Clustering Behavioral Data for Advertising Purposes using K-Prototypes Algorithm
Kiefer Stefano Ranti1, Kelvin Salim2, Andary Dadang Yuliyono3, Abba Suganda Girsang4
1Kiefer Stefano Ranti*, Computer Science Department, BINUS Graduate Program-Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
2Kelvin Salim, Computer Science Department, BINUS Graduate Program-Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
3Andary Dadang Yuliono, Computer Science Department, BINUS Graduate Program-Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
4Abba Suganda Girsang, Computer Science Department, BINUS Graduate Program-Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
Manuscript received on October 16, 2019. | Revised Manuscript received on 24 October, 2019. | Manuscript published on November 10, 2019. | PP: 2329-2334 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5229119119/2019©BEIESP | DOI: 10.35940/ijitee.A5229.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Behavioral Data, Cluster Analysis, Interest, K-Prototypes.
Scope of the Article: Clustering