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Enhancing Car Showroom Profitability through K-Means Clustering for Customer Segmentation
Indonesia is known to be one of Southeast Asia’s largest markets for used cars. Used car showrooms in Indonesia are numerous and varied, some focus on one car brand, some focus on the lower middle class, some focus on the upper middle class, and some provide all types of cars. One of them is PQR Showroom, Even though PQR Showroom was able to generate such a great amount of sales, the profits generated by the PQR showroom are not proportional to the amount of capital invested.
To increase the profit, we proposed the solution using descriptive analytics and prescriptive analytics using K-Means. We also carry out simulations by comparing sales in existing years with the results of the descriptive and prescriptive analytics that have been made for the expected profit.
The results show that the simulation comparison with the data we have obtained from descriptive and prescriptive analysis gives the best-expected profit compared to the initial sales results at the PQR Showroom.
It shows that the data using descriptive and K-Means is great than before. The fastest cars that have been sold is LCGC and the most wanted car is MPY Toyota Avanza and the best profit that can generate is SUV Toyota Fortuner.