Data Driven Marketing - the Key to an Effective Marketing Campaign
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Transcript of Data Driven Marketing - the Key to an Effective Marketing Campaign
[ Data driven marke.ng ] Data to help create highly targeted
and engaging campaigns
[ 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)
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[ Clients across all industries ]
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[ Using data to reduce waste ]
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Media a>ribu.on
Op.mising channel mix
Tes.ng Improving usability
$$$
Targe.ng Increasing relevance
[ The consumer data journey ]
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To reten.on messages To transac.onal data
From suspect to To customer
From behavioural data From awareness messages
Time Time prospect
[ Coordina.on across channels ]
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Off-‐site targe.ng
On-‐site targe.ng
Profile targe.ng
Genera.ng awareness
Crea.ng engagement
Maximising revenue
TV, radio, print, outdoor, search marke<ng, display ads, performance networks, affiliates, social media, etc
Retail stores, in-‐store kiosks, call centers, brochures, websites, mobile apps, online chat, social media, etc
Outbound calls, direct mail, emails, social media, SMS, mobile apps, etc
Off-‐site targe<ng
On-‐site targe<ng
Profile targe<ng
[ Combining targe.ng plaKorms ]
September 2010 © Datalicious Pty Ltd 7
September 2010 © Datalicious Pty Ltd 8 h>p://ww.wesKield.com?data=zimbio,promo.on
[ Search and media planning ]
September 2010 © Datalicious Pty Ltd 9
September 2010 © Datalicious Pty Ltd 10 cookie: zimbio, promo.on, chris.ne, fashion
[ Affinity targe.ng in ac.on ]
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Different type of visitors respond to different ads. By using category affinity targe<ng, response rates are li\ed significantly across products.
Message CTR By Category Affinity
Postpay Prepay Broadb. Business
Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - +
Google: “vodafone omniture case study” or h>p://bit.ly/de70b7
September 2010 © Datalicious Pty Ltd 12 h>p://ww.wesKield.com?data=chris.ne,promo.on
[ Customer profiling in ac.on ]
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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.
Phase Segment A/B Channels Data Points
Awareness Seen this? Social, display, search, etc Default
Considera.on Great feature! Social, search, website, etc
Download, product view
Purchase Intent Great value! Search, site, emails, etc
Cart add, checkout, etc
Up/Cross-‐Sell Add this! Direct mail, emails, etc
Email response, login, etc
[ Developing a targe.ng matrix ]
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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.”
[ Quality content key to success ]
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Campaign response data
[ Combining data sets ]
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Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
[ Behaviours plus transac.ons ]
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one-‐off collec<on of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira.on, etc predic<ve models based on data mining
propensity to buy, churn, etc historical data from previous transac<ons
average order value, points, etc
CRM Profile
Updated Occasionally
+ tracking of purchase funnel stage
browsing, checkout, etc tracking of content preferences
products, brands, features, etc tracking of external campaign responses
search terms, referrers, etc tracking of internal promo<on responses
emails, internal search, etc
Site Behaviour
Updated Con.nuously
[ Sample customer level data ]
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[ Social media as data source ]
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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?
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(influencers only)
(all contacts)
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Appending social data to customer profiles Name, age, gender, occupa.on, loca.on, social profiles and influencer ranking based on email
[ 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
[ Overall volume and influence ]
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Data from
[ Influence and media value ]
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US
UK
AU/NZ
Data from
[ Google data in Australia ]
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Source: h_p://www.hitwise.com/au/datacentre
[ Search at all stages ]
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Source: Inside the Mind of the Searcher, Enquiro 2004
[ Search and brand strength ]
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[ Search and the product lifecycle ]
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Nokia N-‐Series
Apple iPhone
[ Search driving offline crea.ve ]
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Direct mail, email, etc
Facebook Twi>er, etc
[ Mapping out campaign flows ]
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POS kiosks, loyalty cards, etc
CRM program
Home pages, portals, etc
YouTube, blog, etc
Paid search
Organic search
Landing pages, offers, etc
PR, WOM, events, etc
TV, print, radio, etc
C2
C3
= Paid media
= Viral elements
Call center, retail stores, etc
= Coupons, surveys
Display ads, affiliates, etc
C1
People Reached
People Engaged
People Converted
People Delighted
[ Developing a metrics framework ]
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40% 10% 1%
Quan<ta<ve and qualita<ve research data
Website, call center and retail data
Social media data
Media and search data
Social media
[ De-‐duplica.on across channels ]
September 2010 © Datalicious Pty Ltd 32
Banner Ads
Email Blast
Paid Search
Organic Search
$ Bid Mgmt
Ad Server
Email PlaKorm
Google Analy.cs
$
$
$
Central Analy.cs PlaKorm
$
$
$
[ Success a>ribu.on models ]
Banner Ad $100
Email Blast
Paid Search $100
Banner Ad $100
Affiliate Referral $100
Success $100
Success $100
Banner Ad
Paid Search
Organic Search $100
Success $100
Last channel gets all credit
First channel gets all credit
All channels get equal credit
Print Ad $33
Social Media $33
Paid Search $33
Success $100
All channels get par.al credit
Paid Search
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[ Search call to ac.on for offline ]
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September 2010 © Datalicious Pty Ltd 35
[ Understanding channel mix ]
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[ Target Denim ]
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§ 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)
© Datalicious Pty Ltd
[ Key traffic drivers ]
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NB: Removed data from Friday Feb 11th as due to extreme skew
§ The campaign had a huge first day before paid media began which built momentum early
[ 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
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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
[ Campaign comparison ]
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§ Campaign traffic was almost that of Christmas and much higher than the very successful ‘Colours’ campaign
56% of Visits occur in the first 4 days Site Visits
Christmas = Nov 20 to Dec 30, 2009 Colours = Aug 7 – Sep 16, 2009
§ Looking only at campaign incep<on, it did drive higher daily average traffic; 34k to 32k respec<vely
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