## January 12, 2016

### Targeting Variables in Practice

Here's an example of targeting variables in practice ... I am predicting the annual probability of a customer purchasing from weighted category 11.

It's a logistic regression equation:
• Logit = -1.851 + 0.018*(weighted category 10 / 1000) + 0.051*(weighted category 11 / 1000) + 0.006*(weighted category 13).
• Annual Response = EXP(Logit) / (1 + EXP(Logit)).
Ok, enough of the geeky stuff.

Let's say you wanted to populate your email campaign with the four most likely categories, sort of like what you see below:

You go into your predictions at a category level:
• Category 01 = 38%.
• Category 02 = 19%.
• Category 03 = 77%.
• Category 04 = 2%.
• Category 05 = 4%.
• Category 06 = 6%.
• Category 07 = 27%.
• Category 08 = 1%.
• Category 09 = 3%.
• Category 10 = 18%.
• Category 11 = 9%.
• Category 12 = 5%.
• Category 13 = 11%.
Clearly, this customer is likely to buy from categories 03 / 01 / 07 / 02, with category 10 a close fifth. Pick an item from these categories, populate your email campaign, and send customers to an appropriate landing page.

You can update your scores on a hourly / daily / weekly basis, whatever works for you. Work with your email service provider to populate based on category scores, and you've just improved the productivity of your email campaigns by 20%!

And if you are a catalog marketer, this is how you are going to paginate your personalized merchandise assortment in future catalogs.

Yes - use whatever modeling methodology you like - you don't have to copy my logistic regression structure using weighted variables - use whatever tactic you like.

Make sense?