couponsretailer: Data on daily spending and coupon receipt A dataset...

In causalweight: Estimation Methods for Causal Inference Based on Inverse Probability Weighting

Data on daily spending and coupon receipt A dataset containing information on the purchasing behavior of 1582 retail store customers across 32 coupon campaigns.

Description

Data on daily spending and coupon receipt A dataset containing information on the purchasing behavior of 1582 retail store customers across 32 coupon campaigns.

Usage

couponsretailer

Format

A data frame with 50,624 rows and 27 variables:

customer_id

customer identifier

period

period of observation: 1 = 1st period to 32 = last period

age_range

age of customer: 1 = 18-25; 2 = 26-35; 3 = 36-45; 4 = 46-55; 5 = 56-70; 6 = 71 plus

married

marital status: 1 = married; 0 = unmarried

rented

dwelling type: 1 = rented; 0 = owned

family_size

number of family members: 1 = 1; 2 = 2; 3 = 3; 4 = 4; 5 = 5 plus

income_bracket

income group: 1 = lowest to 12 = highest

dailyspending_preperiod

customer's daily spending at the retailer in previous period

purchase_ReadyEatFood_preperiod

purchases of ready-to-eat food in previous period: 1 = yes, 0 = no

purchase_MeatSeafood_preperiod

purchases of meat and seafood products in previous period: 1 = yes, 0 = no

purchase_OtherFood_preperiod

purchases of other food products in previous period: 1 = yes, 0 = no

purchase_Drugstore_preperiod

purchases of drugstore products in previous period: 1 = yes, 0 = no

purchase_OtherNonfood_preperiod

purchases of other non-food products in previous period: 1 = yes, 0 = no

coupons_Any_preperiod

coupon reception in previous period: 1 = customer received at least one coupon; 0 = customer did not receive any coupon

coupons_ReadyEatFood_preperiod

coupon reception in previous period: 1 = customer received at least one ready-to-eat food coupon; 0 = customer did not receive any ready-to-eat food coupon

coupons_MeatSeafood_preperiod

coupon reception in previous period: 1 = customer received at least one meat/seafood coupon; 0 = customer did not receive any meat/seafood coupon

coupons_OtherFood_preperiod

coupon reception in previous period: 1 = customer received at least one coupon applicable to other food items; 0 = customer did not receive any coupon applicable to other food items

coupons_Drugstore_preperiod

coupon reception in previous period: 1 = customer received at least one drugstore coupon; 0 = customer did not receive any drugstore coupon

coupons_OtherNonfood_preperiod

coupon reception in previous period: 1 = customer received at least one coupon applicable to other non-food items; 0 = customer did not receive any coupon applicable to other non-food items

coupons_Any_redeemed_preperiod

coupon redemption in previous period: 1 = customer redeemed at least one coupon; 0 = customer did not redeem any coupon

coupons_Any

treatment: 1 = customer received at least one coupon in current period; 0 = customer did not receive any coupon

coupons_ReadyEatFood

treatment: 1 = customer received at least one ready-to-eat food coupon; 0 = customer did not receive any ready-to-eat food coupon

coupons_MeatSeafood

treatment: 1 = customer received at least one meat/seafood coupon; 0 = customer did not receive any meat/seafood coupon

coupons_OtherFood

treatment: 1 = customer received at least one coupon applicable to other food items; 0 = customer did not receive any coupon applicable to other food items

coupons_Drugstore

treatment: 1 = customer received at least one drugstore coupon; 0 = customer did not receive any drugstore coupon

coupons_OtherNonfood

treatment: 1 = customer received at least one coupon applicable to other non-food items; 0 = customer did not receive any coupon applicable to other non-food items

dailyspending

outcome: customer's daily spending at the retailer in current period

References

Langen, Henrika, and Huber, Martin (2023): "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign." PLoS ONE, 18 (1): e0278937.

causalweight documentation built on May 4, 2023, 5:10 p.m.