pnbd_expectation

pnbd_nocov_expectation

pnbd_staticcov_expectation

shape parameter of the Gamma distribution of the purchase process. The smaller r, the stronger the heterogeneity of the purchase process

shape parameter of the Gamma distribution for the lifetime process. The smaller s, the stronger the heterogeneity of customer lifetimes

rate parameter of the Gamma distribution of the purchase process

alpha_0

rate parameter for the Gamma distribution for the lifetime process.

beta_0

Number of periods since the customer came alive

vT_i

Vector of individual parameters alpha

vAlpha_i

Vector of individual parameters beta

vBeta_i

Computes the expected number of repeat transactions in the interval (0, vT_i]
for a randomly selected customer, where 0 is defined as the point when the customer came alive.

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<e2><80><99> 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:
[1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution),
[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
[4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution).
Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals.

pnbd_expectation: Pareto/NBD: Unconditional Expectation

Description

Usage

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References