| Type: | Package | 
| Title: | Data Sets for Statistical Methods in Customer Relationship Management by Kumar and Petersen (2012). | 
| Version: | 0.0-3 | 
| Date: | 2013-09-16 | 
| Author: | Tobias Verbeke, based on datasets provided on the book's website | 
| Maintainer: | Tobias Verbeke <tobias.verbeke@openanalytics.eu> | 
| Description: | Data Sets for Kumar and Petersen (2012). Statistical Methods in Customer Relationship Management, Wiley: New York. | 
| License: | GPL-3 | 
| Collate: | 'customerAcquisition.R' 'acquisitionRetention.R' 'customerChurn.R' 'customerWinBack.R' 'customerRetentionDemographics.R' 'customerRetentionLifetimeDuration.R' 'customerRetentionTransactions.R' | 
| Packaged: | 2013-09-16 07:04:19 UTC; tobias | 
| NeedsCompilation: | no | 
| Repository: | CRAN | 
| Date/Publication: | 2013-09-16 09:17:35 | 
Acquisition-Retention Data from Chapter 5
Description
Acquisition-Retention Data from Chapter 5
Usage
  acquisitionRetention
Format
Data frame with the following 15 variables
- customer
- customer number (from 1 to 500) 
- acquisition
- 1 if the prospect was acquired, 0 otherwise 
- duration
- number of days the customer was a customer of the firm, 0 if acquisition == 0 
- profit
- customer lifetime value (CLV) of a given customer, -(Acq_Exp) if the customer is not acquired 
- acq_exp
- total dollars spent on trying to acquire this prospect 
- ret_exp
- total dollars spent on trying to retain this customer 
- acq_exp_sq
- square of the total dollars spent on trying to acquire this prospect 
- ret_exp_sq
- square of the total dollars spent on trying to retain this customer 
- freq
- number of purchases the customer made during that customer's lifetime with the firm, 0 if acquisition == 0 
- freq_sq
- square of the number of purchases the customer made during that customer's lifetime with the firm 
- crossbuy
- number of product categories the customer purchased from during that customer's lifetime with the firm, 0 if acquisition = 0 
- sow
- Share-of-Wallet; percentage of purchases the customer makes from the given firm given the total amount of purchases across all firms in that category 
- industry
- 1 if the customer is in the B2B industry, 0 otherwise 
- revenue
- annual sales revenue of the prospect's firm (in millions of dollar) 
- employees
- number of employees in the prospect's firm 
Examples
data(acquisitionRetention)
  str(acquisitionRetention)
Customer Acquisition Data from Chapter 3
Description
Customer Acquisition Data from Chapter 3
Usage
  customerAcquisition
Format
Data frame with the following 17 variables
- customer
- customer number (from 1 to 500) 
- acquisition
- 1 if the prospect was acquired, 0 otherwise 
- first_purchase
- dollar value of the first purchase (0 if the customer was not acquired) 
- clv
- the predicted customer lifetime value score. It is 0 if the prospect was not acquired or has already churned from the firm. 
- duration
- time in days that the acquired prospect has been or was a customer, right-censored at 730 days 
- censor
- 1 if the customer was still a customer at the end of the observation window, 0 otherwise 
- acq_expense
- dollars spent on marketing efforts to try and acquire that prospect 
- acq_expense_sq
- square of dollars spent on marketing efforts to try and acquire that prospect 
- industry
- 1 if the customer is in the B2B industry, 0 otherwise 
- revenue
- annual sales revenue of the prospect's firm (in millions of dollar) 
- employees
- number of employees in the prospect's firm 
- ret_expense
- dollars spent on marketing efforts to try and retain that customer 
- ret_expense_sq
- square of dollars spent on marketing efforts to try and retain that customer 
- crossbuy
- the number of categories the customer has purchased 
- frequency
- the number of times the customer purchased during the observation window 
- frequency_sq
- the square of the number of times the customer purchased during the observation window 
Examples
data(customerAcquisition)
  str(customerAcquisition)
Customer Churn Data from Chapter 6
Description
Customer Churn Data from Chapter 6
Usage
  customerChurn
Format
Data frame with the following 11 variables
- customer
- customer number (from 1 to 500) 
- duration
- time in days that the acquired prospect has been or was a customer, right-censored at 730 days 
- censor
- 1 if the customer was still a customer at the end of the observation window, 0 otherwise 
- avg_ret_exp
- average number of dollars spent on marketing efforts to try and retain that customer per month 
- avg_ret_exp_sq
- square of the average number of dollars spent on marketing efforts to try and retain that customer per month 
- total_crossbuy
- total number of categories the customer has purchased during the customer's lifetime 
- total_freq
- total number of purchase occasions the customer had with the firm in the customer's lifetime 
- total_freq_sq
- square of the total number of purchase occasions the customer had with the firm in the customer's lifetime 
- industry
- 1 if the customer is in the B2B industry, 0 otherwise 
- revenue
- annual sales revenue of the prospect's firm (in millions of dollar) 
- employees
- number of employees in the prospect's firm 
Examples
data(customerChurn)
  str(customerChurn)
Demographics Data for Customer Retention (Chapter 4)
Description
Demographics Data for Customer Retention (Chapter 4)
Usage
  customerRetentionDemographics
Format
Data frame with the following 8 variables
- customer
- customer number (from 1 to 500) 
- gender
- 1 if the customer is male, 0 if the customer is female 
- married
- 1 if the customer is married, 0 if the customer is not married 
- income
- 1 if income < \$30,000 2 if \$30,001 < income < \$45,000 3 if \$45,001 < income < \$60,000 4 if \$60,001 < income < \$75,000 5 if \$75,001 < income < \$90,000 6 if income > \$90,001 
- first_purchase
- value of the first purchase made by the customer in quarter 1 
- loyalty
- 1 if the customer is a member of the loyalty program, 0 if not 
- sow
- share-of-wallet; the percentage of purchases the customer makes from the given firm given the total amount of purchases across all firms in that category 
- clv
- discounted value of all expected future profits, or customer lifetime value 
Examples
data(customerRetentionDemographics)
  str(customerRetentionDemographics)
Lifetime Duration Data for Customer Retention (Chapter 4)
Description
Lifetime Duration Data for Customer Retention (Chapter 4)
Usage
  customerRetentionLifetimeDuration
Format
Data frame with the following 8 variables
- customer
- customer number (from 1 to 500) 
- x
- The number of transactions by a given customer over all time periods. Here we assume that it is the sum of the variable Purchase where customers at most made 1 purchase per quarter. 
- tx
- time of the last transaction, i.e. the last quarter where purchase == 1 
- T
- total time between the first purchase and the end of the observation window, i.e. 12 quarters for all customers 
See Also
customerRetentionTransactions
Examples
data(customerRetentionLifetimeDuration)
  str(customerRetentionLifetimeDuration)
Transactions Data for Customer Retention (Chapter 4)
Description
Transactions Data for Customer Retention (Chapter 4)
Usage
  customerRetentionTransactions
Format
Data frame with the following 7 variables
- customer
- customer number (from 1 to 500) 
- quarter
- quarter (from 1 to 12) where the transactions occurred 
- purchase
- 1 when the customer purchased in the given quarter and 0 if no purchase occurred in that quarter 
- order_quantity
- dollar value of the purchases in the given quarter 
- crossby
- number of different categories purchased in a given quarter 
- ret_expense
- dollars spent on marketing efforts to try and retain that customer in the given quarter 
- ret_expense_sq
- square of dollars spent on marketing efforts to try and retain that customer in the given quarter 
Examples
data(customerRetentionTransactions)
str(customerRetentionTransactions)
Customer Win-Back from Chapter 7
Description
Customer Win-Back from Chapter 7
Usage
  customerWinBack
Format
Data frame with the following 10 variables
- customer
- customer number (from 1 to 500) 
- reacquire
- 1 if the customer is reacquired, 0 if not 
- duration_2
- time in days of the customer's second lifecycle with the company, 0 if not reacquired 
- slcv
- CLV of the customer in the second lifecycle 
- duration_1
- time in days of the customer's first lifecycle with the company 
- offer
- value of the offer provided to the customer for reacquisition 
- duration_lapse
- time in days since the customer was lost to when the offer to reacquire was given 
- price_change
- increase (or decrease) in price of the subscription the customer received between the first lifecycle and the second lifecycle, 0 if not reacquired 
- gender
- 1 if male, 0 if female 
- age
- age in years of the customer at the time of the attempt to reacquire 
Examples
data(customerWinBack)
str(customerWinBack)