Aspect-based Analysis of Advertising Appeals for Search Engine Advertising

TL;DR

This paper investigates how different advertising appeals (A$^3$) influence search engine ad effectiveness across industries, using a dataset and aspect detection model to identify industry-specific appeals and their impact on performance.

Contribution

It introduces an industry-specific analysis of advertising appeals and demonstrates how identifying effective A$^3$ can improve ad performance estimation.

Findings

Different industries have unique effective A$^3$

Identification of A$^3$ aids in estimating ad performance

Created a dataset and used an existing model for aspect detection

Abstract

Writing an ad text that attracts people and persuades them to click or act is essential for the success of search engine advertising. Therefore, ad creators must consider various aspects of advertising appeals (A$^3$) such as the price, product features, and quality. However, products and services exhibit unique effective A$^3$ for different industries. In this work, we focus on exploring the effective A$^3$ for different industries with the aim of assisting the ad creation process. To this end, we created a dataset of advertising appeals and used an existing model that detects various aspects for ad texts. Our experiments demonstrated that different industries have their own effective A$^3$ and that the identification of the A$^3$ contributes to the estimation of advertising performance.