poster

Nonparametric hierarchal bayesian modeling in non-contractual heterogeneous survival data

Authors:

Shouichi Nagano,

Yusuke Ichikawa,

Tadasu Uchiyama,

Makoto AbeAuthors Info & Claims

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 668 - 676

https://doi.org/10.1145/2487575.2487590

Published: 11 August 2013 Publication History

Abstract

An important problem in the non-contractual marketing domain is discovering the customer lifetime and assessing the impact of customer's characteristic variables on the lifetime. Unfortunately, the conventional hierarchical Bayes model cannot discern the impact of customer's characteristic variables for each customer. To overcome this problem, we present a new survival model using a non-parametric Bayes paradigm with MCMC. The assumption of a conventional model, logarithm of purchase rate and dropout rate with linear regression, is extended to include our assumption of the Dirichlet Process Mixture of regression. The extension assumes that each customer belongs probabilistically to different mixtures of regression, thereby permitting us to estimate a different impact of customer characteristic variables for each customer. Our model creates several customer groups to mirror the structure of the target data set.

The effectiveness of our proposal is confirmed by a comparison involving a real e-commerce transaction dataset and an artificial dataset; it generally achieves higher predictive performance. In addition, we show that preselecting the actual number of customer groups does not always lead to higher predictive performance.

References

[1]

Rust R. T. and T. S. Chung, Marketing models of service and relationships, Marketing Science, vol.25, no.6, pp.560--580, 2006.

Digital Library

[2]

Sun B., Technology innovation and implications for customer relationship management, Marketing Science, vol.25, no.6, pp.594--597, 2006.

Digital Library

[3]

Schmittlein D. C., D. G. Morrison, and R. Colombo, "Counting your customers: Who are they and what will they do next?", Management Science, vol.33 no.1, pp.1--24, 1987.

Digital Library

[4]

Fader P. S., B. G. S. Hardie and K. L. Lee, Counting your customers the easy way: An alternative to the Pareto/NBD model, Marketing Science, vol.24, no.2, pp.275--284, 2005.

Digital Library

[5]

Fader P. S., B. G. S. Hardie and K. L. Lee, RFM and CLV: Using iso-value curves for customer base analysis, Journal of Marketing Research, vol.42, no.4, pp.415--430, 2005.

[6]

Reinartz W. J. and V. Kumar, On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing, Journal of Marketing, vol.64, no.4 pp.17--35, 2000.

[7]

Schmittlein D. C. and R. A. Peterson, Customer base analysis: An industrial purchase process application, Marketing Science, vol.13, no.1, pp.41--67, 1994.

Digital Library

[8]

Abe, M., "Counting Your Customers" One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model, Marketing Science, vol.28, no.3, pp.541--553, 2009.

Digital Library

[9]

Reinartz W. J., and V. Kumar, "The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration", Journal of Marketing, vol.67, no.1, pp.77--99, 2003.

[10]

D. Blackwell and J. B. MacQueen, "Ferguson distributions via Polya urn schemes", The Annals of Statistics, vol.1, no.2, pp.353--355, 1973.

[11]

C. Kemp, J. B. Tenenbaum, T. L. Griffiths, T. Yamada and N. Ueda, "Learning systems of concepts with an infinite relational model", Proceedings of the 21st National Conference on Artificial Intelligence, 2006.

Digital Library

[12]

J. Pitman, Combinatorial Stochastic Processes, Lecture Notes for St. Flour Summer School, Springer-Verlag, New York, 2002.

[13]

Marin J. M., Mengersen K. and Robert C. P., Bayesian modelling and inference on mixtures of distributions, In Handbook of Statistics, vol.25, pp.459--507, Amsterdam, North-Holland, 2005.

[14]

Mengersen K. and Robert C., Testing for mixtures: A Bayesian entropic approach (with discussion), Bayesian Statics 5, pp.255--276, Oxford. Oxford University Press, 1996.

[15]

Lindsay B., Mixture Models: Theory, Geometry and applications, IMS Monographs, Hayward, CA, 1995.

[16]

Dupuis J. and Robert C., Model choice in qualitative regression models, Journal of Statistical Planning and Inference, vol.111, pp.77--94, 2003.

[17]

Tanner, M. A., W. H. Wong, The calculation of posterior distributions by data augmentation, theory and methods, Journal of the American Statistical Association, vol.82, no.398, pp.528--540, 1987.

[18]

Geweke J., Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments, Bayesian Statistics, vol.4, Oxford University Press, Oxford, pp.169--193, 1992.

[19]

Congdon P., Bayesian Statistical Modelling, London, 2001.

[20]

Gelman A., J. B. Carlin, H. S. Stern and D. B. Rubin, Bayesian Data Analysis. Chapman and Hall, Boca Raton, FL, 1995.

[21]

Rossi P. E., G. Allenby and R. McCulloch, Bayesian Statistics and Marketing, John Wiley and Sons, London, 2005.

Cited By

Kohjima MMatsubayashi TToda H(2019)Variational Bayes for Mixture Models with Censored DataMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-10928-8_36(605-620)Online publication date: 23-Jan-2019
https://doi.org/10.1007/978-3-030-10928-8_36
Trouleau WAshkan ADing WEriksson BKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)Just One MoreProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939792(1215-1224)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2939672.2939792
Karvanen JRantanen ALuoma L(2014)Survey data and Bayesian analysis: a cost-efficient way to estimate customer equityQuantitative Marketing and Economics10.1007/s11129-014-9148-412:3(305-329)Online publication date: 8-Jul-2014
https://doi.org/10.1007/s11129-014-9148-4

Index Terms

Nonparametric hierarchal bayesian modeling in non-contractual heterogeneous survival data
1. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Survival analysis

Recommendations

No Customer Left Behind: A Distribution-Free Bayesian Approach to Accounting for Missing Xs in Marketing Models

In marketing applications, it is common that some key covariates in a regression model, such as marketing mix variables or consumer profiles, are subject to missingness. The convenient method that excludes the consumers with missingness in any covariate ...
“Counting Your Customers” One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model

This research extends a Pareto/NBD model of customer-base analysis using a hierarchical Bayesian HB framework to suit today's customized marketing. The proposed HB model presumes three tried and tested assumptions of Pareto/NBD models: 1 a Poisson ...
Bayesian nonparametric latent feature models

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2013

1534 pages

ISBN:9781450321747

DOI:10.1145/2487575

Editors:
Rayid Ghani
University of Chicago
,
Ted E. Senator
SAIC
,
Paul Bradley
MethodCare, Inc.
,
Rajesh Parekh
Groupon
,
Jingrui He
Stevens Institute of Technology
,
General Chairs:
Robert L. Grossman
University of Chicago and Open Data Group
,
Ramasamy Uthurusamy
General Motors Corporation (retired)
,
Program Chairs:
Inderjit S. Dhillon
University of Texas
,
Yehuda Koren
Google

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 August 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

KDD' 13

Sponsor:

KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 11 - 14, 2013

Illinois, Chicago, USA

Acceptance Rates

KDD '13 Paper Acceptance Rate 125 of 726 submissions, 17%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
419
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)3

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kohjima MMatsubayashi TToda H(2019)Variational Bayes for Mixture Models with Censored DataMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-10928-8_36(605-620)Online publication date: 23-Jan-2019
https://doi.org/10.1007/978-3-030-10928-8_36
Trouleau WAshkan ADing WEriksson BKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)Just One MoreProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939792(1215-1224)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2939672.2939792
Karvanen JRantanen ALuoma L(2014)Survey data and Bayesian analysis: a cost-efficient way to estimate customer equityQuantitative Marketing and Economics10.1007/s11129-014-9148-412:3(305-329)Online publication date: 8-Jul-2014
https://doi.org/10.1007/s11129-014-9148-4

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten