Response Modeling If you run a catalog based retail business – that is, if you are heavily dependent on regular catalog mailers for reaching out to your customers, good response modeling is crucial to cutting marketing costs and enhancing return on your marketing dollars. It is even more crucial to targeting the more responsive customers – enabling you to improve your ROI.
The philosophy is simple: mail the catalogs to people most likely to come back and make a purchase. This way you make the most revenue while mailing the least catalogs.
Getting good at it! There are several mathematical and statistical techniques that can be employed to make a good response model. But even before you start employing fancy math, there’s a lot you can do at the data collection step itself that can improve your modeling. You can sit down and find the most pertinent variables to be considered for the model, for instance.
But an even easier trick is to not model response by customer, but model response by household.
Multiple customers at each household Often, retail businesses attract more than one customer per household. For instance, husband, wife and daughter might have all bought apparel from your catalog – three customers from the same catalog. The database might have even recorded two/three customers when there is really just one – sometimes in their haste to record a sale, the sales rep would have assigned the customer a new customer id. So this new customer might have bought 5 items under Customer ID 1, 3 items under Customer ID 2 and 1 item under Customer ID 3. Really, he’s bought 9 items.
But even if that is not the case, and you happen to be modeling response by customer not household, the following might happen: Husband, wife, daughter all placed 2 orders each – and hence any customer based model might not give any of them a high rank. But the household – has 6 orders! The household itself will get a higher rank in the model, as it should. Someone from the household will make a purchase!
Testing time! In order to test this theory out, we made a customer based response model, then using the same data – first rolled up variables to the household level – then, made a household level response model. We had about 1.4M households to contend with and we ranked each customer/household on its propensity to return and make a purchase. We then ordered these ranks in 100K segments and found out how many customers had returned per segment.
Results Both models ordered the file really well – at 50% of the file, both models had more than 90% of the response coming in. However the house hold based model comfortably beat the customer based one. The graph shows the % of response captured per the each 100K ranks of the two models.
So at 700K, for instance, the customer based model captured 92% of the total responders while the household based model captured 95% of the total response. The usual catalog circulation size for this client was 700K and this improved model would have brought in an additional 3% of the responders for the same number of catalogs mailed. This would have equated to roughly $77K per month – an amount of no insignificance.
Or looking at it another way, the new model would have brought in the same amount of money by mailing 100K lesser catalogs (since the new model brings in the 92% response at the 600K mark). At the cost of 70 cents a catalog, that’s a saving of $70K to earn the same revenue as before.
Modeling by household thus provides a definite edge over modeling by customers. Not only is it a win in monetary savings, or increase revenue through your catalog mailing, it also ensures you send out one catalog – for each household. This way you get that one catalog for getting a sale from any or all of the possible customers you have there.