Advanced database marketing is designed to ascertain individual customers’ market responses with a discount or display of widely various products from transaction data. However, transaction data recorded in a supermarket or electric commerce are fundamentally sparse because most customers purchase only a few products from all products in shops. Existing methods are not applicable to elucidate the personalized response because of a lack of sample size of purchased data. This paper proposes a personalized market response estimation method for a wide set of customers and products from these sparse data. The method compresses a sparse transaction data with information related to response to marketing variables into a reduced-dimensional space for feasible parameter estimation. Then, they are decompressed into original space using augmented latent variables to obtain individual response parameters. Results show that the method can find suitable marketing promotions for individual customers to every analyzed product.
|Number of pages||16|
|Journal||International Journal of Data Science and Analytics|
|Publication status||Published - 2018 Jun 1|
- Database marketing
- Hierarchical Bayes model
- Marketing variables
- Topic modeling