Ecommerce ad images enhanced by targeted click-driven editing
Evolving Creatives with Clicks: CTR-Driven Advertising image Editing via Attribute Disentanglement and Preference Alignment
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Online advertising aims to increase how often people click on ads instead of just making the images look nice. The authors found that changing every part of an underperforming ad image is not ideal since some parts actually help clicks. They created a method called ECC that focuses only on tweaking the parts of the image that reduce clicks while keeping the good ones. This approach uses statistical analysis to find key image traits linked to clicks and learns from real click patterns to improve advertising images. Their tests show ECC makes ad images get clicked on much more than other methods.
What this means in practice
- •For digital marketing teams: Improve online ad images by selectively refining features that negatively impact user clicks, increasing overall campaign effectiveness.
- •For advertising platform developers: Integrate fine-grained attribute editing based on user click data to automatically optimize ad creative assets for higher engagement rates.$Commercial implications: Enables automated enhancement tools for ad platforms to improve client ROI by increasing click-through rates on displayed creatives.
Authors
Xu Zhang, Haohan Wang, Zheng Zhang, Yingjie Xia, Wei Feng, Honghe Zhu, Jingjing Lv, Changping Peng, Xuheng Qin, Zehua Zhang, Zheng Luo, Qicheng Li
Abstract
The optimization objective for e-commerce advertising creatives has shifted from aesthetic quality to online business metrics like click-through rate (CTR). Recent methods generate appealing ad images from product images by constructing CTR-based reward signals and refining prompt generation via Reinforcement Learning. Such paradigms equally penalize all attributes within an underperforming image, even though some are actually beneficial to clicks. In this work, we propose Evolving Creatives with Clicks (ECC), which reformulates creative optimization as fine-grained editing rather than generation from scratch. ECC selectively refines attributes that hurt CTR while preserving those that contribute positively. Specifically, faced with a rich attribute space across massive images, ECC first identifies 28 attributes significantly correlated with CTR via ANOVA and Random Forest regression. Based on attribute contrasts between images with disparate CTRs, we instruction-tune a prompt model to generate editing suggestions, then refine it with real click preferences since reference-derived suggestions are not uniformly beneficial. Furthermore, we introduce Consistent Preference Optimization (CPO) as ECC's preference optimization strategy, which enforces consistency across semantically equivalent shuffled suggestions to mitigate DPO overfitting and improve preference generalization. Extensive experiments demonstrate that ECC significantly improves the CTR of ad images, outperforming state-of-the-art models by a substantial margin. The complete data pipeline, including attribute extraction and analysis, the resulting dataset, and source code will be released upon acceptance.