Preference Analysis and Default Optimization in Web-based Product Configuration Systems

Abstract

Today, more and more companies are providing web-based product configuration systems in order to better meet individual customer preferences. In many cases, pre-defined product specifications are additionally offered to facilitate the corresponding choice decisions. Against this background, we present a Poisson regression approach for analyzing customer preferences and a genetic algorithm for determining preferencebased default products. The basis for this is transaction data, as they are automatically generated when configuring a product online. The potentials of the suggested methodology are demonstrated by means of two case studies referring to different product categories.

Determining the attractiveness of product attributes in consumer goods markets using POS scanner data

“…Here, both the use of data as it is provided by online product configuration systems (Decker and Scholz 2009), e.g. by car or computer manufactures, and the use of online customer reviews, e.g.…”