Our client is a prominent US-based retailer. Direct mail advertising constituted a significant portion of their advertising budget, in which a set of households were selected based on a set of heuristic rules including customer RFM metrics, geography, etc. While the heuristic selected the best households for each campaign, the ability to select households that delivered highest incremental in response to the campaign was limited.
Hence, the key objective was to build a data science driven approach to:
— Accurately measure the baseline purchase propensity for each household
— Accurately measure the incremental purchase propensity for each household, in response to a campaign
— Use the combination of the above to cherry-pick households to maximize the sales and margin impact
Selection strategy driven by this approach helped identify 5-15% margin improvement opportunities from campaign to campaign
This approach also indicated a potential opportunity to standardize the household selection process for campaigns vs designing heuristic rules on a campaign to campaign basis