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
Handbook of Mathematical Functions
McQuarrie, D. A. (1966) · American Journal of Physics · doi:10.1119/1.1972842 · Cited by 40,433
Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables
Owen, D. B., Abramowitz, M., & Stegun, I. A. (1965) · Technometrics · doi:10.2307/1266136 · Cited by 16,138
Marketing management: analysis. planning. implementation and control
Kotler, P. (2000) · Cited by 6,639
A leisurely look at the bootstrap, the jackknife, and cross-validation
Efron, B., & Gong, G. (1983) · The American Statistician · doi:10.1080/00031305.1983.10483087 · Cited by 3,229
A Logit Model of Brand Choice Calibrated on Scanner Data
Guadagni, P. M., & Little, J. D. C. (1983) · Marketing Science · doi:10.1287/mksc.2.3.203 · Cited by 1,862
Impact of Sales Promotions on when, what, and how Much to Buy
Gupta, S. (1988) · Journal of Marketing Research · doi:10.1177/002224378802500402 · Cited by 1,049
Counting Your Customers: Who-Are They and What Will They Do Next?
Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987) · Management Science · doi:10.1287/mnsc.33.1.1 · Cited by 679
Self-citationOrganizational Buying Behavior
Sciullo, H. A., Webster, F. E., & Wind, Y. (1973) · Journal of Marketing · doi:10.2307/1250373 · Cited by 640
Morwitz, V. G., & Schmittlein, D. (1992) · Journal of Marketing Research · doi:10.2307/3172706 · Cited by 293
Self-citationLattin, J. M., & McAlister, L. M. (1985) · Journal of Marketing Research · doi:10.1177/002224378502200308 · Cited by 232
Morrison, D. G., & Schmittlein, D. C. (1988) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1988.10509648 · Cited by 194
Self-citationStochastic Models of Interpurchase Time with Time-Dependent Covariates
Гупта, С. (1991) · Journal of Marketing Research · doi:10.1177/002224379102800101 · Cited by 172
Unique aspects of marketing industrial services
Jackson, R., & Cooper, P. D. (1988) · Industrial Marketing Management · doi:10.1016/0019-8501(88)90013-2 · Cited by 127
SchmittleinDavid, C., MorrisonDonald, G., & ColomboRichard (1987) · Management Science · doi:10.5555/2780513.2780514 · Cited by 104
Fader, P. S., & Lattin, J. M. (1993) · Marketing Science · doi:10.1287/mksc.12.3.304 · Cited by 97
Schmittlein, D. C., Bemmaor, A. C., & Morrison, D. G. (1985) · Marketing Science · doi:10.1287/mksc.4.3.255 · Cited by 91
Self-citationAdding Explanatory Variables to a Consumer Purchase Behavior Model: An Exploratory Study
Jones, J. M., & Zufryden, F. S. (1980) · Journal of Marketing Research · doi:10.1177/002224378001700305 · Cited by 89
Truth in Concentration in the Land of (80/20) Laws
Schmittlein, D. C., Cooper, L. G., & Morrison, D. G. (1993) · Marketing Science · doi:10.1287/mksc.12.2.167 · Cited by 83
Self-citationAlgorithms for the Computation of Mathematical Functions.
G., W., & Luke, Y. L. (1979) · Mathematics of Computation · doi:10.2307/2006057 · Cited by 80
A Multivariate Polya Model of Brand Choice and Purchase Incidence
Wagner, U., & Taudes, A. (1986) · Marketing Science · doi:10.1287/mksc.5.3.219 · Cited by 69
Prediction of Future Random Events with the Condensed Negative Binomial Distribution
Schmittlein, D. C., & Morrison, D. G. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477993 · Cited by 46
Self-citationConditional Trend Analysis: A Model That Allows for Nonusers
Morrison, D. G. (1969) · Journal of Marketing Research · doi:10.1177/002224376900600309 · Cited by 45
Schmittlein, D. C. (1989) · The American Statistician · doi:10.1080/00031305.1989.10475651 · Cited by 37
Self-citationIndustrial buying behavior: Aviation fuel contracts
Uncles, M., & Ehrenberg, A. S. C. (1990) · International Journal of Research in Marketing · doi:10.1016/0167-8116(90)90033-j · Cited by 34
Zufryden, F. S. (1986) · Journal of Marketing Research · doi:10.1177/002224378602300210 · Cited by 34
On linearly increasing mean residual lifetimes
Morrison, D. G. (1978) · Journal of Applied Probability · doi:10.2307/3213124 · Cited by 27
Modeling and estimation using job duration data
Schmittlein, D. C., & Morrison, D. G. (1983) · Organizational Behavior and Human Performance · doi:10.1016/0030-5073(83)90136-8 · Cited by 13
Self-citation
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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
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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