Effective Targeting
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Transcript of Effective Targeting
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> Effec've Targe'ng < Coordinate the user experience
to boost conversions
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> Short but sharp history
§ Datalicious was founded late 2007 § Strong Omniture web analy?cs history § Now 360 data agency with specialist team § Combina?on of analysts and developers § Carefully selected best of breed partners § Driving industry best prac?ce (ADMA) § Turning data into ac?onable insights § Execu?ng smart data driven campaigns
June 2010 © Datalicious Pty Ltd 2
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> Smart data driven marke'ng
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Media A>ribu'on & Modeling
Op'mise channel mix, predict sales
Tes'ng & Op'misa'on Remove barriers, drive sales
Boos'ng ROI
Targeted Direct Marke'ng Increase relevance, reduce churn
“Using data to widen the funnel”
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> Wide range of data services
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Data PlaKorms Data collec'on and processing Web analy'cs solu'ons Omniture, Google Analy'cs, etc Tag-‐less online data capture End-‐to-‐end data plaKorms IVR and call center repor'ng Single customer view
Insights Analy'cs Data mining and modelling Customised dashboards Tableau, SpoKire, SPSS, etc Media a>ribu'on models Market and compe'tor trends Social media monitoring Customer profiling
Ac'on Campaigns Data usage and applica'on Marke'ng automa'on Alterian, SiteCore, Inxmail, etc Targe'ng and merchandising Internal search op'misa'on CRM strategy and execu'on Tes'ng programs
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> Clients across all industries
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Ques'ons? Tweet @datalicious
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The right message Via the right channel To the right person At the right ?me
Targe'ng
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Capture internet traffic Capture 50-‐100% of fair market share of traffic
Increase consumer engagement Exceed 50% of best compe?tor’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
> Increase revenue by 10-‐20%
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> New consumer decision journey
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The consumer decision process is changing from linear to circular.
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> New consumer decision journey
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The consumer decision process is changing from linear to circular.
Change increases the importance of experience during research phase.
Online research
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> 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
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> 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
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Off-‐site targe?ng
On-‐site targe?ng
Profile targe?ng
> Integra'ng targe'ng plaKorms
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Campaign response data
> Combining data sources
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Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
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> Transac'ons plus behaviours
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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
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> Sample customer level data
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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 overes?mated visitors by up to 7.6 ?mes whilst a cookie-‐based approach overes'mated visitors by up to 2.3 'mes.
> Unique visitor overes'ma'on
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Source: White Paper, RedEye, 2007
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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 iden?fica?on through Cookies
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> Customer profiling in ac'on
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Using website and email responses to learn a lifle bite more about
subscribers at every touch point to keep
refining profiles and messages.
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On-‐site segments
Off-‐site segments
> Combining ad plaKorms
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CRM
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> The Datalicious SuperTag
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SuperTag
Ad Servers
Paid Search
Affiliate Programs
Behavioral Targe'ng
A/B Tes'ng Heat Maps
Live Chat
Web Analy'cs
Media A>ribu'on
Easily implement and update any tag on any websites without IT involvement. De-‐duplicate conversions and collect media afribu?on data to boost return on ad spend. Implement complex re-‐targe?ng strategies across plagorms to increase response rates. Enable advanced features such heat maps, tes?ng and live chat to op?mise conversions.
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Apple iPhone 4
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Apple iPhone 4
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> Affinity re-‐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 lihed 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
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> Ad-‐sequencing in ac'on
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Marke?ng is about telling stories and
stories are not sta?c but evolve over ?me
Ad-‐sequencing can help to evolve stories over ?me the more users engage with ads
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> Sample site visitor composi'on
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30% exis'ng customers with extensive profile including transac?onal history of which maybe 50% can actually be iden?fied as individuals
30% new visitors with no previous website history aside from campaign or referrer data of which maybe 50% is useful
10% serious prospects with limited profile data
30% repeat visitors with referral data and some website history allowing 50% to be segmented by content affinity
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> Search call to ac'on for offline
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> PURLs boos'ng DM response rates
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Text
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> Unique phone numbers
June 2010 © Datalicious Pty Ltd 33
2 out of 3 callers hang up as they cannot get their informa?on fast enough. Unique phone numbers can help improve call experience.
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Purchase Cycle
Segments: Colour, price, product affinity, etc
Media Channels
Data Points
Default, awareness
Have you seen A?
Have you seen B?
Display, search, etc Default
Research, considera'on
A has great features!
B has great features!
Search, website, etc
Ad clicks, prod views
Purchase intent
A delivers great value!
B delivers great value!
Website, emails, etc
Cart adds, checkouts
Reten'on, up/cross-‐sell
Why not buy B?
Why not buy A?
Direct mails, emails, etc
Email clicks, logins, etc
> Developing a targe'ng matrix
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> Quality content is key
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.”
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> Google Ngram: Privacy
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Collec'ng data for the sake of it or to add value to customers?
37
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June 2010 © Datalicious Pty Ltd 38
Contact me [email protected]
Learn more
blog.datalicious.com
Follow me twi>er.com/datalicious
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Data > Insights > Ac'on
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