An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 56

Association vs. Classification Models for Product Suggestions
As described above, the association models can be used for cross selling and
for identification of the next best offers for each customer. Although useful,
association modeling is not the ideal approach for next best product cam-
paigns, mainly because they do not take into account customer evolvement
and possible changes in the product mix over time.
A recommended approach would be to analyze the profile of customers
before the uptake of a product to identify the characteristics that have caused
the event and are not the result of the event. This approach is feasible by using
either test campaign data or historical data. For instance, an organization
might conduct a pilot campaign among a sample of customers not owning
a specific product that it wants to promote, and mine the collected results
to identify the profile of customers most likely to respond to the product
offer. Alternatively, it can use historical data, and analyze the profile of
those customers who recently acquired the specific product. Both these
approaches require the application of a classification model to effectively
estimate acquisition propensities.
Therefore, a set of separate classification models for each product and
a procedure that would combine the estimated propensities into a next best
offer strategy are a more efficient approach than a set of association rules.
Most association models include categorical and specifically binary (flag or
dichotomous) fields, which typically denote product possession or purchase. We
can also include supplementary fields, like demographics, in order to enhance
the antecedent part of the rules and enrich the results. These fields must also be
categorical, although specific algorithms, like GRI (Generalized Rule Induction),
can also handle continuous supplementary fields. The a priori algorithm is perhaps
the most widely used association modeling technique.
DISCOVERING EVENT SEQUENCES WITH SEQUENCE MODELING
TECHNIQUES
Sequence modeling techniques are used to identify associations of events/
purchases/attributes over time. They take into account the order of events and
detect sequential associations that lead to specific outcomes. They generate rules
analogous to association models but with one difference: a sequence of antecedent
events is strongly associated with the occurrence of a consequent. In other words,