The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Customer Base Analysis: An Industrial Purchase Process Application
Abstract
Customer base analysis is concerned with using the observed past purchase behavior of customers to understand their current and likely future purchase patterns. More specifically, as developed in Schmittlein et al. (1987), customer base analysis uses data on the frequency, timing, and dollar value of each customer's past purchases to infer • the number of customers currently active, • how that number has changed over time, • which individual customers are most likely still active, • how much longer each is likely to remain an active customer, and • how many purchases can be expected from each during any future time period of interest. In this paper we empirically validate the model proposed by Schmittlein et al. In doing so, we provide one of the few applications of stochastic models to industrial purchase processes and industrial marketing decisions. Besides showing that the model does capture key aspects of the purchase process, we also present a more effective parameter estimation method and some results regarding sampling properties of the parameter estimates. Finally, we extend the model to explicitly incorporate dollar volume of past purchases. Our results indicate that this kind of customer base analysis can be both effective in predicting purchase patterns and in generating insights into how key customer groups differ. The link of both these benefits to industrial marketing decision making is also discussed.
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In the citing paper
“For instance, to the best of our knowledge, this is only the second empirical validation of the Pareto/NBD model -the first being Schmittlein and Peterson (1994).”
From “Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model (2005) · cites this paper as Schmittlein and Peterson (1994)
In the citing paper
“It is worth noting that this broad range covers the observed values from the original Schmittlein and Peterson (1994) application as well as the actual dataset used in our empirical analysis (to be discussed in the next section).”
From “Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model (2005) · cites this paper as Schmittlein and Peterson (1994)
In the citing paper
“As an alternative to MLE, SMC proposed a three-step method-of-moments estimation procedure, which was further refined by Schmittlein and Peterson (1994).”
From “Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model (2005) · cites this paper as Schmittlein and Peterson (1994)
Citations
Cites
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Self-citationOrganizational Buying Behavior
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Gigerenzer, G., & Gaissmaier, W. (2011) · Annual Review of Psychology · doi:10.1146/annurev-psych-120709-145346 · Cited by 4,037
Reinartz, W. J., & Kumar, V. (2000) · Journal of Marketing · doi:10.1509/jmkg.64.4.17.18077 · Cited by 1,364
Marketing Analytics for Data-Rich Environments
Wedel, M., & Kannan, P. K. (2016) · Journal of Marketing · doi:10.1509/jm.15.0413 · Cited by 1,261
Implications of Loyalty Program Membership and Service Experiences for Customer Retention and Value
Bolton, R. N., Kannan, P. K., & Bramlett, M. D. (2000) · Journal of the Academy of Marketing Science · doi:10.1177/0092070300281009 · Cited by 1,157
A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy
Venkatesan, R., & Kumar, V. (2004) · Journal of Marketing · doi:10.1509/jmkg.68.4.106.42728 · Cited by 948
The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration
Reinartz, W. J., & Kumar, V. (2003) · Journal of Marketing · doi:10.1509/jmkg.67.1.77.18589 · Cited by 811
Bolton, R. N., Lemon, K. N., & Verhoef, P. C. (2004) · Journal of the Academy of Marketing Science · doi:10.1177/0092070304263341 · Cited by 741
Customer lifetime value research in marketing: A review and future directions
Jain, D., & Singh, S. S. (2002) · Journal of Interactive Marketing · doi:10.1002/dir.10032 · Cited by 583
Growing on Steroids: Rapidly Scaling the User Base of Digital Ventures Through Digital Innovaton1
Huang, J., Henfridsson, O., Liu, M. J., & Newell, S. (2017) · MIS Quarterly · doi:10.25300/misq/2017/41.1.16 · Cited by 478
“Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model
Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005) · Marketing Science · doi:10.1287/mksc.1040.0098 · Cited by 480
Probability Models for Customer-Base Analysis
Fader, P. S., & Hardie, B. G. S. (2009) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2008.11.003 · Cited by 204
Optimizing the Marketing Interventions Mix in Intermediate-Term CRM
Rust, R. T., & Verhoef, P. C. (2005) · Marketing Science · doi:10.1287/mksc.1040.0107 · Cited by 191
Verhoef, P. C., Spring, P. N., Hoekstra, J. C., & Leeflang, P. S. H. (2003) · Decision Support Systems · doi:10.1016/s0167-9236(02)00069-6 · Cited by 125
Time-varying effects in the analysis of customer loyalty: A case study in insurance
Guillén, M., Nielsen, J. P., Scheike, T. H., & Pérez-Marín, A. M. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.09.045 · Cited by 65
Forecasting Repeat Sales at CDNOW: A Case Study
Fader, P. S., & Hardie, B. G. S. (2001) · Interfaces · doi:10.1287/inte.31.3s.94.9683 · Cited by 25
Metadata
- Authors
- Schmittlein, D. C., & Peterson, R. A.
- Affiliations
- University of Pennsylvania
- University of Texas at Austin
- Venue
- Marketing Science
- Published
- 1994-02
- Volume
- 13
- Issue
- 1
- Pages
- 41-67
- ISSN
- 0732-2399
- 1526-548X
- Cited by
- 248
- References
- 27
- Language
- en
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.