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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14288
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| Title: | Customer Interaction Patterns in Electronic Commerce: Maximizing
Information Liquidity for Adaptive Decision Making |
| Authors: | Dhar, Vasant
Sundararajan, Arun |
| Issue Date: | Nov-1999 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-99-17 |
| Abstract: | Electronic commerce is resulting in unprecedented amounts of transaction
and behavior data that are available to organizations. The emerging
bottleneck is that of converting this data into useful information, or
that of maximizing the information liquidity - the rate at which
organizations are able to transform the inherent information in a data
set into an economically valuable action. We describe how to overcome
this bottleneck, by presenting a model for maximizing information
liquidity in electronic commerce. Our model is usable in a variety of
situations. Specifically, when a large amount of transaction data
already exists, the model is able to exploit this data to generate rules
describing preferences that can be used to classify behaviors, and to
subsequently map behaviors of non-customers into known ones.
Alternatively, where the predominant data available are about behaviors,
the model can be used to cluster these behaviors and combine the
resulting clusters with available transaction data to generate rules
describing preferences. In both cases, the central question addressed is
"when do I have enough information to make a meaningful
offer?â Acting too early can result in inappropriate offers,
while acting too late can result in missed opportunities. Good
information and timing are therefore critical; the model in this paper
is a first step in this direction. |
| URI: | http://hdl.handle.net/2451/14288 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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