[malshe]

If I understand it correctly, you estimate the probability of purchase given the user characteristics, behaviors, etc. If this probability is below a cutoff, you offer a discount. Did I get it right?

Is the cutoff itself a function of other variables in the data?

[pmoot]

Yes, that's mostly right. We also vary the discount value, so it's less a binary discount/no discount and more a range. There is often a cutoff though. Merchants can input a hard cutoff e.g. if they want to ensure everyone gets a discount (great if they also have marketing assets for a sale), or if they want to avoid making their sites feel too 'sales-y'. Otherwise the cutoff is defined by conversion prediction, inventory levels, and a few other inputs.

There's actually a lot more we could do to make this cutoff more intelligent though - e.g. at Uber the cutoff was set to exhaust a certain promotional budget. Or we could target a specific ROI if we eventually have good enough predictions.

[malshe]

Thanks for the reply. Do you use Bayesian models for this? Btw, Pete Fader[1] has done so much work in customer valuation where estimating the probability of purchase is a crucial aspect. Maybe you already use them.

[1] https://marketing.wharton.upenn.edu/profile/faderp/#overview

[pmoot]

We're using a neural network, not a bayesian model. And we haven't used Pete Fader's work, but thank you for the resource.