datalicious Pty Ltd 2
- 3. [ Wide range of data services ] Data Insights Ac.on Pla=orms Repor.ng Applica.ons 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 Media aMribu.on models Aprimo, Trac.on, Inxmail, etc Tag-‐less online data capture Market and compe.tor trends Targe.ng and merchandising End-‐to-‐end data pla=orms Social media monitoring Internal search op.misa.on IVR and call center repor.ng Online surveys and polls CRM strategy and execu.on Single customer view Customer profiling Tes.ng programs September 2010 © Datalicious Pty Ltd 3
- 4. [ Clients across all industries ] September 2010 © Datalicious Pty Ltd 4
- 5. [ Using data to reduce waste ] Media aMribu.on Op.mising channel mix Targe.ng Increasing relevance Tes.ng Improving usability $$$ September 2010 © Datalicious Pty Ltd 5
- 6. [ Increase revenue by 10-‐20% ] By coordina.ng the consumer’s end-‐to-‐end experience, companies could enjoy revenue increases of 10-‐20%. Google: “get more value from digital marke.ng” or hMp://bit.ly/cAtSUN September 2010 © Datalicious Pty Ltd 6 Source: McKinsey Quarterly, 2010
- 7. [ 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 7
- 8. [ Coordina.on across channels ] Genera.ng Crea.ng Maximising awareness engagement revenue TV, radio, print, Retail stores, call Outbound calls, direct outdoor, search centers, brochures, mail, emails, SMS, etc marke3ng, display websites, landing ads, performance pages, mobile apps, networks, affiliates, online chat, etc social media, etc Off-‐site On-‐site Profile targe.ng targe.ng targe.ng September 2010 © Datalicious Pty Ltd 8
- 9. [ Combining targe.ng pla=orms ] Off-‐site targe3ng Profile On-‐site targe3ng targe3ng September 2010 © Datalicious Pty Ltd 9
- 10. September 2010 © Datalicious Pty Ltd 10
- 11. September 2010 © Datalicious Pty Ltd 11
- 12. [ Combining technology ] On-‐site Off-‐site segments segments September 2010 © Datalicious Pty Ltd 12
- 13. [ Datalicious SuperTag ] § Central JavaScript based container tag § One tag for all pla^orms incl. Omniture § Either hosted internally or externally § Faster tag implementa3on and updates § Consistent network wide re-‐targe3ng § Transfer or profiling data between sites § Iden3fica3on of exis3ng customers § Re-‐targe3ng by brand preferences September 2010 © Datalicious Pty Ltd 13
- 14. [ 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 14
- 15. [ Behaviours plus transac.ons ] Site Behaviour CRM Profile tracking of purchase funnel stage one-‐off collec3on 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 predic3ve models based on data mining search terms, referrers, etc propensity to buy, churn, etc tracking of internal promo3on responses historical data from previous transac3ons emails, internal search, etc average order value, points, etc UPDATED CONTINUOUSLY UPDATED OCCASIONALLY September 2010 © Datalicious Pty Ltd 15
- 16. [ Using Pion to enrich CRM data ] § Single point of data capture and processing § Real-‐3me queries to enrich website data § Mul3ple data export op3ons for web analy3cs § Enriching single-‐customer view website behaviour September 2010 © Datalicious Pty Ltd 16
- 17. [ Overes.ma.ng unique visitors ] 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 overes3mated visitors by up to 7.6 3mes whilst a cookie-‐based approach overes.mated visitors by up to 2.3 .mes. Google: ”red eye cookie report pdf” or hMp://bit.ly/cszp2o Source: White Paper, RedEye, 2007
- 18. Datalicious SuperCookie Persistent Flash cookie that cannot be deleted September 2010 © Datalicious Pty Ltd 18
- 19. [ Maximise iden.fica.on points ] 160% 140% 120% 100% 80% 60% −−− Probability of iden3fica3on through Cookies 40% 20% 0 4 8 12 16 20 24 28 32 36 40 44 48 Weeks September 2010 © Datalicious Pty Ltd 19
- 20. [ Sample customer level data ] September 2010 © Datalicious Pty Ltd 20
- 21. [ 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 transac3onal history of prospects which maybe 50% can actually be with limited iden3fied as individuals profile data September 2010 © Datalicious Pty Ltd 21
- 22. [ Poten.al home page layout ] Customise content Branded header delivery on the fly based on referrer data, past content Rule based offer Login consump3on or profile data for exis3ng customers. Targeted Targeted offer offer Popular links, FAQs September 2010 © Datalicious Pty Ltd 22
- 23. [ Prospect targe.ng parameters ] September 2010 © Datalicious Pty Ltd 23
- 24. [ Affinity targe.ng in ac.on ] Different type of visitors respond to different ads. By using category affinity targe3ng, response rates are liked 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 hMp://bit.ly/de70b7 12 Month Caps - + - + September 2010 © Datalicious Pty Ltd 24
- 25. [ Poten.al newsleMer layout ] Using profile data Rule based branded header enhanced with website behaviour Data verifica.on NPS data imported into the email delivery pla^orm to build Rule based offer business rules and Closest stores, customise content Profile based offer delivery. offers etc September 2010 © Datalicious Pty Ltd 25
- 26. [ Customer profiling in ac.on ] Using website and email responses to learn a lille bite more about customers at every touch point in order to keep refining customer profiles and customising future communica3ons. September 2010 © Datalicious Pty Ltd 26
- 27. [ Poten.al landing page layout ] Passing data on user Rule based branded header preferences through to the website via parameters in email Campaign message match click-‐through URLs to customise content delivery. Targeted offer Call to ac.on September 2010 © Datalicious Pty Ltd 27
- 28. Exercise: Targe.ng matrix September 2010 © Datalicious Pty Ltd 28
- 29. [ Exercise: Targe.ng matrix ] Phase Segment A/B Channels Data Points Awareness Considera.on Purchase Intent Up/Cross-‐Sell September 2010 © Datalicious Pty Ltd 29
- 30. [ Exercise: 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 30
- 31. [ 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 31
- 32. [ Small changes with big impact ] September 2010 © Datalicious Pty Ltd 32
- 33. [ Bad campaign worse than none ] September 2010 © Datalicious Pty Ltd 33
- 34. [ Keys to effec.ve targe.ng ] 1. Define success metrics 2. Define and validate segments 3. Develop targe3ng and message matrix 4. Transform matrix into business rules 5. Develop and test content 6. Start targe3ng and automate 7. Keep tes3ng and refining 8. Communicate results September 2010 © Datalicious Pty Ltd 34
- 35. ADMA short course “Analyse to op.mise” In Melbourne & Sydney October/November By Datalicious September 2010 © Datalicious Pty Ltd 35
- 36. Email me cbartens@datalicious.com Follow us twiMer.com/datalicious Learn more blog.datalicious.com September 2010 © Datalicious Pty Ltd 36