Maximizing Information Liquidity in Electronic Commerce
Vasant Dhar and Arun Sundararajan
Abstract:
The emerging bottleneck in electronic commerce is
that of converting vast amounts of customer behavior data into
useful information. We view this as a problem of maximizing
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 noncustomers
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?” 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.
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