201306 aimia big data beyond the hype v1
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> Big data in marke.ng < What the heck? What does it all mean and how does it help me?
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> Using data to widen the funnel
Media A:ribu.on & Modeling
Maximise reach, awareness & increase ROI
Tes.ng & Op.misa.on Remove barriers, drive sales
Boos.ng ROMI
Targe.ng & Merchandising Improve engagement, boost loyalty
“Turning data into ac.onable insights to widen the conversion funnel”
June 2013 © Datalicious Pty Ltd 2
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> Clients across all industries
June 2013 © Datalicious Pty Ltd 3
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> Wikipedia: Big data In informaAon technology, big data consists of datasets that grow so large that they become awkward to work with using on-‐hand database management tools. DifficulAes include capture, storage, search, sharing, analyAcs, and visualizing. Big data are high volume, high velocity, and/or high variety informa.on assets that require new forms of processing to enable enhanced decision making, insight discovery and process opAmizaAon.
June 2013 © Datalicious Pty Ltd 4
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Big data = Bo:lenecks
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> Big data analy.cs bo:lenecks
June 2013 © Datalicious Pty Ltd 6
Fast laptops now have up to 8GB of RAM, that means you can compute up to 6GB of raw data very fast in memory thus bypassing the biggest boTleneck: I/O
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> Power vs. distributed compu.ng
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Adding more supercomputers is difficult as they are complex and expensive but adding machines to a distributed compuAng network is fairly cheap and ‘easy’.
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Big data = Structure?
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> Does big data need structure?
June 2013 © Datalicious Pty Ltd 9
Volume, velocity, variety, sexy
Structure, m
ainten
ance, b
oring
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> Big data s.ll needs structure
June 2013 © Datalicious Pty Ltd 10
Volume, velocity, variety, sexy
Structure, m
ainten
ance, b
oring
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Big data = Hype?
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> Importance of research experience
June 2013 © Datalicious Pty Ltd 12
The consumer decision process is changing from linear to circular.
Considera.on set now grows during (online) research phase which increases importance of user experience during that phase
(Online) Research
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Offer
Issue
Offer
> Design and test experiences
June 2013 © Datalicious Pty Ltd 13
Live chat Phone call
Phone call Le:er Email
Issue
All customers Segment A, B, C
Segment D, E Influencers High valu
Display
Postcard
Display
FAQs
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> The consumer data journey
June 2013 © Datalicious Pty Ltd 14
To reten.on messages To transac.onal data
From suspect to To customer
From behavioural data From awareness messages
Time Time prospect
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Transac.onal data
> Combining data sources is key
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3rd party data
+ The whole is greater than the sum of its parts
Behavioural data
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June 2013 © Datalicious Pty Ltd 16 Example: Phone call data
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June 2013 © Datalicious Pty Ltd 17 Example: Website data
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June 2013 © Datalicious Pty Ltd 18 Example: Social media data
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> Maximise iden.fica.on points
20%
40%
60%
80%
100%
120%
140%
160%
0 4 8 12 16 20 24 28 32 36 40 44 48
Weeks
−−− Probability of idenAficaAon through Cookies
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Customer data exposed in page or URL on login and logout
CustomerID=12345& Demographics=M|25& CustomerSegment=A1& CustomerValue=High& ProductHistory=A6& NextProduct=A7& ChurnRisk=High& [...]
> Registra.on and login pages
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hTp://www.acme.com/email-‐landing-‐page.html?
CampaignID=12345& CustomerID=12345& Demographics=M|25& CustomerSegment=A1& CustomerValue=High& ProductHistory=A6& NextProduct=A7& ChurnRisk=High& [...]
> Email click-‐through iden.fica.on
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acme.com/chris.anbartens redirects to amp.com.au?
CampaignID=12345& CustomerID=12345& Demographics=M|25& CustomerSegment=A1& CustomerValue=High& ProductHistory=A6& NextProduct=A7& ChurnRisk=High& [...]
> Personalised URLs for direct mail
June 2013 © Datalicious Pty Ltd 24
Catch on acme.com
404 error page
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> Combine data across devices
June 2013 © Datalicious Pty Ltd 25
Mobile Home Work
Tablet Media Etc
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> Indirect combina.on of data
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Social IDs
Client ID
Web data
Address Geo segment
Roy Morgan
Etc
MOSAIC Hitwise
Social data
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June 2013 © Datalicious Pty Ltd 29
Contact us [email protected]
Learn more
blog.datalicious.com
Follow us twi:er.com/datalicious
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Smart data driven marke.ng
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