Measuring Similarity between Brands using Followers' Post in Social Media

TL;DR

This paper introduces a novel social media-based measure to quantify brand similarity by analyzing followers' posts, which can predict user interest in brands with high correlation, aiding targeted marketing strategies.

Contribution

The paper presents a new algorithm that analyzes followers' social media posts to measure brand similarity, incorporating clustering and histogram conversion for better prediction of user interest.

Findings

The method predicts user interest in brands with a correlation over 0.53.

Purchase data predicts co-purchase behavior well, but not willingness to buy new brands.

The proposed social media analysis outperforms traditional purchase data in estimating brand interest.

Abstract

In this paper, we propose a new measure to estimate the similarity between brands via posts of brands' followers on social network services (SNS). Our method was developed with the intention of exploring the brands that customers are likely to jointly purchase. Nowadays, brands use social media for targeted advertising because influencing users' preferences can greatly affect the trends in sales. We assume that data on SNS allows us to make quantitative comparisons between brands. Our proposed algorithm analyzes the daily photos and hashtags posted by each brand's followers. By clustering them and converting them to histograms, we can calculate the similarity between brands. We evaluated our proposed algorithm with purchase logs, credit card information, and answers to the questionnaires. The experimental results show that the purchase data maintained by a mall or a credit card company can predict the co-purchase very well, but not the customer's willingness to buy products of new brands. On the other hand, our method can predict the users' interest on brands with a correlation value over 0.53, which is pretty high considering that such interest to brands are high subjective and individual dependent.