Display advertising: Estimating conversion probability efficiently
This work addresses a domain-specific problem for advertisers and marketers by providing a more efficient estimator, though it is incremental as it builds on existing logistic regression and joint models.
The paper tackled the problem of efficiently estimating conversion probability in online display advertising, addressing delays in observing conversions and large data sets, and proposed a compromise estimator that balances bias and computational cost, applied to a Criteo data set.
The goal of online display advertising is to entice users to "convert" (i.e., take a pre-defined action such as making a purchase) after clicking on the ad. An important measure of the value of an ad is the probability of conversion. The focus of this paper is the development of a computationally efficient, accurate, and precise estimator of conversion probability. The challenges associated with this estimation problem are the delays in observing conversions and the size of the data set (both number of observations and number of predictors). Two models have previously been considered as a basis for estimation: A logistic regression model and a joint model for observed conversion statuses and delay times. Fitting the former is simple, but ignoring the delays in conversion leads to an under-estimate of conversion probability. On the other hand, the latter is less biased but computationally expensive to fit. Our proposed estimator is a compromise between these two estimators. We apply our results to a data set from Criteo, a commerce marketing company that personalizes online display advertisements for users.