Introduction to Behavioural Marketing & Advertising

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    Introduction to Behavioural Marketing & Advertising - Presentation Transcript

    1. Behavioural Targeting: Audience based Advertising
    2. Introduction
      • What is behavioral targeting (BT)? Anonymously targeting web users based on observed previous behavior
      • Behavioral Targeting is a rule based model - e.g. if user does x, present them with y – based on applying recency and frequency (RF) to consumer data acquired over time
        • Recency: how recently a user has performed an action
        • Frequency: how often a user has performed an action
      • Behavioural Targeting works on the basis of three questions:
        • Where are they?
        • Where they before?
        • What do they do?
      • Behavioral Targeting is not a media buying model but an approach that assimilates technology, media, creative and customer insight to increase
    3. Pro’s and Con’s of Behavioural Targeting
      • Target people, not pages
      • Narrowcasting as opposed to broadcasting
      • Increase relevance of communications by marrying profiling, media and creative
        • Standard model of is driven by c ontent-based advertising - traditional advertising’s obsession with editorial adjacency
      • Extends the long tail of your communication, by shifting the focus from depth of content (e.g. New car section) to the depth of user’s consumption
        • Advertisers currently direct 96 percent of online display adspend to just 30 percent of overall Web traffic
      Opportunities Challenges
      • Need to distinguish between intent-based and a routine-based behaviors (recency is key)
      • On-going optimization given the constant contraction and expansion of behavioral segments
      • Requires dedicated investment in creative
        • Front end targeting filters need to be matched with back-end creative assets
      • Limited reach across certain industry verticals
        • Only about 33.8% of websites in the US currently offer behavioural targeting *
      • While up-lift in engagement rates will be high (click through) up-lift in conversion moderate
        • Due to last click attribution rule
      * Source: OMMA Magazine August 2007
    4. Ways of delivering Behavioural Targeting
      • While all forms of behavioural targeting are based on the same principle, there are different ways of delivering behavioural targeting
      Network e.g. Tacoda Ad Server e.g. Touch Clarity Computer e.g. uTarget Publisher e.g. Guardian
    5. How Behavioural Works
    6. Publisher Based
      • Individual publishers track and segments based on users 1) surfing behavior and/or 2) registration data
      • Publishers implement third party targeting platforms (decision engine) - Point Dexter, Tacoma, Wunderloop, Revenue Science - into their ad inventory system
        • Increase yield on existing ad inventory by creating custom segments – e.g. Silver Surfer Travel Enthusiasts
        • Create new revenue streams by aggregating multiple streams of data
      • The largest publishers offering behavioral targeting in the UK include:
        • Yahoo, MSN, Lycos, Guardian, FT.com, OAG.com, CondeNast Traveller, Reuters, Associated New Media (travelmail.co.uk), EMAP, Channel 4
    7. Publisher Case Study
      • Guardian unlimited, leveraging Revenue Science targeting
      • platform provides a series of behavioral targeting options
      • including:
      • The content they have read
      • The sections and pages that individuals visit on Guardian Unlimited and for the first time
      • The words that appear on the pages they have read
      • The number of relevant pages the have read
      • How often they read these pages
      • How recently they were reading those pages
      • Registration data
      • Registration data such as job title or seniority
      • Frequency of visit
      • The industry they work in
      • Domain details
      • FTSE 350 status
    8. Publisher Case Study
      • Leveraging MSN Passport registration data, keyword
      • search behavior, site visits and overall recency;
      • MSN users are placed in specific travel segments:
      • Hotel seekers
        • Users that have shown an interest or looked for hotels
      • Cruise seekers
        • Users that have shown an interest or looked for cruises
      • Airfare seekers
        • Users that have shown an interest or looked for airfare
    9. Network Based
      • Networks track and segments based on users aggregated from different sites based on their surfing behavior and click behavior
      • Networks implement in-house or third party targeting platforms (decision engine) - Point Dexter, Tacoma, Wunderloop, Revenue Science - into their ad inventory system
        • Increase yield or eCPM on brokered ad inventory by creating custom segments – e.g. Silver Surfer Travel Enthusiasts
        • Create new revenue streams by aggregating multiple streams of data
      • The largest networks offering behavioral targeting in the UK include:
        • Tacoma, Drive PM, Adviva, Advertising.com
    10. Publisher Case Study
      • Tacoda leverages site visitation behavior of its users and
      • sites against 350 behavioral segments (32 sub segments)
      • Cookie is dropped when a user enters the Tacoda network of sites
      • Identify behavior by analyzing the URL string
      • User is profiled based on recent consumption
        • 2 to 4 visits to relevant sites across a 30, 14 or 7 day period
        • The sections and pages that individuals visit on Guardian Unlimited and for the first time
      • Spectrum: run of behavioural network – run across all segments and
      • optimise based on response (£1.50 CPM to £1.85)
      • Audience Point: buy a specific segment or profile (£5 CPM - £10 CPM)
      • – e.g. Business Travelers, Holidays Takers
      • Spectrum + : run of behavioral network being optimized against
      • Tacoda tag on the booking confirmation page
    11. Computer Based
      • Track and segment users based on site visitation captured by application downloaded by user (opt-in ad software) into access device
        • Application sends data to a server to create a historical profile; and uses this data to target content or advertising
      • Focus is shifting from stand-alone “push” software/desktop applications to “pull” personalization engines
        • Google Toolbar
        • Claria Axon
      • Depth of behavioral targeting is fairly limited – URL typed or search query (serve a standard or rich media pop-under)
      • The largest providers include Zango and uTarget
    12. Publisher Case Study
      • Utarget provides access to over 650 UK publishers
      • delivering a series of rich media and standard
      • Formats based on a series of criteria, captured through
      • application including:
      • Entry URL
      • Exit URL
      • Keyword searched
      • Opportunity to serve a variety of ad formats based on users
      • URL and search term consumption:
      • Interstitial (between two pages of content)
      • Pop Under (under browser window)
        • Video Pop Under
        • Flash Pop Under
        • Landing Page Pop Under
      • Customized strip on top of browser page
    13. Ad-Server
      • Segment and target users based on cookie data collected through 1) front-end ad server - e.g. ATLAS or 2) on-site based ad server - e.g. Omniture
        • Ad or site interaction optimization
      • Front-end ad server behavioral: leverage ad-serving platform data to serve inventory based on specific behavioral rules
        • Build segments based on users interaction and serve a specific creative into existing ad inventory (free of charge)
          • Re-targeting messaging
          • Story Board messaging
      • Site-end ad server behavioural: implement optimization engine (Touch Local ) into existing site analytics tool (Omniture)
        • Deliver content and offers to users visiting Avis.co.uk on real-time and determined on their past behaviour
    14. Re-Targeting (Network or Ad Server)
      • User visits bt.com/btvision
      • Clicks on flash panel, with an embedded 1x1 ad-server pixel (used to track conversions and serve ads)
      • Action tag cookies user based on behaviour defined
      Site B
      • Cookie ID appears within 1) a site part of the media BT Vision plan or 2) ad network
      • Segment criteria identified
      • User served bespoke BT Vision ad relative to their previous declared interest
      User: 1 st session Same user: new session Site A Site C … .site x Cookied user meets segment criteria e.g. Clicked on Movie panel but did not book Segment 1 Segment 2 Segment 3

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