Package 'SMCRM'

Title: Data Sets for Statistical Methods in Customer Relationship Management by Kumar and Petersen (2012).
Description: Data Sets for Kumar and Petersen (2012). Statistical Methods in Customer Relationship Management, Wiley: New York.
Authors: Tobias Verbeke, based on datasets provided on the book's website
Maintainer: Tobias Verbeke <[email protected]>
License: GPL-3
Version: 0.0-3
Built: 2024-11-22 05:21:21 UTC
Source: https://github.com/cran/SMCRM

Help Index


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)