It becomes increasingly important for service industries to understand customer behavior using large-scale data such as POS data. However, limitations exist in a customer model constructed on the basis of such behavioral data alone. This paper presents how we can construct a customer model on the basis of both large-scale purchase data and lifestyle survey data. It proposes a method that reveals the connection between lifestyle and behavior by deducing lifestyle from behavioral data using Random Forests, a machine learning algorithm. Then, It applies the proposed method to an actual mass merchandizers using questionnaires on lifestyle collected and the customer behavioral data (ID-POS Data). It thereby demonstrates the effectiveness of the proposed method and its possible use in supporting managerial decision-making on critical issues such as product selection.
|Journal||IEEJ Transactions on Electronics, Information and Systems|
|Publication status||Published - 2013|
- Computational Customer Model
- Large-scale Data
- Random Forests
ASJC Scopus subject areas
- Electrical and Electronic Engineering