| Summary: | 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 <inline-formula> <tex-math notation="LaTeX">$O(1)$ </tex-math></inline-formula> time, and require <inline-formula> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula> space for <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> observations. Furthermore, both models can be updated with fresh data in <inline-formula> <tex-math notation="LaTeX">$O(1)$ </tex-math></inline-formula> time; hence, they are suitable for a data streaming application where new data arrives continuously in an online manner.
|
|---|