Calculate churn

Calculate churn using variable window size

calcChurn(df, groupVars = NULL, deathThreshold = 5)

Arguments

df	A data frame
groupVars	A character vector of variable names to group by
deathThreshold	A numeric value indicating how many years must elapse before an individual is considered churned

Value

A dataframe with columns for grouping variables and columns churnedCustomers, retainedCustomers, totalCustomers, churnRate, and retentionRate

See also

Other analysis functions: calcGenderProportion, calcParticipation, calcRecruitment, countCustomers, countItems, itemGroupCount, sumRevenue

Examples

# Demo data: Churn for customers purchasing a fishing
# license between 2010 and 2017
filterData(
  dataSource = "csv",
  activeFilters = list(itemType = "Fish", itemYear = c(2010, 2017))
) %>%
  calcChurn(c("itemType"))
#> # A tibble: 3 x 7
#>   itemYear itemType churnedCustomers retainedCustome… totalCustomers churnRate
#>   <fct>    <fct>               <dbl>            <dbl>          <dbl>     <dbl>
#> 1 2010     Fish                  490             1138           1628     0.301
#> 2 2011     Fish                  488             1120           1608     0.303
#> 3 2012     Fish                  594             1279           1873     0.317
#> # … with 1 more variable: retentionRate <dbl>
if (FALSE) {
# Database connection. Suggest using keyring package to avoid hardcoding
# passwords
myConn <- DBI::dbConnect(odbc::odbc(),
  dsn = "HuntFishApp", # Your datasource name
  uid = keyring::key_get("HuntFishAppUID"), # Your username
  pwd = keyring::key_get("HuntFishAppPWD")
) # Your password

# SQL Backend: Churn for customers purchasing a fishing
# license between 2010 and 2017
filterData(
  dataSource = "sql",
  conn = myConn,
  activeFilters = list(itemType = "Fish", itemYear = c(2010, 2017))
) %>%
  calcChurn(c("itemType"))
}