Which three components should an e-commerce company's story include for comprehensive analytics on sales performance?

For comprehensive analytics on sales performance in an e-commerce context, including predictive forecasting for future sales is pivotal. This component allows businesses to utilize historical data to make informed projections about future performance, taking into account trends, seasonality, and other influencing factors. By understanding potential future sales, companies can strategically plan inventory, marketing efforts, and resource allocation, thus gaining a competitive edge in their market.

While correlation analysis for customer behavior, a pie chart to display product category shares, and anomaly detection for unexpected sales changes are valuable components in their own right, they serve different purposes. Correlation analysis provides insights into relationships between various customer behaviors and sales, which informs strategy but does not directly project future performance. A pie chart is an effective visualization tool for presenting categorical data, such as product shares, but it does not offer predictive insights or a comprehensive analysis framework. Anomaly detection is crucial for identifying unusual patterns that could indicate issues or opportunities, but it similarly does not focus specifically on forecasting future trends. Therefore, the inclusion of predictive forecasting as a core component aligns best with the need to understand and anticipate sales performance comprehensively.