An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 57

when certain things happen in a specific order, a specific event has an increased
probability of occurring next.
Sequence modeling techniques analyze paths of events in order to detect
common sequences. Their origin lies in web mining and click stream analysis of
web pages. They began as a way to analyze weblog data in order to understand
the navigation patterns in web sites and identify the browsing trails that end
up in specific pages, for instance purchase checkout pages. The use of these
techniques has been extended and nowadays can be applied to all ''sequence''
business problems.
The techniques can also be used as a means for predicting the next expected
''move'' of the customers or the next phase in a customer's lifecycle. In banking,
they can be applied to identify a series of events or customer interactions that may
be associated with discontinuing the use of a credit card; in telecommunications, for
identifying typical purchase paths that are highly associated with the purchase of a
particular add-on service; and in manufacturing and quality control, for uncovering
signs in the production process that lead to defective products.
The rules generated by association models include antecedents or conditions
and a consequent or conclusion. When antecedents occur in a specific order, it is
likely that they will be followed by the occurrence of the consequent. Their general
format is:

IF (ANTECEDENTS with a specific order) THEN CONSEQUENT

or:

IF (product A and THEN product F and THEN product C and THEN ...) THEN
product D

For example, a rule referring to bank products might be:

IF SAVINGS & THEN CREDIT CARD & THEN SHORT-TERM DEPOSIT → STOCKS

This rule states that bank customers who start their relationship with the
bank as savings account customers, and subsequently acquire a credit card and
a short-term deposit product, present an increased likelihood to invest in stocks.
The likelihood of the consequent, given the antecedents, is expressed by the
confidence value. The confidence value assesses the rule's strength. Support and
confidence measures, which were presented in detail for association models, are
also applicable in sequence models.
The generated sequence models, when used for scoring, provide a set of
predictions denoting the n , for instance the three, most likely next steps, given