🤖 AI Summary
Digital advertising platforms (e.g., Meta Ads) suffer from algorithmic opacity, hindering advertisers’ understanding of audience targeting, pricing mechanisms, and ad relevance—thereby impeding data-driven decision-making. To address this, we propose SODA: the first explainable advertising analytics framework integrating multimodal text-image models with large language models (LLMs). Our method introduces a natural-language–based interactive explanation interface tailored for non-technical marketing professionals, enabling automated competitive ad summarization, attribution analysis, and click-through rate (CTR) prediction. By synergistically combining eXplainable AI (XAI) techniques with natural language generation and understanding, SODA enhances predictive accuracy while delivering actionable, trustworthy AI-assisted insights. Evaluated in real-world deployment scenarios, SODA significantly improves interpretability without compromising performance, empowering marketers to make informed, auditable decisions grounded in transparent model reasoning.
📝 Abstract
The opaqueness of modern digital advertising, exemplified by platforms such as Meta Ads, raises concerns regarding their autonomous control over audience targeting, pricing structures, and ad relevancy assessments. Locked in their leading positions by network effects, "Metas and Googles of the world" attract countless advertisers who rely on intuition, with billions of dollars lost on ineffective social media ads. The platforms' algorithms use huge amounts of data unavailable to advertisers, and the algorithms themselves are opaque as well. This lack of transparency hinders the advertisers' ability to make informed decisions and necessitates efforts to promote transparency, standardize industry metrics, and strengthen regulatory frameworks. In this work, we propose novel ways to assist marketers in optimizing their advertising strategies via machine learning techniques designed to analyze and evaluate content, in particular, predict the click-through rates (CTR) of novel advertising content. Another important problem is that large volumes of data available in the competitive landscape, e.g., competitors' ads, impede the ability of marketers to derive meaningful insights. This leads to a pressing need for a novel approach that would allow us to summarize and comprehend complex data. Inspired by the success of ChatGPT in bridging the gap between large language models (LLMs) and a broader non-technical audience, we propose a novel system that facilitates marketers in data interpretation, called SODA, that merges LLMs with explainable AI, enabling better human-AI collaboration with an emphasis on the domain of digital marketing and advertising. By combining LLMs and explainability features, in particular modern text-image models, we aim to improve the synergy between human marketers and AI systems.