DATA MINING CUSTOMER CLUSTERING USING K-MEANS METHOD
Keywords:
Data Mining, K-Means Clustering, Customer Grouping.Abstract
The company recognizes the crucial role of customers in achieving business success and as the main source of revenue. Therefore, it is important for companies to understand the needs and desires of customers in order to build a mutually beneficial relationship. Customers have functional and emotional needs that they want to fulfill through the products or services they buy. Customer experience, both positive and negative, has a significant impact on satisfaction, loyalty and corporate image. This research faces the challenge of decreasing the number of customers who make purchases at these companies or service providers. To overcome this problem, companies need to adopt an effective market strategy to improve operational efficiency and better understand customer needs. One approach used is to understand customer needs through grouping, so that companies can develop products or services that are more suitable for each customer group. This helps improve the product's relationship with customer needs and provides services that match their expectations. Customer grouping was performed using the K-means algorithm, with 47 customers grouped based on relevant attributes. Determining the optimal number of clusters is done by comparing the performance of the clusters that are formed, and the results produce two new clusters with different numbers of customers. The K-means algorithm is implemented using the RapidMiner application to simplify the process. The final analysis shows that the second cluster has more customers than the first cluster. This research confirms the importance of understanding customer needs, classifying them appropriately, and taking effective actions to maintain customer satisfaction. The K-means algorithm and the RapidMiner application prove to be very useful in this process, enabling companies to strengthen customer relationships and create significant added value. The final results of this study indicate that the first cluster (cluster 0) contains 22 customers, while the second cluster (cluster 2) contains 25 customers. Therefore, the second cluster has a larger number of customers compared to the first cluster.
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