E-Commerce Management And Ai Based Dynamic Pricing Revenue Optimization Strategies

Abstract

Online pricing is direct and might be the primary considers a purchase. Despite the fact that pricing unpredictability is frequent and used to support sales and productivity, online firms benefit from it. Business success depends on pricing, especially in membership-based models. Albeit successful before, quickly changing market dynamics are making static pricing structures unsuitable for the present businesses and causing significant issues. Computer based intelligence has been used to improve pricing systems in response to these difficulties. Improving e-commerce pricing techniques emphasizes picking the right price over the lowest. The review focuses on inventory-led e-commerce businesses, however online marketplaces without inventory can benefit from the concept. The review uses factual and machine learning methods to anticipate item purchases utilizing adaptive or dynamic pricing. This system is based on several information sources that gather visit attributes, guest details, purchase history, online information, and contextual bits of knowledge. Interestingly, the examination forecasts customer segment purchases higher than individual consumers. Further extensions will be developed after the current research results are released to personalize adaptive pricing and purchase prediction. The review's answer landscape covers machine learning, enormous information, and web mining.