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[2602.13852] Experimentation Accelerator: Interpretable Insights and Creative Recommendations for A/B Testing with Content-Aware ranking
Summary
The paper presents the Experimentation Accelerator, a framework that enhances A/B testing by providing interpretable insights and creative recommendations using content-aware ranking.
Why It Matters
This research addresses key challenges in online experimentation, such as traffic scarcity and inconsistent post-hoc analysis. By leveraging historical A/B results and content embeddings, it aims to optimize testing processes, making them more efficient and insightful. This is particularly relevant for businesses looking to enhance their marketing strategies through data-driven decision-making.
Key Takeaways
- Introduces a unified framework for prioritizing A/B test variants.
- Enhances interpretability of results through semantic marketing attributes.
- Utilizes LLMs for generating actionable creative suggestions.
- Demonstrates real-world application and validation through Adobe's product.
- Addresses common bottlenecks in online experimentation effectively.
Computer Science > Artificial Intelligence arXiv:2602.13852 (cs) [Submitted on 14 Feb 2026] Title:Experimentation Accelerator: Interpretable Insights and Creative Recommendations for A/B Testing with Content-Aware ranking Authors:Zhengmian Hu, Lei Shi, Ritwik Sinha, Justin Grover, David Arbour View a PDF of the paper titled Experimentation Accelerator: Interpretable Insights and Creative Recommendations for A/B Testing with Content-Aware ranking, by Zhengmian Hu and 4 other authors View PDF HTML (experimental) Abstract:Modern online experimentation faces two bottlenecks: scarce traffic forces tough choices on which variants to test, and post-hoc insight extraction is manual, inconsistent, and often content-agnostic. Meanwhile, organizations underuse historical A/B results and rich content embeddings that could guide prioritization and creative iteration. We present a unified framework to (i) prioritize which variants to test, (ii) explain why winners win, and (iii) surface targeted opportunities for new, higher-potential variants. Leveraging treatment embeddings and historical outcomes, we train a CTR ranking model with fixed effects for contextual shifts that scores candidates while balancing value and content diversity. For better interpretability and understanding, we project treatments onto curated semantic marketing attributes and re-express the ranker in this space via a sign-consistent, sparse constrained Lasso, yielding per-attribute coefficients and signed contrib...