An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 58

the observed antecedents. Predictions are sorted in terms of their confidence and
may indicate for example the top three next product suggestions for each customer
according to his or her recorded path of product purchasing to date.
Sequence models require the presence of an ID field to monitor the events of
the same individual over time. The sequence data could be tabular or transactional,
in a format similar to the one presented for association modeling. Fields required
for the analysis involve: content(s) field(s), an analysis ID field, and a time
field. Content(s) fields denote the occurrence of events of interest, for instance
purchased products or web pages viewed during a visit to a site. The analysis
ID field determines the level of analysis, for instance whether the revealed
sequence patterns would refer to customers, transactions, or web visits, based on
appropriately prepared weblog files. The time field records the time of the events
and is required so that the algorithm can track the occurrence order. A typical
transactional modeling dataset, recording customer purchases over time, is given
in Table 2.13.
A derived association rule is displayed in Table 2.14.

Table 2.13 A transactional modeling data set for association
modeling.

Input-output field

Analysis ID field Time field Content field

Customer ID Acquisition time Products

101 30 June 2007 Product 1

101 12 August 2007 Product 3

101 20 December 2008 Product 4

102 10 September 2008 Product 3

102 12 September 2008 Product 5

102 20 January 2009 Product 5

103 30 January 2009 Product 1

104 10 January 2009 Product 1

104 10 January 2009 Product 3

104 10 January 2009 Product 4

105 10 January 2009 Product 1

105 10 February 2009 Product 5

106 30 June 2007 Product 1

106 12 August 2007 Product 3

106 20 December 2008 Product 4

107 30 June 2007 Product 2

107 12 August 2007 Product 1

107 20 December 2008 Product 3