Data on daily spending and coupon receipt A dataset containing information on the purchasing behavior of 1582 retail store customers across 32 coupon campaigns.
couponsretailerA data frame with 50,624 rows and 27 variables:
customer identifier
period of observation: 1 = 1st period to 32 = last period
age of customer: 1 = 18-25; 2 = 26-35; 3 = 36-45; 4 = 46-55; 5 = 56-70; 6 = 71 plus
marital status: 1 = married; 0 = unmarried
dwelling type: 1 = rented; 0 = owned
number of family members: 1 = 1; 2 = 2; 3 = 3; 4 = 4; 5 = 5 plus
income group: 1 = lowest to 12 = highest
customer's daily spending at the retailer in previous period
purchases of ready-to-eat food in previous period: 1 = yes, 0 = no
purchases of meat and seafood products in previous period: 1 = yes, 0 = no
purchases of other food products in previous period: 1 = yes, 0 = no
purchases of drugstore products in previous period: 1 = yes, 0 = no
purchases of other non-food products in previous period: 1 = yes, 0 = no
coupon reception in previous period: 1 = customer received at least one coupon; 0 = customer did not receive any coupon
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
coupon reception in previous period: 1 = customer received at least one meat/seafood coupon; 0 = customer did not receive any meat/seafood coupon
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
coupon reception in previous period: 1 = customer received at least one drugstore coupon; 0 = customer did not receive any drugstore coupon
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
coupon redemption in previous period: 1 = customer redeemed at least one coupon; 0 = customer did not redeem any coupon
treatment: 1 = customer received at least one coupon in current period; 0 = customer did not receive any coupon
treatment: 1 = customer received at least one ready-to-eat food coupon; 0 = customer did not receive any ready-to-eat food coupon
treatment: 1 = customer received at least one meat/seafood coupon; 0 = customer did not receive any meat/seafood coupon
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
treatment: 1 = customer received at least one drugstore coupon; 0 = customer did not receive any drugstore coupon
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
outcome: customer's daily spending at the retailer in current period
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.