ca_SRM_time_varying: Time varying Simple retention model Estimates retention rate...

In CADF: Customer Analytics Data Formatting

Time varying Simple retention model Estimates retention rate using logistic regression and the simple regression model Mostly used for contractual models where there are clear opportunities for cancellation. Could be used in non-contractional situations although the cancellation opportunities should be defined. Not recommended for use with services that consumers use rotating-door style. Use the migration model there.

Description

Time varying Simple retention model Estimates retention rate using logistic regression and the simple regression model Mostly used for contractual models where there are clear opportunities for cancellation. Could be used in non-contractional situations although the cancellation opportunities should be defined. Not recommended for use with services that consumers use rotating-door style. Use the migration model there.

Usage

ca_SRM_time_varying(df_logistic, reference_level = 12, maxT = 12)

Arguments

df_logistic	A data frame, formatted for logistic regression. 1 row for each customer id/timeperiod. 1/0 for purchase.
reference_level	All coefficients will be judged relevant to the reference level. It defaults to time period 12. (Note interpretation will change based on how T is formulated.)
maxT	The number of timeperiods to build.

Value

Returns logistic model results (the glm model)

Examples

library(stats)
x <- c(3, 1, 5)
df_logistic <- bigT_expand_via_apply(x)
model <- ca_SRM_time_varying(df_logistic, reference_level = 3)

CADF documentation built on Oct. 31, 2024, 5:08 p.m.