Work

A Large-Scale Marketing Model using Variational Bayes Inference for Sparse Transaction Data

Year: 2014

Type: article

Abstract: Large-scale databases in marketing track multiple consumers across multiple product categories.A challenge in modeling these data is the resulting size of the data matrix, which often has thousands of consumers and thousands of choice alternatives with prices and merchandising variables changing over time.We develop a heterogeneous topic model for these data, and employ variational Bayes techniques for estimation that are shown to be accurate in a Monte Carlo simulation study.We find the model to be highly scalable and useful for identifying effective marketing variables for different consumers, and for predicting the choices of infrequent purchasers.

Source: Institutional Repositories DataBase (IRDB)

Authors: Tsukasa Ishigaki, Nobuhiko Terui, Tadahiko Sato, Greg M. Allenby

Language: English

Topic: Consumer Market Behavior and Pricing

Subfield: Marketing

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

Funder: Japan Society for the Promotion of Science

Awards: Organizational Study on The Theory and Development of CRM by Fusing Large-scale Heterogeneous Information, Large-scale personalized marketing modeling for effective use of transaction data

OpenAlex ID: W298956648

FWCI: 0.792

Cites: 26

Cited by: 1

Related to: 10