Dynamic Bidding with Contextual Bid Decision Trees in Digital Advertisement

Abstract

Real-time bidding (RTB) has been one of the most prominent technological advances in online display advertising. Billions of transactions in the form of programmatic advertising auctions happen on a daily basis on ad-networks and exchanges, where advertisers compete for the ad slots by bidding for that slot. The question: how much should I bid? has lingered around and troubled many marketers from a long time. Past strategies’ formulation has been mostly based on targeting users by analyzing their browsing behavior via cookies to predict the likelihood that they will interact with the ad. But due to growing privacy concerns where browsers are taking down cookies and recent regulations like General Data Protection Regulation (GDPR) in Europe, targeting users has become difficult and these bidding methodologies fail to deliver. This paper presents a novel approach to tackle the dual problem of optimal bidding and finding an alternative to user-based targeting by focusing on contextual-level targeting using features like site-domain, keywords, postcode, browser, operating system, etc. The targeting is done at feature combination level in the form Bid Decision Trees. The framework discussed in the paper dynamically learns and optimizes bid values for the context features based on their performance over a specific time interval using a heuristic Feedback Mechanism to optimize the online advertising KPIs: Cost per Acquisition (CPA) and Conversion Rate (CVR). A comparison of the performance of this context-based tree-bidding framework reveals a 59% lower CPA and 163% higher CVR as compared to other targeting strategies within the overall campaign budget, which are clear indicators of its lucrativeness in a world where user-based targeting is losing popularity.