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Activity Number:
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168
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Type:
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Invited
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Date/Time:
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Monday, August 4, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and Marketing
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| Abstract - #300355 |
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Title:
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Modeling Consumer Search for Making Online Advertising Decisions
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Author(s):
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Alan Montgomery*+
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Companies:
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Carnegie Mellon University
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Address:
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Tepper School of Business, Pittsburgh, PA, 15241,
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Keywords:
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Clickstream ; Bayesian Models ; Purchase Conversion ; Text Modeling ; Marketing
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Abstract:
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Many consumers begin their purchase process at search engines such as Google, Yahoo, or MSN instead of traditional retailers. Consumers rely upon the search results provided by these engines along with paid advertising to make decisions about what sites to visit and subsequently which products to purchase. In this study we propose a statistical model that predicts consumer search and the probability of purchase using clickstream data collected from an online sample of consumers. A challenge in analyzing this data is the textual nature of the search strings and the scarcity of many search terms. We also consider how consumers will search based upon the specificity of the search term. We illustrate how this model can be used to aid advertisers in making decisions about how much to bid, what phrase to bid upon, and the appropriate landing page for the consumer once they enter the web site.
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