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

Tomohiro Ando

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - Sixth International Conference on Data Mining, ICDM 2006
ページ21-32
ページ数12
DOI
出版ステータスPublished - 2006
外部発表はい
イベント6th International Conference on Data Mining, ICDM 2006 - Hong Kong, China
継続期間: 2006 12 182006 12 22

出版物シリーズ

名前Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN(印刷版)1550-4786

Other

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

ASJC Scopus subject areas

  • 工学(全般)

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