UNDERSTANDING SUCCESSFUL IMAGE ADS: NEW INSIGHTS FROM THEORY-DRIVEN COMPUTATIONAL ANALYSES

UNDERSTANDING SUCCESSFUL IMAGE ADS:  NEW INSIGHTS FROM THEORY-DRIVEN COMPUTATIONAL ANALYSES

Abstract

Successful image ads are liked, shared, and remembered. Although the success of an image ad can often be understood with the benefit of hindsight, explaining whether an image ad will be received favorably by its target group is more difficult. This paper introduces a theoretical framework to disentangle image-related factors that influence liking, sharing, and recognition responses. The proposed framework is tested with the help of Facebook image ads posted by three fast-food chains. Both human judgments and computational measures of aesthetic features of image ads allow for important insights into how image ads are processed perceptually. Moreover, the theoretical framework, in combination with the computational measures, provides significant benefits for image-ad effectiveness research: It identifies the features of an image ad that explain its performance, is easy to implement, and is scalable.

More information on Prof. Dr. Ulf Böckenholt can be found here.

The talk will take place from 12:15-13:30 in RuW 1.201.