Customer profiling using K-means clustering method / Nik Asyraniasna Nik Mohd Asri

Businesses must understand customer behavior in today's ever-changing business environment in order to properly customize their marketing strategy. With the use of the K-means clustering approach, this research seeks to improve customer profiling by allowing businesses to divide their customers into discrete groups according to shared behaviors and preferences. Through the analysis of various customer data sets, such as people, products, promotion, place, the K-means algorithm can detect clusters that correspond to consistent client groups. The next phase of the project will concentrate on creating a thorough customer profiling system that makes use of these clusters to produce insightful data on customer preferences. This will allow companies to create customized marketing campaigns that appeal to certain target customers. By providing more relevant content, this strategy not only increases customer satisfaction but also improves marketing effectiveness and boosts conversion rates. In order to facilitate smooth business interactions with the profiling system, the project will incorporate the K-means clustering technique for consumer segmentation and optimize it. The end goal is to provide organizations with an effective tool that helps them better understand their customer segments, enabling them to develop more individualized and successful communication strategies in the context of a competitive market.