I have a customer scoring problem I'm working on specifically on predicting conversion and coming up with a probability score on conversion (using xgboost classifier atm). There's a feature I want to introduce, but I am having a hard time formulating what the feature definition should be.

Specifically, I know that when an event A happens recently (eg, customer phones our office), that is an indicator that the customer is interested in our product and might convert. So to do this, I created a recency feature that is basically: (today - event date) in days.

The problem is that this does not capture the influence of older customer records. For example, a customer might have called us a year ago (event A triggered) and converted soon thereafter and using that formula, the recency feature will be relatively large. I want the model to learn that low recency values translate to higher probability.

Are there any good ways to engineer the feature to capture this relationship?

  • $\begingroup$ What if you assume that after $X$ days the the call did not result in a conversion, then you could have two fields, one capturing whether the customer converted, and then a second specifying the number of days, which could be $min(\text{last call} - \text{day of conversion}, X)$ $\endgroup$
    – nwaldo
    Commented May 15 at 14:54
  • $\begingroup$ @nwaldo I tried something similar as a feature and that resulted in overfitting. I think what was happening is say, all of the customers who converts have called us, but not all customers who call will convert. And so the model attributes a super high probability to every customers who calls $\endgroup$ Commented May 15 at 18:20

1 Answer 1


I think an exponential decay or RBF feature would map close dates towards 1.0, and distant dates to smaller values (approaching zero in the limit). In particular, consider this formulation:

$\mathrm{call\_score}=\exp\{-[min(\mathrm{current\_date},~\mathrm{conversion\_date\_if\_available) - \mathrm{call\_date}}]\}$

We can break it down into the following scenarios which cover all bases:

  • If a customer called long ago, and shortly thereafter converted, the feature would be close to 1.

  • If a customer called recently, and converted shortly after, the feature would be close to 1.

    • These two cases capture: short conversion times score highly
  • If a customer called recently, and hasn't converted, their score would still be high.

    • We consider them to be at a high likelihood of converting, having called recently despite not yet converting.
  • If a customer called long ago, but took a while to convert, their score will be decayed according to how long they took.

  • If a customer called long ago, and still hasn't converted, they'd have the lowest score of all.

    • These two cases capture: if it has been a while since you've called, and then converted late or not at all, you score low.

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.