Evaluating the Effectiveness of Digital Product Advertisement Type using Machine Learning and Shapley Additive Explanations Analysis

Digital advertising continues to grow rapidly, yet advertisers face persistent uncertainty in selecting the most effective ad format for different campaign objectives and audience segments. Prior studies often rely on limited metrics or lack interpretability, making it difficult to explain why certain formats perform better. This study addresses this gap by evaluating ad format effectiveness using explainable machine learning. This study evaluates the effectiveness of image and video advertisements (ads) across five key performance indicators (KPIs): reach, impressions, link clicks, cost per click (CPC), and cost per thousand impressions (CPM). A dataset of 4,526 campaigns from Meta Ads Manager (July 2021–August 2024) was analysed using machine learning models integrated with Shapley Additive Explanations (SHAP). Model performance was assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The results showed that video ads achieved higher reach and impressions with lower CPM. Meanwhile, image ads delivered lower CPCs and stronger click performance, particularly among users aged 13–17 and 55+. Aside from confirming established format patterns, this study made certain contributions by applying explainable machine learning in a large-scale, non-Western context and by clarifying the mechanisms behind ad performance. The findings offered actionable guidance; for example, video ads were optimal for awareness and visibility objectives. Additionally, image ads were preferable for engagement-driven campaigns.