Machine Learning-Based Optimization of E-Commerce Advertising Campaigns

Abstract

E-commerce platforms facilitate the generation of advertisement campaigns by retailers for the purpose of promoting their products. Marketers need to generate demand for their products by means of online advertising (ad). Game theoretic and continuous experimentation feedback-based advertising optimization is imperative to enable efficient and effective advertising at scale. To address this, we propose a solution that utilizes machine learning and statistical techniques to optimize e-commerce ad campaigns, intending to create an optimal and targeted ad campaign strategy. The dataset utilized here is Amazon’s e-commerce dataset obtained from a prominent e-commerce firm. The proposed work examines these key approaches: For predicting profitability and campaign impressions, we implemented a model using the first approach, blending statistical techniques with machine-learning algorithms. The results provide a comparison between the algorithms, offering insights into the observed outcomes. In the second approach, we leverage the k-means clustering algorithm and Bayesian Information Criterion (BIC) technique to establish a correlation between keyword performance, campaign profitability, and bidding strategies. In the concluding approach, we introduce an innovative model that uses Joint Probability Distribution and Gaussian functions to determine the profitability of ad campaigns. This model generates multivariate-density graphs, enabling a comprehensive exploration to better comprehend and predict profitability, specifically in terms of Return on Ad Spend (ROAS). For example, we can now answer questions like: How do the profitability (ROAS) and awareness (%impression share) of a campaign change with variations in the budget? How do the profitability (ROAS) and awareness (%impression share) of a keyword change with different bid values? These insights provide valuable information for optimizing campaign performance and making informed decisions regarding budget allocation, bid adjustments, and overall campaign structure. The results offer practical insights for optimizing an ad campaign’s performance through developing effective and targeted strategies.