Forecasting Conversion Rate for Real Time CPC Bidding With Target ROAS

Abstract

For bidding in real time, the rate of customer conversion needs to be predicted in real time. Using the rate prediction and the target return on ad spend, a competitive CPC bid can be computed. In our study, we built two models, i.e., MoM and MCI namely, for forecasting the rate of conversion. The results we obtained by applying our models on the marketing campaigns of two startups were promising. Both MoM and MCI run in constant $O(1)$ time, and require $O(n)$ space for $n$ observations. Furthermore, both models can be updated with fresh data in $O(1)$ time; hence, they are suitable for a data streaming application where new data arrives continuously in an online manner.

Keywords

• Real time bidding
• conversion rate modeling
• return on ad spend