Marketing Customer analytics ( SAS )
Budget: $10 – $30 USD
Tuscan Lifestyles: Lifts and Gains
A data scientist conducted RFM and logistic analyses for mailing offer for Tuscan Lifestyles catalog. For RFM, she created tiles using recency, frequency, and monetary quintiles (rfm_index) and estimated response rate for these tiles (rfm_iq). Next, she wants to compare predictive power of RFM model using lifts and gains. She splits observations in the dataset into deciles by rfm_iq variable and created a new variable (rank_rfm_iq) that reflects decile number (lower decile number corresponds to the higher values of rfm_iq). A new variable is saved in the dataset Catalog_LG that contains the same items for 96K customers from home assignment RFM:
A data scientist conducted RFM and logistic analyses for mailing offer for Tuscan Lifestyles catalog. For RFM, she created tiles using recency, frequency, and monetary quintiles (rfm_index) and estimated response rate for these tiles (rfm_iq). Next, she wants to compare predictive power of RFM model using lifts and gains. She splits observations in the dataset into deciles by rfm_iq variable and created a new variable (rank_rfm_iq) that reflects decile number (lower decile number corresponds to the higher values of rfm_iq). A new variable is saved in the dataset Catalog_LG that contains the same items for 96K customers from home assignment RFM: