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CLVTools (version 0.6.0)

Tools for Customer Lifetime Value Estimation

Description

Probabilistic latent customer attrition models (also known as "buy-'til-you-die models") are used to predict future purchase behavior of customers. This package includes fast and accurate implementations of various probabilistic latent customer attrition models for non-contractual settings (e.g., retail business) with and without time-invariant and time-varying covariates. Currently, the package includes the Pareto/NBD model (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) , for the BG/NBD model, see Fader PS, Hardie BG, Lee K (2005) and for the GGom/NBD model see Bemmaor AC, Glady N (2012) . For reference to the Gamma/Gamma model, see Fader PS, Hardie BG, Lee K (2005) .

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Install

install.packages('CLVTools')

Monthly Downloads

579

Version

0.6.0

License

GPL-3

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Maintainer

Patrick Bachmann

Last Published

June 24th, 2020

Functions in CLVTools (0.6.0)

bgbb

BG/BB models - Work In Progress
SetStaticCovariates

Add Static Covariates to a CLV data object
SetDynamicCovariates

Add Dynamic Covariates to a CLV data object
bgnbd

BG/NBD models
bgnbd_CET

BG/NBD: Conditional Expected Transactions
bgnbd_LL

BG/NBD: Log-Likelihood functions
apparelTrans

Apparel Retailer Dataset
CLVTools-package

Customer Lifetime Value Tools
apparelStaticCov

Time-invariant Covariates for the Apparel Retailer Dataset
apparelDynCov

Time-varying Covariates for the Apparel Retailer Dataset
clv.fitted.dynamic.cov-class

Fitted CLV Model with Dynamic covariates
clv.fitted.static.cov-class

Fitted CLV Model with Static covariates
clv.time.weeks-class

Time unit representing a single Week
clv.model.ggomnbd.static.cov-class

CLV Model functionality for GGompertz/NBD with static covariates
clv.time.years-class

Time unit representing a single Year
clv.model.pnbd.dynamic.cov-class

CLV Model functionality for PNBD with dynamic covariates
clv.pnbd-class

Result of fitting the Pareto/NBD model without covariates
clv.pnbd.dynamic.cov-class

Result of fitting the Pareto/NBD model with dynamic covariates
clv.bgnbd-class

Result of fitting the BG/NBD model without covariates
clv.bgnbd.static.cov-class

Result of fitting the BG/NBD model with static covariates
clv.model.bgnbd.no.cov-class

CLV Model functionality for BG/NBD without covariates
clv.model-class

CLV Model providing model related functionalities
clv.data-class

Transactional data to fit CLV models
nobs.clv.data

Number of observations
clv.time.days-class

Time unit representing a single Day
clv.data.dynamic.covariates-class

Transactional and dynamic covariates data to fit CLV models
clv.model.pnbd.no.cov-class

CLV Model functionality for Pareto/NBD without covariates
bgnbd_PAlive

BG/NBD: Probability of Being Alive
cdnow

CDNOW dataset
clv.model.bgnbd.static.cov-class

CLV Model functionality for BG/NBD with static covariates
gg_LL

Gamma-Gamma: Log-Likelihood Function
clv.data.static.covariates-class

Transactional and static covariates data to fit CLV models
clv.model.pnbd.static.cov-class

CLV Model functionality for Pareto/NBD with static covariates
clvdata

Create an object for transactional data required to estimate CLV
ggomnbd_LL

GGompertz/NBD: Log-Likelihood functions
clv.fitted-class

Fitted CLV Model without covariates
plot.clv.data

Plot actual repeat transactions
ggomnbd

Gamma-Gompertz/NBD model
ggomnbd_CET

GGompertz/NBD: Conditional Expected Transactions
clv.time.hours-class

Time unit representing a single hour
plot.clv.fitted

Plot expected and actual repeat transactions
clv.ggomnbd.static.cov-class

Result of fitting the GGompertz/NBD model with static covariates
clv.ggomnbd-class

Result of fitting the GGompertz/NBD model without covariates
clv.model.ggomnbd.no.cov-class

CLV Model functionality for GGompertz/NBD without covariates
fitted.clv.fitted

Extract Unconditional Expectation
pnbd

Pareto/NBD models
clv.pnbd.static.cov-class

Result of fitting the Pareto/NBD model with static covariates
pnbd_DERT

Pareto/NBD: Discounted Expected Residual Transactions
pnbd_LL

Pareto/NBD: Log-Likelihood functions
nobs.clv.fitted

Number of observations
pnbd_PAlive

Pareto/NBD: Probability of Being Alive
clv.time-class

Time Unit defining conceptual periods
pnbd_CET

Pareto/NBD: Conditional Expected Transactions
predict.clv.fitted

Predict CLV from a fitted model
ggomnbd_expectation

GGompertz/NBD: Unconditional Expectation
ggomnbd_PAlive

GGompertz/NBD: Probability of Being Alive
clv.time.date-class

Date based time-units
clv.time.datetime-class

POSIXct based time-units
print.clv.time

Summarizing a CLV time object
vcov.clv.fitted

Calculate Variance-Covariance Matrix for CLV Models fitted with Maximum Likelihood Estimation
summary.clv.fitted

Summarizing a fitted CLV model
summary.clv.data

Summarizing a CLV data object
vec_gsl_hyp2f0_e

GSL Hypergeom 2f0 for equal length vectors
vec_gsl_hyp2f1_e

GSL Hypergeom 2f1 for equal length vectors