Package: CLVTools 0.11.2
CLVTools: Tools for Customer Lifetime Value Estimation
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.
Authors:
CLVTools_0.11.2.tar.gz
CLVTools_0.11.2.tar.gz(r-4.5-noble)CLVTools_0.11.2.tar.gz(r-4.4-noble)
CLVTools_0.11.2.tgz(r-4.4-emscripten)CLVTools_0.11.2.tgz(r-4.3-emscripten)
CLVTools.pdf |CLVTools.html✨
CLVTools/json (API)
NEWS
# Install 'CLVTools' in R: |
install.packages('CLVTools', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bachmannpatrick/clvtools/issues
- apparelDynCov - Time-varying Covariates for the Apparel Retailer Dataset
- apparelDynCovFuture - Future Time-varying Covariates for the Apparel Retailer Dataset
- apparelStaticCov - Time-invariant Covariates for the Apparel Retailer Dataset
- apparelTrans - Apparel Retailer Dataset
- cdnow - CDNOW dataset
Last updated 5 days agofrom:0d2dede789. Checks:OK: 1 WARNING: 1. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 03 2024 |
R-4.5-linux-x86_64 | WARNING | Dec 03 2024 |
Exports:as.clv.databgbbbgnbdclv.bootstrapped.applyclvdataggggomnbdlatentAttritionlrtestnewcustomernewcustomer.dynamicnewcustomer.spendingnewcustomer.staticplotpmfpnbdpredictSetDynamicCovariatesSetStaticCovariatesshowspending
Dependencies:briocallrclicolorspacecpp11crayondata.tabledescdiffobjdigestevaluatefansifarverFormulafsgenericsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclelubridatemagrittrMASSMatrixmgcvmunsellnlmenloptrnumDerivoptimxpillarpkgbuildpkgconfigpkgloadpracmapraiseprocessxpsR6RColorBrewerRcppRcppArmadilloRcppGSLrlangrprojrootscalestestthattibbletimechangeutf8vctrsviridisLitewaldowithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Customer Lifetime Value Tools | CLVTools-package CLVTools |
Time-varying Covariates for the Apparel Retailer Dataset | apparelDynCov |
Future Time-varying Covariates for the Apparel Retailer Dataset | apparelDynCovFuture |
Time-invariant Covariates for the Apparel Retailer Dataset | apparelStaticCov |
Apparel Retailer Dataset | apparelTrans |
Coerce to clv.data object | as.clv.data as.clv.data.data.frame as.clv.data.data.table |
Coerce to a Data Frame | as.data.frame.clv.data |
Coerce to a Data Table | as.data.table.clv.data |
BG/BB models - Work In Progress | bgbb bgbb,clv.data-method bgbb,clv.data.dynamic.covariates-method bgbb,clv.data.static.covariates-method |
BG/NBD models | bgnbd bgnbd,clv.data-method bgnbd,clv.data.dynamic.covariates-method bgnbd,clv.data.static.covariates-method |
BG/NBD: Conditional Expected Transactions | bgnbd_CET bgnbd_nocov_CET bgnbd_staticcov_CET |
BG/NBD: Unconditional Expectation | bgnbd_expectation bgnbd_nocov_expectation bgnbd_staticcov_expectation |
BG/NBD: Log-Likelihood functions | bgnbd_LL bgnbd_nocov_LL_ind bgnbd_nocov_LL_sum bgnbd_staticcov_LL_ind bgnbd_staticcov_LL_sum |
BG/NBD: Probability of Being Alive | bgnbd_nocov_PAlive bgnbd_PAlive bgnbd_staticcov_PAlive |
BG/NBD: Probability Mass Function (PMF) | bgnbd_nocov_PMF bgnbd_pmf bgnbd_staticcov_PMF |
CDNOW dataset | cdnow |
Bootstrapping: Fit a model again on sampled data and apply method | clv.bootstrapped.apply |
Create an object for transactional data required to estimate CLV | clvdata |
Extract Unconditional Expectation | fitted.clv.fitted |
Gamma/Gamma Spending model | gg gg,clv.data-method |
Gamma-Gamma: Log-Likelihood Function | gg_LL |
Gamma-Gompertz/NBD model | ggomnbd ggomnbd,clv.data-method ggomnbd,clv.data.dynamic.covariates-method ggomnbd,clv.data.static.covariates-method |
GGompertz/NBD: Conditional Expected Transactions | ggomnbd_CET ggomnbd_nocov_CET ggomnbd_staticcov_CET |
GGompertz/NBD: Unconditional Expectation | ggomnbd_expectation ggomnbd_nocov_expectation ggomnbd_staticcov_expectation |
GGompertz/NBD: Log-Likelihood functions | ggomnbd_LL ggomnbd_nocov_LL_ind ggomnbd_nocov_LL_sum ggomnbd_staticcov_LL_ind ggomnbd_staticcov_LL_sum |
GGompertz/NBD: Probability of Being Alive | ggomnbd_nocov_PAlive ggomnbd_PAlive ggomnbd_staticcov_PAlive |
GGompertz/NBD: Probability Mass Function (PMF) | ggomnbd_nocov_PMF ggomnbd_PMF ggomnbd_staticcov_PMF |
Formula Interface for Latent Attrition Models | latentAttrition |
Likelihood Ratio Test of Nested Models | lrtest lrtest,clv.fitted-method lrtest.clv.fitted |
New customer prediction data | newcustomer newcustomer.dynamic newcustomer.spending newcustomer.static |
Number of observations | nobs.clv.data |
Number of observations | nobs.clv.fitted |
Plot Diagnostics for the Transaction data in a clv.data Object | plot.clv.data |
Plot expected and actual mean spending per transaction | plot,clv.fitted.spending-method plot.clv.fitted.spending |
Plot Diagnostics for a Fitted Transaction Model | plot plot,clv.fitted.transactions-method plot.clv.fitted.transactions |
Probability Mass Function | pmf pmf,clv.fitted.transactions-method |
Pareto/NBD models | pnbd pnbd,clv.data-method pnbd,clv.data.dynamic.covariates-method pnbd,clv.data.static.covariates-method |
Pareto/NBD: Conditional Expected Transactions | pnbd_CET pnbd_nocov_CET pnbd_staticcov_CET |
Pareto/NBD: Discounted Expected Residual Transactions | pnbd_DERT pnbd_nocov_DERT pnbd_staticcov_DERT |
Pareto/NBD: Unconditional Expectation | pnbd_expectation pnbd_nocov_expectation pnbd_staticcov_expectation |
Pareto/NBD: Log-Likelihood functions | pnbd_LL pnbd_nocov_LL_ind pnbd_nocov_LL_sum pnbd_staticcov_LL_ind pnbd_staticcov_LL_sum |
Pareto/NBD: Probability of Being Alive | pnbd_nocov_PAlive pnbd_PAlive pnbd_staticcov_PAlive |
Pareto/NBD: Probability Mass Function (PMF) | pnbd_nocov_PMF pnbd_pmf pnbd_staticcov_PMF |
Infer customers' spending | predict,clv.fitted.spending-method predict.clv.fitted.spending |
Predict CLV from a fitted transaction model | predict predict,clv.fitted.transactions-method predict.clv.fitted.transactions |
Add Dynamic Covariates to a CLV data object | SetDynamicCovariates |
Add Static Covariates to a CLV data object | SetStaticCovariates |
Formula Interface for Spending Models | spending |
Subsetting clv.data | subset subset.clv.data |
Summarizing a fitted CLV model | print.summary.clv.fitted summary.clv.fitted summary.clv.fitted.transactions.static.cov |
Calculate Variance-Covariance Matrix for CLV Models fitted with Maximum Likelihood Estimation | vcov.clv.fitted |