Clustering behavioral data for advertising purposes using K-prototypes algorithm

Abstract

Understanding the customer sentiment is very important when it comes to advertising. To appeal to their current and potential customers, a company must understand the market interests. Companies can segment their customers by using surveys and telemetry data to get to know the customer’s interests. One way of segmenting the customer is by grouping or clustering them according to their interests and behaviors. In this study, the k-prototypes clustering algorithm, which is an improved combination of k-means and k-modes algorithm, will be used to cluster a behavioral data that contains both numerical and categorical attribute, obtained from a survey conducted on teenagers into clusters of 4, 5, and 6. Each cluster will contain teenagers with certain behavior different from other clusters. And then by analyzing the results, advertisers will be able to define a profile that indicates their interests regarding the internet, social media and text messaging, effectively revealing the kind of ad that would be relatable for them.