Monte Carlo simulation and clustering for customer segmentation in business organization

Utilizing data for segmentation analysis can bring a streamlined way to get potential insight as of decision making support in a business organization. Using appropriate data analytical technique help the organizations in profiling their customer segments accurately. The result brings an effective marketing strategy. However, there are times in doing data analytic, the organization needs another variable of data where the value is unavailable, for example: customer's income data which mostly hard to collect. By using Monte Carlo simulation, the value of customer's income can be generated and then compared with customer spending to construct customer segmentation model. An unsupervised learning for customer segmentation model using K-Means clustering enables us to see the grouping patterns of customer's income towards their spending. Clusters of the dataset might be interpreted as a group of customers that having a similar character. This paper shows us how to generate customer's income data and create data cluster to optimizing customer potential by utilizing data. Furthermore, the result brings us insight into which group of the customer might unserved properly considering their average income with their spending behavior.