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Zhangxi Lin ISQS 3358 Texas Tech University. Realtime BI - Online Targeted Advertising . Internet-based Targeted Marketing Targeted Banner Advertising Online Recommender Systems. Agenda. Internet-based Targeted Marketing. - PowerPoint PPT Presentation
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Zhangxi LinISQS 3358Texas Tech University
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Internet-based Targeted Marketing Targeted Banner Advertising Online Recommender Systems
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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.
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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
aggressively.
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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.
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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
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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 sectors)
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When executed properly, behavioral marketing is a highly effective means of reaching and converting your target audience.
Network Behavioral Targeting vs. Non-Targeted Advertising
Behavioral Re-Targeting vs. Non-Targeting Advertising
Source: Advertising.com, 2005 Source: Advertising.com, 2004
Lift in Conversion rate
Advertiser A 167%
Advertiser B 2,232%
Advertiser C 3,130%
Lift in Conversion Rate
Advertiser A 90%
Advertiser B 323%
Advertiser C 105%
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This travel advertiser targeted consumers who previously visited its website in order to drive actual reservations.
Campaign Results
Behavioral Targeting
Impressions 99 million
Clicks 92,223
Bookings 52,936
Conversion Rate 57.4%
PRODUCT PURCHASE
Visitors who had not booked
a reservation received custom ads highlighting
guaranteed rates, seasonal discounts,
new hotel perks and free gifts with an online
booking.
1 out of every 2 people who
clicked on the ad completed a booking.
A hotel booking was generated for
every 2,000 impressions served.
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 folksonomies.
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 consumers
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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 are: 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.
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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 models
Model scores the propensity to click on a vendor’s banner ad
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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).
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LogData Optimum
Decision Model Ads
Repository
AdsPublishingBanners
Ads Management
AdsContents
AdvertisersOTA ServiceProvider
Web Users
RealtimeAds publishing
AdsPublisher
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
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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 www.netflixprize.com.
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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.
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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.)
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continued...
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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