Probabilistic Models for Assessing and Predicting your Customer Base
Simulate data according to Pareto/NBD (Abe) model assumptions
Pareto/NBD (Abe) Parameter Draws
Check Model Parameters
Generic Method for Plotting Frequency vs. Conditional Expected Frequen...
Generic Method for Plotting Frequency vs. Conditional Expected Frequen...
Generic Method for Tracking Plots
Convert Event Log to customer-level summary statistic
Convert Event Log to Transaction Counts
Estimate Regularity in Intertransaction Timings
(M)BG/CNBD-k Log-Likelihood
(M)BG/CNBD-k Conditional Expected Transactions
(M)BG/CNBD-k Parameter Estimation
(M)BG/CNBD-k Expectation
(M)BG/CNBD-k Expected Cumulative Transactions
Simulate data according to (M)BG/CNBD-k model assumptions
(M)BG/CNBD-k P(alive)
(M)BG/CNBD-k Plot Frequency in Calibration Period
(M)BG/CNBD-k Plot Frequency vs. Conditional Expected Frequency
(M)BG/CNBD-k Plot Actual vs. Conditional Expected Frequency by Recency
(M)BG/CNBD-k Tracking Cumulative Transactions Plot
(M)BG/CNBD-k Tracking Incremental Transactions Comparison
(M)BG/CNBD-k Probability Mass Function
Draws number of future transactions based on MCMC parameter draws
Unconditional Expectation for Pareto/GGG, Pareto/NBD (HB) and Pareto/N...
Expected Cumulative Transactions for Pareto/GGG, Pareto/NBD (HB) and P...
Calculates P(active) based on drawn future transactions.
Calculates P(alive) based on MCMC parameter draws
Frequency in Calibration Period for Pareto/GGG, Pareto/NBD (HB) and Pa...
Draw diagnostic plot to inspect error in P(active).
Tracking Cumulative Transactions Plot for Pareto/GGG, Pareto/NBD (HB) ...
Tracking Incremental Transactions Plot for Pareto/GGG, Pareto/NBD (HB)...
Probability Mass Function for Pareto/GGG, Pareto/NBD (HB) and Pareto/N...
(Re-)set burnin of MCMC chains.
Calculate the log-likelihood of the NBD model
NBD Conditional Expected Transactions
Parameter Estimation for the NBD model
Simulate data according to NBD model assumptions
Calculate the log-likelihood of the NBD model
Simulate data according to Pareto/GGG model assumptions
Pareto/GGG Parameter Draws
Pareto/GGG Plot Regularity Rate Heterogeneity
Plot timing patterns of sampled customers
Simulate data according to Pareto/NBD model assumptions
Pareto/NBD (HB) Parameter Draws
Provides advanced statistical methods to describe and predict customers' purchase behavior in a non-contractual setting. It uses historic transaction records to fit a probabilistic model, which then allows to compute quantities of managerial interest on a cohort- as well as on a customer level (Customer Lifetime Value, Customer Equity, P(alive), etc.). This package complements the BTYD package by providing several additional buy-till-you-die models, that have been published in the marketing literature, but whose implementation are complex and non-trivial. These models are: NBD [Ehrenberg (1959) <doi:10.2307/2985810>], MBG/NBD [Batislam et al (2007) <doi:10.1016/j.ijresmar.2006.12.005>], (M)BG/CNBD-k [Reutterer et al (2020) <doi:10.1016/j.ijresmar.2020.09.002>], Pareto/NBD (HB) [Abe (2009) <doi:10.1287/mksc.1090.0502>] and Pareto/GGG [Platzer and Reutterer (2016) <doi:10.1287/mksc.2015.0963>].