Impact of Big Data on Platform Pricing-Case Studies of Uber, Alibaba, Amazon, and Walmart

As the digital era progresses, big data has propelled real-time pricing strategies into global e-commerce and service platforms’ core competitiveness. This study employs case studies to demonstrate how platforms establish reasonable pricing policies by utilizing big data to dive deep into user activity data that service as critical inputs for price adjustments, such as click-through rates and browsing history, and integrating the influence of variable factors like inventory levels, supply chain costs, and environmental conditions. Mathematical modeling reveals that big data directly impacts real-time pricing through price sensitivity and regional variables like logistics and tariffs. Dynamic pricing models and analysis of marginal costs and revenue are also used to quantify contributing factors. The study shows that Uber considers factors during peak hours and dynamically adjusts prices via its DRS function. Alibaba and Amazon modify 62.3% of their products’ prices based on regional price sensitivity and price elasticity of demand. Walmart employs big data to quickly collect product prices in different regions and allows AI to autonomously decide the price of over 90% of products. This cross-industry research validates how big data shapes real-time pricing and concludes with methods of leveraging big data in real-time pricing strategies and relevant improvement proposals.