I would appreciate if someone could tell me which subjects these papers listed below "belong to", and how do I get started in understanding them - what do I need to read/study and in what sequence.

In general, I would like to be able to understand marketing papers that make heavy use of mathematics, similar to the papers by Fader.

I've had only basic training in statistics and econometrics, however, the concepts these papers use are much more advanced - Beta distribution, Pareto/NBD model, beta-geometric model etc.

In general, I understand that these papers are related to the disciplines of customer analytics, marketing analytics, data science, econometrics and marketing science in general, but I'd be really happy if I could have a list of concrete textbooks or more precise subject names that I can be sure that if I read/study them, then I am well-prepared to read Fader's work.

Also, I would love to have an up-to-date source that reviews and summarizes this kind of research.

Also, I've seen other sophisticated mathematical methods be applied to other domains of marketing. The question I have is - do the researchers and academics who do such studies necessarily have a background in mathematics or natural sciences and then they apply it in marketing, or is there a set of models/methods that are typically used in marketing science that can be learned by someone without a STEM background?

Any help is highly appreciated!

Here are the papers that I am referring to:

Fader, P. S., Hardie, B. G., & Lee, K. L. (2005). “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing science, 24(2), 275-284.

Fader, P. S., Hardie, B. G., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of marketing research, 42(4), 415-430.


1 Answer 1


I'm not sure this question is right for this forum, but certainly useful to know are

  1. Probability distributions, as you mentioned: beta, binomial, gamma, exponential, poisson.
  2. Maximum likelihood estimation.
  3. (Optional) Generalized linear models.

These topics are often covered in any mathematical statistics textbook or course.

  • 1
    $\begingroup$ Thank you very much! I asked it here because I saw some other people ask questions about related topics. Do you know any other place where this question would be more appropriate? $\endgroup$
    – RahPah
    Feb 7, 2022 at 6:36

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