Digital advertising enables advertisers to promote their products on various online and digital channels. Real-Time Bidding is an advanced advertising method which allows advertisers to target potential buyers and acquire ad space on websites, in the form of programmatic auctions. Hundreds of billions of such auctions take place every day. Predicting future performance and developing customized targeting strategies enables advertisers to make better use of their budgets and ultimately improve their KPI measures. With Google’s recent policy on phasing out third-party cookies, organizations around the world are shifting more towards data-protected contextual targeting strategies. This paper proposes a machine learning-based approach to predicting future ad-campaign performance by focusing on contextual features such as browser, operating system, device type, and so on. First, a custom metric encompassing cost, performance and campaign delivery is developed. This metric’s predicted value is used to score and recommend targeting strategies. To generate new features, feature engineering techniques such (The CMO’s Guide to Programmatic Buying, n.d.)as Lag feature generation, statistical encodings, Graph- based embeddings for websites and cyclical feature encodings are prepared for downstream tasks such as CTR prediction This paper then compares and chooses the best performing linear, tree-based, and deep learning models for our proposed hypotheses. Finally, we develop heuristic criteria for offline testing of the recommended strategies and calculate theoretical performance uplift for comparison with older ranker score methodologies, which serve as our baseline results. Our proposed solution outperforms the baseline solution and proposes a novel way of recommending strategies.
