The Impact of Visual Appearance on User Response in Online Display Advertising

TL;DR

This study systematically analyzes how visual features of online display ads influence user response rates, using large-scale data and experiments to identify key visual factors that improve ad performance.

Contribution

It introduces a data-driven approach with a set of 43 visual features, evaluates their effectiveness for CTR prediction, and provides the first dataset and benchmarks for this research area.

Findings

Identified key visual features impacting CTR

Developed a predictive model for ad performance based on visuals

Provided a dataset for future research in visual ad analysis

Abstract

Display advertising has been a significant source of revenue for publishers and ad networks in online advertising ecosystem. One of the main goals in display advertising is to maximize user response rate for advertising campaigns, such as click through rates (CTR) or conversion rates. Although in the online advertising industry we believe that the visual appearance of ads (creatives) matters for propensity of user response, there is no published work so far to address this topic via a systematic data-driven approach. In this paper we quantitatively study the relationship between the visual appearance and performance of creatives using large scale data in the world's largest display ads exchange system, RightMedia. We designed a set of 43 visual features, some of which are novel and some are inspired by related work. We extracted these features from real creatives served on RightMedia. We also designed and conducted a series of experiments to evaluate the effectiveness of visual features for CTR prediction, ranking and performance classification. Based on the evaluation results, we selected a subset of features that have the most important impact on CTR. We believe that the findings presented in this paper will be very useful for the online advertising industry in designing high-performance creatives. It also provides the research community with the first ever data set, initial insights into visual appearance's effect on user response propensity, and evaluation benchmarks for further study.