Chapter 6 by yurtgc548


									Zhangxi Lin
ISQS 3358
Texas Tech University

 Internet-based Targeted Marketing
 Targeted Banner Advertising

 Online Recommender Systems

   In December 2005, Forrester surveyed 371 marketing
    technology decision-makers and influencers to
    investigate trends in marketing technology adoption
    and spending.
   Respondents hail from six major industry groups, and
    two-thirds work for firms whose annual revenues in
    2005 exceeded $1 billion.
     Marketing technology adoption is widespread.
     Marketers say they need a more comprehensive
      application suite.
     Vendors aren’t delivering yet.

   Since 2003, budgets have crept steadily upward and,
    on average, 2006 budgets are up 7% over 2005. But
    spending varies significantly by company size and
    industry. Specifically:
       The largest and smallest firms are scaling back slightly.
       Technology followers are putting cash behind their intentions.
       As a percentage of revenue, retailers spend the most on
        marketing technology.
       B2B firms are growing marketing technology spend

   In 2006, the advertising spending was $16.8 billion an
    increase of 34% from that of 2005 (IAB 2007).
   According to DoubleClick (2005)
       Limited online advertising publishing resources because of
        limited online users’ capability to view growing number of web
        pages (DoubleClick Research 2005)
       Online targeted advertising is a seller market
   Online targeted advertising is emerging as a new trend.
       In March 2007, China’s largest advertising company by
        advertising revenue, Focus Holding Ltd agreed to buy Chinese
        leading online firm Allyes Information Technology Co. Ltd for
        $225 million.
       In April 2007, Google Inc. announced a definitive agreement to
        acquire DoubleClick for $3.1 billion.

   Users know what they want
       Users purchased certain items from certain websites
            We can apply real-time customized marketing solutions (see the process
             map later)
       Users did not purchase, but click through some links
            Mining the click-through streams of the customers, and figure out the
             needs----behavioral targeting
   Users do not know what they want---behavioral targeting
       Collecting information online (such as the blogs, discussions boards
        in a community)
       Segment/target/position strategy
       We can potentially build a database profiling the online users
   How to design (create) ads to make it appeal to end users

   For advertisers
     Help to drive immediate responses (or increased
      sales) to their advertisements
     Help to build branding for the advertisers

   For publishers
     Maximize  the value of high-quality ad inventory
      space (differential services for different site

          When executed properly, behavioral marketing is a highly
      effective means of reaching and converting your target audience.

       Network Behavioral Targeting                Behavioral Re-Targeting vs.
       vs. Non-Targeted Advertising                Non-Targeting Advertising

                 Lift in Conversion Rate                  Lift in Conversion rate

             Advertiser A         90%                 Advertiser A         167%

             Advertiser B        323%                 Advertiser B         2,232%

             Advertiser C        105%                 Advertiser C         3,130%

Source:, 2005              Source:, 2004

                  PRODUCT PURCHASE

      This travel advertiser targeted consumers who previously
       visited its website in order to drive actual reservations.

                       Campaign       Behavioral
     Visitors who       Results       Targeting         A hotel booking
   had not booked                                      was generated for
                                                         every 2,000
    a reservation     Impressions      99 million    impressions served.
  received custom
  ads highlighting
 guaranteed rates,       Clicks         92,223
seasonal discounts,
  new hotel perks
                                                        1 out of every
    and free gifts     Bookings         52,936           2 people who
    with an online                                     clicked on the ad
       booking.       Conversion                     completed a booking.

   Web 2.0
       Aims to facilitate communication, secure information sharing, interoperability, and
        collaboration on the World Wide Web. Web 2.0 concepts have led to the
        development and evolution of web-based communities, hosted services, and
        applications; such as social-networking sites, video-sharing sites, wikis, blogs, and
   Targeted advertising
       Targeted advertising is a type of advertising whereby ads are placed so as to
        reach consumers based on various traits such as demographics, purchase
        history, or observed behavior. Two principal forms of targeted interactive
        advertising are behavioral targeting and contextual advertising.
   Massive customization
       Delivering diversified and customized services online to a large population of
        consumers with different preferences
   User driven services
       A kind of Web 2.0 business model for delivering online services generated by

   Targeting the ads to a shadowy Internet population and
    measuring the success of ads is challenging because failure of
    a banner ad has many overlapping causes. Sources of failure
       poor design, poor placement on Web page, poor choice of Web site for
        placement, poor choice of Web pages within a Web site for placement,
        poor dynamic qualities with respect to repeated page views, poor
        customization tying banner ad to Web site where placement occurs, and
        inadequate oversight by the hosting Web site.
   Seasoned Web surfers grow weary of banner ads that disguise
    themselves as interactive components of a Web page, for
    example, offering multiple-choice answers to a question, only to
    have an interactive click result in being transported to another
    Web site.

   Model can be built using
       Web log data
       Registration data
       Vendor data (may not be required)
   One model with indicator for banner ad/vendor selected
   Multiple models, one for each vendor
       Overlapping data if page sequences are included, because
        “did not click” entries will have common elements in all
   Model scores the propensity to click on a vendor’s
    banner ad

   Cost per thousand impression (CPM)
   Cost-per-Action (CPA)
   Cost-per-Sale (CPS)
   Cost-per-Lead (CPL), and
   Hybrid-Cost-per-Action (HCPA).
       The HCPA model uses two or more different pricing models with
        compound pricing schemes.

   Many common pricing models are based on cost per
    click (CPC) while non targeted advertising model is
    based on cost per thousand impression (CPM).
Web Users
                            Optimum                                 Ads
                          Decision Model                          Contents
         Ads                               Ads publishing
      Repository          Ads Management

            OTA Service                                 Advertisers

   Publishers have the opportunity to maintain their
    business CPM pricing model.
   Advertisers are in charge of CPC/CPA pricing model
   With the help of OSP, advertisers are able to locate
    effective publishers.
   The OSP provides valuable information to advertisers
    about publishers reliability.
   OSP’s database records online visitors click-through
    flow so as to construct an optimal decision model to
    select appropriate advertisements for online visitors

   LOS GATOS, Calif., October 2, 2006 – Netflix, Inc. (Nasdaq: NFLX), the
    world's largest online movie rental service, today announced the creation of
    the Netflix Prize, an award of one million dollars to the first person who can
    achieve certain accuracy goals in recommending movies based on personal
    preferences. The company also made available to contestants 100 million
    anonymous movie ratings ranging from one to five stars, the largest such
    data set ever released.
   The threshold required to win the Netflix Prize is a 10 percent improvement
    in accuracy over the current Netflix recommendation system. If no one wins
    the grand prize this year, the company said it will award a $50,000 progress
    prize to whoever makes the most significant advancement toward the goal
    and will award a progress prize annually until someone wins the grand prize.
   Complete details for registering and competing for the Netflix Prize are
    available at

 As   a customer service
   Customers  looking for products in the book, movie, or
    music categories are often looking for entertainment.
    Most would view recommendations as a plus.
   Customers presented with appealing recommendations
    do not have to resort to tedious searches.
 As   a cross-sell opportunity
   Customers    who intend to only buy one movie may find
       the recommended choices too hard to resist.

   For customers visiting a retail Web site, use
    information from previous purchases to recommend
       Books, Music CDs, Movies
   An “intelligent” music player: plays music specifically
    selected by user, when music has finished and user
    has not made a selection in over L seconds, the player
    makes a selection for the user based on previous
    selections the user has made.
   A news service that provides a personalized custom
    virtual newspaper to the subscriber based on past
    news article preferences. (These are usually content-
    based rather than collaborative.)
   Personalize a user’s home page with “interesting” links,
    with links based on a recommender system algorithm that
    recommends links that should be interesting to the user.
   Send a robot out looking for specific information, score
    each Web page using a recommender algorithm, and then
    return the K most interesting Web pages sorted by
    descending score (search engine applications).
   Index a library of information based on recommender
    system scores.


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