clv.data.dynamic.covariates-class

show,clv.data.dynamic.covariates-method

Extends the class clv.data.static.covariates,
but adds no additional slots to it. The purpose of this class rather is to define different behaviors
suitable for dynamic covariates.
The <code>data.table</code>s stored in the slots <code>data.cov.life</code> and <code>data.cov.trans</code>
each contain an additional column <code>Cov.Date</code> for the timepoint of the covariate.
An object of this class serves as input to fit models with dynamic covariates.

internal

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:
[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.

Patrick Bachmann

CLVTools

Tools for Customer Lifetime Value Estimation

Niels Kuebler

Markus Meierer

Jeffrey Naef

E. Shin Oblander

Patrik Schilter

clv.data.dynamic.covariates-class function

<dl>
<dt><code>data.cov.life</code></dt>
<dd>Single <code>data.table</code> with all static covariate data for the lifetime process</dd></dl>
<dt><code>data.cov.trans</code></dt>
<dd>Single <code>data.table</code> with all static covariate data for the transaction process</dd>
<dt><code>names.cov.data.life</code></dt>
<dd>Character vector with names of the dynamic lifetime covariates.</dd>
<dt><code>names.cov.data.trans</code></dt>
<dd>Character vector with names of the dynamic transaction covariates.</dd>

Slots

Transactional and dynamic covariates data to fit CLV models — clv.data.dynamic.covariates-class

<dl>
<dt><code>data.cov.life</code></dt>
<dd>Single <code>data.table</code> with all static covariate data for the lifetime process</dd>


<dt><code>data.cov.trans</code></dt>
<dd>Single <code>data.table</code> with all static covariate data for the transaction process</dd>


<dt><code>names.cov.data.life</code></dt>
<dd>Character vector with names of the dynamic lifetime covariates.</dd>


<dt><code>names.cov.data.trans</code></dt>
<dd>Character vector with names of the dynamic transaction covariates.</dd>


</dl>

clv.data.dynamic.covariates-class: Transactional and dynamic covariates data to fit CLV models

Description

Usage

Arguments

Slots

See Also