(Pareto/Negative-Binomial-Distribution), the BG/NBD mode (Beta-Gamma/Negative-Binomial-Distribution) and the GGom/NBD
(Gamma-Gompertz/Negative-Binomial-Distribution) for the purchase and the attrition processes as well as the Gamma/Gamma model
for the spending process. For reference to the Pareto/NBD model, see Schmittlein DC, Morrison DG, Colombo R (1987) <doi:10.1287/mnsc.33.1.1>,
for the BG/NBD model, see Fader PS, Hardie BG, Lee K (2005) <doi:10.1287/mksc.1040.0098> and for the GGom/NBD model see Bemmaor AC, Glady N (2012)
<doi:10.1287/mnsc.1110.1461>. For reference to the Gamma/Gamma model, see Fader PS, Hardie BG, Lee K (2005) <doi:10.1509/jmkr.2005.42.4.415>.
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person(given="Niels",   family="Kuebler",     email = "niels.kuebler@uzh.ch", role = "aut"),
person(given="Elliot",  family="Oblander",    email = "eoblander23@gsb.columbia.edu",role = "aut"),
person(given="Patrik",  family="Schilter",    email = "patrik.schilter@gmail.com",role = "aut")
A set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV).
Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals' attrition, transaction, and spending process.
Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process.
They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior.
Moreover, these models have also been used to predict individuals’ long-term engagement in activities such as playing an online game or
posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in
lifetime values estimates for individual customers.
This package includes fast and accurate implementations of various probabilistic models for non-contractual settings
(e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g.,
socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying
covariates to control for e.g., seasonal patterns.
Currently, the package includes the following latent attrition models to model individuals' attrition and transaction process:
[2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates),
[3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the
Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals.
Imports: data.table (>= 1.12.0), ggplot2 (>= 3.2.0), lubridate (>=
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