Advertiser Bidding Prediction and Optimization in Online Advertising

We study the problem of optimal bid selection across ads and time, with the aim to maximize incoming click traffic to the advertiser’s landing page, which is directly translated in maximizing revenue. A major novelty of our approach lies in using Machine Learning (ML) to build regression models out of available data for deriving for each ad the relations, (i) cost-per-click (CPC) charged by the platform versus bid, (ii) assigned ad position in the ad list versus bid value, and (iii) number of ad clicks versus its position. These regression models naturally reveal hidden trends that would have been otherwise unavailable to the advertiser, such as the bidding behavior of competing advertisers and quality scores of their ads. We then incorporate these relations into a convex optimization problem of budget allocation across ads and across time, the solution of which is the optimal bidding strategy of the advertiser. We validate our approach with real data provided by an online advertising company that is active in the banking sector. Our solution leads to substantial increase in the amount of inbound click traffic to the advertiser’s landing page compared to other approaches that are either heuristic and data-agnostic or employ simple statistics on data.