Articles

Enhancing Car Showroom Profitability through K-Means Clustering for Customer Segmentation

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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.

Determining Marketing Mix of CV Nutri Pro by Using Big Data Analytics

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Technological growth supports the acceleration of the health industry. Technology provides an opportunity for business actors to convey product advantages to be disseminated widely through digital media. Apart from providing benefits in the easy dissemination of information, digital media can be a sales media for the health industry. Based on data from Tokopedia (e-commerce with the most users in Indonesia), the biggest sales are dominated by health products. Large amounts of data (big data) available in e-commerce can be extracted using the Web Scraping method. Big data can be processed to gain certain insights in achieving competitive advantage. CV Nutri Pro as a medium-sized business has limited data which causes the marketing mix that has been prepared beforehand to be incomplete. This condition causes out of sync, where there are demands that cannot be fulfilled. Based on the opportunity to utilize big data, CV Nutri Pro can determine a comprehensive renewable marketing mix. Each aspect of the marketing mix (4Ps) will be processed using big data analytics. The methods used include Pivot Data, K-Means, and Multidimensional Scaling (MDS). This research provides new insights for the company to renew marketing mix.