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Digit-Tech Analytics Workshop
 

Digit-Tech Analytics Workshop

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Analytics workshop on how to turn data into actionable insights.

Analytics workshop on how to turn data into actionable insights.

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  • Customer Behavior Isn't LinearIf analysis has taught us in the online marketing, where a 10 percent visit-to-purchase conversion rate is still considered extraordinary, it's that customers don't behave in a linear fashion. Customers' goals don't always align with our direct online revenue goals. Customers change their minds. They get distracted. They lose interest. They save carts, abandon carts, add items to carts, remove items from carts, and sometimes all the above -- and in no particular order. Sometimes they navigate for products, sometimes they search for products. Sometimes they do both in the same visit. So long as customers are people, customer behavior will be dynamic and at times irrational, random, and unexplainable.So why are we trying to fit the dynamic nature of online customer behavior into a linear model? I've heard this question discussed recently in online retailing circles. It will gain momentum as a better model for analyzing customer behavior for e-commerce organizations. http://www.clickz.com/showPage.html?page=3596566
  • Please insert the actual statistics into the text below the graph and point out that this is based on McKinsey research and best practiceAdmit that NDS is not there to make money and there might not be any direct competitors but point out that the above applies for leads as well And although we might have a limited amount of direct competitors we’re competing for attention with other sectorsThe smoother the overall experience is from TV ad over website content to application process the better we can competeUse the actual care careers numbers to make the connection clear

Digit-Tech Analytics Workshop Digit-Tech Analytics Workshop Presentation Transcript

  • [Digital Measurement ]
    Analytics workshop on how to turn data into actionable insights
  • [ Company history ]
    Datalicious was founded in 2007
    Strong Omniture web analytics history
    One-stop data agency with specialist team
    Combination of analysts and developers
    Making data accessible and actionable
    Driving industry best practice
    Evangelizing use of data
    June 2010
    © Datalicious Pty Ltd
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  • [ Challenging clients ]
    June 2010
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  • [ Data driven marketing ]
    June 2010
    © Datalicious Pty Ltd
    4
    Insights
    Reporting
    Data mining and modelling
    Customised dashboards
    Media attribution models
    Market and competitor trends
    Social media monitoring
    Online surveys and polls
    Customer profiling
    Action
    Applications
    Data usage and application
    Marketing automation
    Aprimo, Traction, Inxmail, etc
    Targeting and merchandising
    Internal search optimisation
    CRM strategy and execution
    Testing programs
    Data
    Platforms
    Data collection and processing
    Web analytics solutions
    Omniture, Google Analytics, etc
    Tagless online data capture
    End-to-end data platforms
    IVR and call center reporting
    Single customer view
  • [ Today ]
    Capturing data
    Options, limitations, innovations
    Generating insights
    Process, metrics, examples
    Taking action
    Media, targeting, testing
    June 2010
    © Datalicious Pty Ltd
    5
  • [ Capturing data ]
    101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
    June 2010
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  • [ Digital data is cheap ]
    June 2010
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    Source: Omniture Summit, Matt Belkin, 2007
  • [ Digital data options ]
    June 2010
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    +Social
    Source: Accuracy Whitepaper for web analytics, Brian Clifton, 2008
  • [ On-site analytics tools ]
    June 2010
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    Google: ”forrester wave web analytics pdf” or http://bit.ly/aTLAKT
    Source: Forrester Wave Web Analytics, 2009
  • [ What platform to use ]
    June 2010
    © Datalicious Pty Ltd
    10
    Stage 1: Data
    Stage 2: Insights
    Stage 3: Action
    Data is fully owned in-house, advanced predictive modelling and trigger based marketing, i.e. what will happen and making it happen!
    Sophistication
    Data is being brought in-house, shift towards insights generation and data mining, i.e. why did it happen?
    Third parties control most data, ad hoc reporting only, i.e. what happened?
    Time, Control
  • [ Governance and data integrity ]
    June 2010
    © Datalicious Pty Ltd
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    Source: Omniture Summit, Matt Belkin, 2007
  • © Datalicious Pty Ltd
    [ Free off-site analytics tools ]
    http://www.google.com/trends
    http://www.google.com/sktool
    http://www.google.com/insights/search
    http://www.google.com/webmasters
    http://www.google.com/adplanner
    http://www.google.com/videotargeting
    http://www.keywordspy.com
    http://www.compete.com
    http://www.alexa.com
    http://wiki.kenburbary.com
    June 2010
    12
  • [ Search at all stages ]
    June 2010
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    In Australia Google has a market share of almost 90% of all searches, making it a very large and reliable data sample
    Source: Inside the Mind of the Searcher, Enquiro 2004
  • [ Search call to action for offline ]
    June 2010
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  • [ Client side tracking process ]
    June 2010
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    What if: Someone deletes their cookies? Or uses a device that does not support JavaScript? Or uses two computers (work vs. home)? Or two people use the same computer?
    Source: Google Analytics, Justin Cutroni, 2007
  • [ Tag-less data capture ]
    June 2010
    © Datalicious Pty Ltd
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    Google: “atomic labs”
    www.atomiclabs.com
  • The study examined data from two of the UK’s busiest ecommerce websites, ASDAand 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 overestimated visitors by up to 7.6 times whilst a cookie-based approach overestimated visitors by up to 2.3 times.
    Google: ”red eye cookie report pdf” or http://bit.ly/cszp2o
    [ Overestimation of unique visitors ]
    June 2010
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    Source: White Paper, RedEye, 2007
  • [ Maximise identification points ]
    June 2010
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    Campaign response
    Email subscription
    Online purchase
    Repeat purchase
    Confirmation email
    Email newsletter
    Website login
    Online bill payment
  • June 2010
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    DataliciousSuperCookie
    Persistent Flash cookie that cannot be deleted
  • [ Mobile page headers ]
    June 2010
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    MSISDN = Mobile Number
    Source: Mobile Tracking, Omniture, 2008
  • [ Single-sign on ]
    June 2010
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    Facebook Connect gives your company the following data and more with just one click!
    ID, first name, last name, middle name, picture, affiliations, last profile update, time zone, religion, political interests, interests, sex, birthday, attracted to which sex, why they want to meet someone, home town, relationship status, current location, activities, music interests, tv show interests, education history, work history, family and email
    Need anything else?
  • [ Research online, shop offline ]
    June 2010
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    Google: ”digital future report 2009 pdf” or http://bit.ly/ZkLvr
    Source: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School
  • [ Offline sales driven by online ]
    June 2010
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    Tying offline conversions back to online campaign and research behavior using standard cookie technology by triggering virtual online order confirmation pages for offline sales using email receipts.
    Credit Check Fulfilment
    Phone Orders
    Website.com Research
    Virtual OrderConfirmation
    @
    Retail Orders
    Credit Check Fulfilment
    Website.com Research
    Virtual OrderConfirmation
    @
    Advertising Campaign
    Website.com Research
    Online Orders
    Credit Check Fulfilment
    Online Order Confirmation
    Virtual OrderConfirmation
    @
    Cookie
    Cookie
    Cookie
  • [ Summary: Capturing data ]
    Plenty of data sources and platforms
    Especially search is great free data source
    Maintaining data integrity takes effort
    Cookie technology has its limitations
    New tag-less technologies emerging
    Maximise identification points
    Offline can be tied to online
    June 2010
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  • [ Generating insights ]
    101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
    June 2010
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  • [ Corporate data journey ]
    June 2010
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    Stage 1Data
    Stage 2Insights
    Stage 3Action
    Data is fully owned in-house, advanced predictive modelling and trigger based marketing, i.e. what will happen and making it happen!
    Sophistication
    Data is being brought in-house, shift towards insights generation and data mining, i.e. why did it happen?
    Third parties control most data, ad hoc reporting only, i.e. what happened?
    Time, Control
  • [ The ideal analyst ]
    Business minded
    Setting realistic improvement goals
    Technically savvy
    Bridging gap between business and IT
    Strong sales skills
    Raising awareness for the value of data
    Seniority and experience
    Needs to be taken serious across organisation
    Position within hierarchy
    Able to analyse without loyalty conflict
    June 2010
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  • [ Process is key to success ]
    June 2010
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    Source: Omniture Summit, Matt Belkin, 2007
  • Website, call center and retail data
    Quantitative and qualitative research data
    [ Defining metrics frameworks ]
    June 2010
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    Media and search data
    Social media data
    Social media
  • [ Key metrics by website type ]
    June 2010
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    Source: Omniture Summit, Matt Belkin, 2007
  • [ Conversion funnel 1.0 ]
    June 2010
    Campaign responses
    Conversion funnel
    Product page, add to shopping cart, view shopping cart, cart checkout, payment details, shipping information, order confirmation, etc
    Conversion event
    © Datalicious Pty Ltd
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  • [ Conversion funnel 2.0 ]
    June 2010
    Campaign responses (inbound spokes)
    Offline campaigns, banner ads, email marketing, referrals, organic search, paid search, internal promotions, etc
    Landing page (hub)
    Success events (outbound spokes)
    Bounce rate, add to cart, cart checkout, confirmed order, call back request, registration, product comparison, product review, forward to friend, etc
    © Datalicious Pty Ltd
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  • [ Additional success metrics ]
    June 2010
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    Click Through
    $
    Click Through
    Add To Cart
    $
    Cart Checkout
    ?
    Click Through
    Bounce Rate
    $
    Pages Per Visit
    Video Views
    Click Through
    Call back requests
    Store Searches
    ?
    $
  • June 2010
    © Datalicious Pty Ltd
    Exercise: Metrics framework
    34
  • [ Exercise: Metrics framework ]
    June 2010
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  • [ Exercise: Metrics framework ]
    June 2010
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  • Customer data
    [ Combining data sets ]
    June 2010
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    Web analytics data
    +
    The whole is greater than the sum of its parts
    3rd party data
  • [ Behaviours vs. transactions ]
    June 2010
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    CRM Profile
    Site Behaviour
    one-off collection of demographical data age, gender, address, etc
    customer lifecycle metrics and key datesprofitability, expiration, etc
    predictive models based on data miningpropensity to buy, churn, etc
    historical data from previous transactionsaverage order value, points, etc
    tracking of purchase funnel stagebrowsing, checkout, etc
    tracking of content preferencesproducts, brands, features, etc
    tracking of external campaign responses
    search terms, referrers, etc
    tracking of internal promotion responses
    emails, internal search, etc
    +
    Updated OCCASIONALLY
    Updated continuously
  • [ Store searches vs. actual locations ]
    June 2010
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  • [ Enriching customer profiles ]
    June 2010
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    All you need is an address
    Source: Hitwise, 2006
  • [Hitwise Mosaic segment swing ]
    australia.com vs. newzealand.com
    australia.com vs. bulafiji.com
    June 2010
    © Datalicious Pty Ltd
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    Source: Hitwise, 2006
  • [Hitwise Mosaic segment swing ]
    australia.com vs. newzealand.com
    australia.com vs. newzealand.com
    June 2010
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    Source: Hitwise, 2006
  • [ Single source of truth ]
    June 2010
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    Insights
    Reporting
  • [ De-duplication across channels ]
    June 2010
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    Paid Search
    $
    Bid Mgmt
    Central AnalyticsPlatform
    Banner Ads
    Ad Server
    $
    $
    Email Blast
    Email Platform
    $
    $
    Organic Search
    Google Analytics
    $
    $
  • June 2010
    © Datalicious Pty Ltd
    Thinking outside the box
    45
  • [ Search and brand strength ]
    June 2010
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    46
  • [ Search and the product lifecycle ]
    June 2010
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    Nokia N-Series
    www.google.com/trends
    Apple iPhone
  • [ Search and media planning ]
    June 2010
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    www.google.com/adplanner
  • June 2010
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  • June 2010
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  • June 2010
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    Fiat 500: Online influencing offline
    Google: “slideshare fiat 500 case study” or http://bit.ly/lh7bx
  • [ Search driving offline creative ]
    June 2010
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  • June 2010
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  • June 2010
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    Sentiment analysis: People vs. machine
    Google: “people vs machines debate” or http://bit.ly/8VbtB
  • [ Social metrics and tools ]
    June 2010
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    Google: ”slidesharealtimeter report” or http://bit.ly/c8uYXT
    Source: Social Marketing Analytics, Altimeter, 2010
  • June 2010
    © Datalicious Pty Ltd
    Exercise: Statistical significance
    56
  • June 2010
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    How many survey responses do you need if you have 10,000 customers?
    How many email opens do you need to test 2 subject linesif your subscriber base is 50,000?
    How many orders do you need to test 6 banner executions if you serve 1,000,000 banners
  • June 2010
    © Datalicious Pty Ltd
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    How many survey responses do you need if you have 10,000 customers?
    369 for each question or 369 complete responses
    How many email opens do you need to test 2 subject linesif your subscriber base is 50,000?
    381 per subject line or 381 x 2 = 762 email opens
    How many orders do you need to test 6 banner executions if you serve 1,000,000 banners?
    383 sales per banner execution or 383 x 6 = 2,298 sales
  • [ Summary: Generating insights ]
    Right resources and processes are key
    Define a flexible metrics framework
    Maintain framework to enable comparison
    Combine data sets for hidden insights
    Establish a single (data) source of truth
    Think outside the box and across channels
    Data does not equal significance
    June 2010
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  • [ Taking action ]
    101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
    June 2010
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  • [ How to drive ROI ]
    Increasing revenue
    Increasing overall amount of sales 
    Increasing the average revenue per sale
    Reducing costs
    Increasing media effectiveness
    Increasing website conversion rates
    Increasing online self-service usage
    Improving customer experience
    Reducing steps necessary to complete a task
    Perceived value or quality of the final solution
    June 2010
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  • [ How to drive ROI ]
    June 2010
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    Media or how to optimise the channel mix
    Targeting or how to increasing relevance
    Testing or how to maximise conversion
  • [ Success attribution models ]
    Banner Ad
    Paid Search
    OrganicSearch$100
    Success
    $100
    Last channel gets all credit
    Banner Ad$100
    Email Blast
    Success$100
    First channel gets all credit
    Paid Search
    Paid Search$100
    Banner Ad$100
    Affiliate Referral$100
    Success$100
    All channels get equal credit
    Print Ad$33
    Social Media$33
    Paid Search$33
    Success$100
    All channels get partial credit
    June 2010
    63
    © Datalicious Pty Ltd
  • [ First vs. last click attribution ]
    June 2010
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    Chart shows percentage of channel touch points that lead to a conversion.
    Paid/Organic Search
    Neither first nor last-click measurementwould provide true picture
    Emails/Shopping Engines
  • [ Path to purchase ]
    Banner Click
    SEM Generic
    PartnerSite
    Direct Visit
    $
    Banner View
    June 2010
    65
    © Datalicious Pty Ltd
    SEO Generic
    $
    TVAd
    SEOBranded
    Banner Click
    $
    Print Ad
    Social Media
    Email Update
    Direct Visit
    $
  • [ Forrester media attribution ]
    June 2010
    © Datalicious Pty Ltd
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    Google: ”forrester attribution framework pdf” or http://bit.ly/dnbnzY
    Source: Forrester, 2009
  • [ Customer data journey ]
    June 2010
    © Datalicious Pty Ltd
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    To retention messages
    To transactional data
    From suspect to
    To customer
    prospect
    Time
    Time
    From behavioural data
    From awareness messages
  • June 2010
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  • June 2010
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  • [ Matching segments are key ]
    June 2010
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    On and off-site targeting platforms should use identical triggers to sort visitors into segments
  • [ Off-site targeting platforms ]
    Ad servers
    Google/DoubleClick
    Eyeblaster
    Faciliate
    Atlas
    Etc
    Ad Networks
    Google
    Yahoo
    ValueClick
    Adconian
    Etc
    June 2010
    © Datalicious Pty Ltd
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    http://en.wikipedia.org/wiki/Contextual_advertising, http://hubpages.com/hub/101-Google-Adsense-Alternatives, http://en.wikipedia.org/wiki/Central_ad_server, http://www.adoperationsonline.com/2008/05/23/list-of-ad-servers/,
    http://lists.econsultant.com/top-10-advertising-networks.html, http://www.clickz.com/3633599, http://en.wikipedia.org/wiki/behavioural_targeting
  • [ On-site targeting platforms ]
    Test&Target (Omniture, Offermatica, TouchClarity)
    Memetrics (Accenture)
    Optimost (Autonomy)
    Kefta (Acxiom)
    AudienceScience
    Maxymiser
    Amadesa
    Certona
    SiteSpect
    BTBuckets (free)
    Google/DoubleClick Ad Server (free)
    June 2010
    © Datalicious Pty Ltd
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  • [ Prospect targeting parameters ]
    June 2010
    © Datalicious Pty Ltd
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  • [ Vodafone affinity targeting ]
    June 2010
    © Datalicious Pty Ltd
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    Different type of visitors respond to different ads. By using category affinity targeting, response rates are lifted significantly across products.
  • [ Affinity targeting ]
    Function of behavioural targeting
    Grouping of visitors into major segments
    Based on content and conversion behaviour
    Ease of use vs. reduced targeting ability
    Most common affinities used
    Brand affinity
    Image preference
    Price sensitivity
    Product affinity
    Content affinity
    June 2010
    © Datalicious Pty Ltd
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  • [ Coordinate the experience ]
    June 2010
    © Datalicious Pty Ltd
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    By coordinating the consumer’s end-to-end experience, companies could enjoy revenue increases of 10-20%.
    Google: “get more value from digital marketing” or http://bit.ly/cAtSUN
    Source: McKinsey Quarterly, 2010
  • AvinashKaushik: “The principle of garbage in, garbage out applies here. […] what makes a behaviour targeting platform tick, 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 is key ]
    June 2010
    © Datalicious Pty Ltd
    77
  • June 2010
    © Datalicious Pty Ltd
    Exercise: Targeting matrix
    78
  • [ Exercise: Targeting matrix ]
    June 2010
    © Datalicious Pty Ltd
    79
  • [ Exercise: Targeting matrix ]
    June 2010
    © Datalicious Pty Ltd
    80
  • Google: “change one word double conversion” or http://bit.ly/bpyqFp
    [ClickTale testing case study ]
    June 2010
    © Datalicious Pty Ltd
    81
  • [ Testing platforms ]
    Test&Target (Omniture, Offermatica, TouchClarity)
    Memetrics (Accenture)
    Optimost (Autonomy)
    Kefta (Acxiom)
    Maxymiser
    Amadesa
    SiteSpect
    ClickTale (cheap)
    Unbounce (cheap)
    Google Website Optimiser (free)
    June 2010
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  • [ Summary ]
    There is no magic formula for ROI
    Focus on the entire conversion funnel
    Media attribution is hard but necessary
    Neither first nor last click method works
    Create a coordinated targeted experience
    Content is always king no matter what
    Test, learn and refine continuously
    June 2010
    © Datalicious Pty Ltd
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  • June 2010
    © Datalicious Pty Ltd
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    Contact mecbartens@datalicious.com
    Learn moreblog.datalicious.com
    Follow ustwitter.com/datalicious