Data-Driven Decision-Making Based on Revenue

Continuous monitoring of sales and key performance indicators are essential for big data e-commerce businesses. It allows tracking performance, identifying trends, and making informed decisions to increase revenue and maximize profitability. Analytical solutions for revenue metrics are presented in this research paper. The distribution of the average revenue per user (ARPU) is defined by leveraging the beta gamma ratio distribution. The cumulative distribution function and the probability density function are analytically derived. The probability that the ARPU for the user group interacting with content B is greater than the ARPU for the user group interacting with content A is explicitly expressed. Additionally, the expected value of the loss function is obtained using a closed-form solution based on the Meijer G-function. A statistical methodology for data-driven decision-making is described, leveraging novel analytical results. Precise results are crucial for accurate predictions and data-driven decision-making based on revenue. Elegant closed-form solutions demonstrate conciseness and simplicity.