
Probability Models for Customer-Base Analysis (2013) [pdf] - tacon
http://www.brucehardie.com/talks/ho_cba_tut_art_13.pdf
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eadan
I have experience implementing similar models for analyzing and predicting
customer spending behavior. The posted paper may be somewhat abtruse for the
uninitiated, so I thought I'd provide some background knowledge.

As the title states these are probability models for describing the purchasing
behavior of customers. The paper outlines outlines several models depending on
if customers may purchase at discrete times or continuously, and whether the
customer relationship is contractual or not. As an example let's take the
"BG/NBD" model for continuous time, non-contractual customer relations.

We assume each customer, `i`:

    
    
      1. Makes purchases according to an exponential distribution with mean rate `lambda[i]` [0].
      2. After a purchase becomes inactive (never makes a purchase again) with a fixed probability `p[i]`.
    

And, across all customers:

    
    
      3. The purchasing rate, `lambda[i]`, follows a Gamma distribution with parameters `alpha` and `beta` [1].
      4. The inactivity probability, `p[i]`, follows a Beta distribution with parameters `a` and `b` [2].
    

This is a generative model of the purchasing behavior of each customer. The
goal is to find values of the parameters `lambda[i]`, `p[i]`, `alpha`, `beta`,
`a` and `b` that "fit" your data the "best". Emphasis here because there
different notions on what "best" means, but the two main approaches are
optimization (typically maximizing the likelihood) and Bayesian approaches
which rather than finding the "best" value for a parameter, find a
distribution over all possible values called the posterior.

There are implementations of these models for python [3] and R [4].

[0]
[https://en.wikipedia.org/wiki/Exponential_distribution](https://en.wikipedia.org/wiki/Exponential_distribution)

[1]
[https://en.wikipedia.org/wiki/Gamma_distribution](https://en.wikipedia.org/wiki/Gamma_distribution)

[2]
[https://en.wikipedia.org/wiki/Beta_distribution](https://en.wikipedia.org/wiki/Beta_distribution)

[3]
[https://github.com/CamDavidsonPilon/lifetimes](https://github.com/CamDavidsonPilon/lifetimes)

[4] [https://github.com/cran/BTYD](https://github.com/cran/BTYD)

~~~
malshe
Thanks for the Github links. I would like to add
[https://github.com/mplatzer/BTYDplus](https://github.com/mplatzer/BTYDplus)

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mwexler
If you enjoy this work, you may also enjoy reading other work Peter Fader has
done (in cooperation with Hardie in some cases, as in parent post).

[https://marketing.wharton.upenn.edu/profile/faderp/#research](https://marketing.wharton.upenn.edu/profile/faderp/#research)

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jmeister
I just started studying CLV models for a research project. Has anyone
implemented/seen these kinds of models in production?

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brian_spiering
Is there an updated version? It would be nice to see the same rigour applied
to SaaS.

~~~
joker3
Peter and his former student Dan McCarthy have done a lot of work on CLV
calculations.

