Makine öğrenmesi teknikleri kullanarak moda e-ticaret sektöründe müşteri segmentasyonu

In today's world where technology is developing very rapidly, internet usage is also increasing proportionally. This change has revealed that brands attach importance to the sector. The significance of e-commerce is to the advantage of brands because there have been decreases in some fixed expenses of companies. With the increase in online shopping, personal analyses of customers can also be made by customer relationship management (CRM). It is necessary to divide customers into segments for customer-oriented marketing. Customer segmentation is a widely used form of analysis. There is an increasing demand to develop a deep awareness of individual customer needs and desires. Segmentation, a commonly utilized method for achieving this understanding, has undergone continuous refinement in recent years. This study targets to present a detailed analysis of various segmentation approaches and their evolution. In this study, RFM (Recency, Frequency, Monetary) analysis was used for segmenting the customers. Customers were divided into segments by scoring them on the last shopping time, shopping frequency and total spending. Four customer groups were created with K-means and the values of each segment were analyzed. Churn rate analysis determined customers who did not shop for 90 days as lost. Churn estimation was performed with the LightGBM model using the machine learning technique. In addition, the Predictive CLV (customer lifetime value) model was developed using the 1 machine learning technique Ridge Regression. The accuracy rate was increased and low, medium and high CLV segments were created. As a result; RFM , K-means and CLV estimation were used to optimize customer relationships and increase revenues. E-commerce data of a private brand was analyzed using machine learning techniques. Nowadays, there is an increase in computing power and rapid developments in machine learning/artificial intelligence algorithms. This has recently enabled the application of more advanced techniques.