Bayesian state space modeling approach for measuring the effectiveness of marketing activities and baseline sales from POS data

Tomohiro Ando

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Analysis of Point of Sales (POS) data is an important research area of marketing science and knowledge discovery, which may enable marketing managers to attain the effective marketing activities. To measure the effectiveness of marketing activities and baseline sales, we develop the multivariate time series modeling method in the framework of a general state space model. A multivariate Poisson model and a multivariate correlated auto-regressive model are used for a system model and an observation model. The Bayesian approach via Markov Chain Monte Carlo (MCMC) algorithm is employed for estimating model parameters. To evaluate the goodness of the estimated models, the Bayesian predictive information criterion is utilized. The proposed model is evaluated with its application to actual POS data.

Original languageEnglish
Title of host publicationProceedings - Sixth International Conference on Data Mining, ICDM 2006
Pages21-32
Number of pages12
DOIs
Publication statusPublished - 2006 Dec 1
Event6th International Conference on Data Mining, ICDM 2006 - Hong Kong, China
Duration: 2006 Dec 182006 Dec 22

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other6th International Conference on Data Mining, ICDM 2006
CountryChina
CityHong Kong
Period06/12/1806/12/22

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Ando, T. (2006). Bayesian state space modeling approach for measuring the effectiveness of marketing activities and baseline sales from POS data. In Proceedings - Sixth International Conference on Data Mining, ICDM 2006 (pp. 21-32). [4053031] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2006.25