Implementasi Data Science dalam Ritel Online: Analisis Customer Retention dan Clustering Customer dengan Metode K-Means

Irma Permata Sari

Abstract


Data regarding huge sales and purchase transactions are stored electronically in company databases. Leave this data alone so it will not have any impact. Lately, many companies provide promo prices to customers to attract customers. However, the decision is suitable to be taken, regardless of sales growth, to not cause more significant losses. The sales show that has been recorded each year in the sales transaction database. This research focuses on implementing data science at a retail company to analyze sales performance using the cohort analytics method to calculate customer retention and perform clustering customer using the K-Means model. As the results, we can conclude that the company has sales performcean about 37.4%, seen from customer retention and the monthly sales volume within one year. There are three groupings produced, namely ID 0, 1, and 2. Customers with Cluster Label 2 are customers with the highest number of transactions compared to other groups.

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References


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DOI: http://dx.doi.org/10.30645/j-sakti.v5i1.333

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