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ACM SIGKDD 2006 Tutorial
Data Analytics for Marketing Decision Support
August 20th, 2006, Philadelphia, PA
Tutorial Co-Organizers: Saharon Rosset and Naoki Abe
Introduction
Today, with increasing amounts of business data accumulated within
enterprises and wider availability of data management infrastructure,
the use of analytical tools to support marketing decisions is
becoming commonly acknowledged as key to success in a wide range of
business areas.
One particularly important challenge one faces when delivering
marketing decision support systems is that of understanding the
true value of customers, beyond the currently observed value.
Some key issues that arise when attempting to meet this challenge are:
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Customer targeting for the so-called whitespace,
i.e., those entities that are currently non-customers of the enterprise
in question.
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Customer lifetime value estimation, i.e., estimating the expected
value of a customer over the course of its long term interaction with
the enterprise.
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Customer wallet estimation, i.e.,
estimating the potential needs, and hence value, of a customer,
inclusive of the portion currently spent on purchases from the competitors.
As the expectation and dependency on data mining tools grows in
marketing support practices, so do the challenges and issues, and the
gap widens between what is solvable by straightforward applications of
analytical methods and what really needs to be solved for the
business problem at hand. This tutorial aims at clarifying the
nature of this gap, and describing what solutions there are to
bridge it.
The tutorial will consist of three parts. First, we will give an
overview of the type of challenges that are often faced by data
mining practitioners when applying analytic tools to marketing
decision support problems. We will then review a number of general
methodologies that can be used to address and partly solve these problems,
either by solving them under restrictive assumptions, or by requiring
further input for quantities that cannot be estimated from the
data. Finally, we will describe in detail a number of case
studies, in order to illustrate how one goes about designing
a practically useful solution to a real world marketing decision
support problem.
The Challenges
In marketing decision support problems, one wishes to use data
analytics to help obtain better understanding of the effects of marketing
actions, and ultimately, make better marketing decisions. The data on which
analytics are applied, however, may not contain all necessary information.
In particular, the effect of marketing actions on future,
potentially long-term, behavior of a customer is not directly observed,
and the behavior of the customer beyond its interaction with the
enterprise collecting the data is typically unobserved.
(Some exceptions exist to this rule, as is the case, for example,
with credit rating firms). This mismatch between the goal of analytics
and the data is the source of much of the challenge that we wish
to address in this tutorial. Specifically:
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The data may not contain information on the decisions/actions that
one is trying to optimize, thereby making it difficult to
estimate the effect of decisions/actions from the data.
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The data may not contain direct information on the objective
of marketing decision making, such as customer lifetime value
and customer wallet.
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The data may have been collected in a different context and thus
straightforward application of estimation methods would result in
biased estimation.
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Correlations observed in the data may not necessarily correspond
to causality, and hence the results of analysis do not give rise
to actionable rules for marketing optimization.
There are many other issues in applying data mining to business
analytics in general, such as the issues of data quality and cleansing
and that of stationarity of distributions. In this tutorial, we focus on
the challenges listed above, which are specific to the goal of
supporting optimal marketing decision making.
Methodologies
These problems have been encountered by many data mining experts,
researchers and practitioners, and solutions exist with varying
degrees of sophistication and effectiveness. Some methodologies
that are of direct relevance include:
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Markov Decision Processes (MDP) and reinforcement learning
methods, which resolve credit assignment of marketing actions to
their delayed effects, and hence allow modeling of long-term effects
of those actions.
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Bayesian Networks, which formalize constraints on possible causal effects
by setting a network structure. This approach allows
us to make optimal use of available pieces of information to make
inference on unobserved quantities, such as the customer wallet.
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Integration of expert knowledge and data analytics, allowing human
experts to specify parameters that cannot be learned purely from
available data. Bayesian methods can create a smooth combination,
where priors are specified that can be ``over-ridden'' by evidence
from data, if available.
We plan to review in some detail these approaches
and their effectiveness in the present context of
marketing decision support problems.
We will also touch upon some of the other methodologies that are
applicable to this domain, when we widen the scope of issues and problems:
%albeit to lesser extent to the specific problems illustrated above:
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Controlled experiments, where the possible business actions are
played out on appropriately chosen sub-populations, to give
``unbiased'' evaluation of the success of these actions. In
particular, active learning methods allow the dynamic design of
controlled experiments to more efficiently collect data for
analysis.
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Bias correction methods, which allow for
correction of biases introduced by data sampled in a different context.
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Cost sensitive learning methods, which allow the cost aspect of
the marketing problem to be considered, and better guide the
estimation process and outcomes.
Case Studies
As noted in Introduction, correct application of data mining
technology is critical to the success of marketing support systems and
practices.
In this tutorial, we will select at least three case studies based on our
experience in this area, to illustrate this point.
We will describe in detail the challenges faced and methods
applied in the following real world applications (we will
probably choose three of these, as time dictates):
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Application of the Markov Decision Processes (MDP) approach to
address customer life time value modeling and marketing
optimization in the retails industry.
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Modeling of unobserved customer wallets through a couple of different
approaches: quantile regression and integration of
domain knowledge via efficient Bayesian network inference.
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Application of data-enhanced modeling techniques to the problem of
optimizing sales targeting policies for acquiring ``white space''
customers for software products.
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Integration of expert knowledge and data mining to model the
effect of marketing actions on customer life time value in a
telecom environment.
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