E-commerce has witnessed exponential growth in recent years, becoming a dominant force in retail. However, with this growth comes intense competition, making customer retention a critical focus for e-commerce businesses. Retaining customers is not only cost-effective but also contributes significantly to long-term business success and profitability. The concept of customer retention has been integral to business strategies across various industries for centuries. The challenge lies in developing effective customer retention strategies tailored to the unique dynamics of e-commerce. This requires a deep understanding of customer behavior, preferences, and purchasing intentions. Analyzing purchasing intention data provides valuable insights that can inform targeted strategies to keep customers engaged and loyal. Historically, customer retention efforts in e-commerce relied on email marketing, loyalty programs, and periodic discounts. While these methods remain effective, they often lack the personalization and data-driven insights needed to truly understand and cater to individual customer preferences. Therefore, the proposed approach leverages advanced data analytics techniques to gain insights from purchasing intention data. By analyzing customer behavior, browsing patterns, and interactions within the e-commerce platform, businesses can identify key indicators of purchasing intent. These insights can be used to tailor marketing efforts, provide personalized recommendations, and offer incentives that resonate with individual customers. Furthermore, proposed algorithms are applied to predict future purchasing intentions based on historical data, enabling businesses to proactively engage with customers before they make a purchase decision. This data-driven approach not only enhances customer satisfaction but also optimizes marketing efforts and resource allocation.

Machin Learning, E-commerce growth, Customer retention, Data analytics, Personalized marketing.