Abstract—This paper describes a method for computing the
potential profit of a sales item from cross-selling relationships
produced by association rule mining. This method generates a
true ranking by which most valuable ite ms are top-ranked as
contributing to the increase of total profits even if each item is
unprofitable. Such ranking is effective in real situations where
some items are loss leaders in daily cross-selling. Unprofitable
items in the head of a rule are likely valuable for selling more
profitable items. Such potential profit is simply defined and
computed in terms of the confidence factors of association rules,
thus, efficient and easy implementation is possible. Moreover,
presentation by ranking is simple enough to suggest a
marketing strategy for sales promotion and advertising.
Index Terms—Association rule, sales analysis, cross-selling relationships.
The authors are with Tokyo University of Science, Japan (e-mail: firstname.lastname@example.org).
Cite:Tatsuya Mori, Katsutoshi Kanamori, and Hayato Ohwada, "Computing the Potential Profit of a Sales Item from Cross-Selling Relationships," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 754-757, 2012.