E-commerce analytics involves collecting and analyzing online data to gain insights into customer behavior and business operations. The benefits include improved marketing strategies, better user experience, enhanced inventory management, improved forecasting, fraud reduction, and gaining a competitive advantage.

E-commerce analytics comes in different types:

1. Descriptive analytics: Summarizes past performance data.
2. Predictive analytics: Forecasts future trends and outcomes.
3. Prescriptive analytics: Recommends actions to achieve objectives.
4. Churn analysis: Identifies customers who have stopped engaging.
5. Sentiment analysis: Analyzes text data to determine customer sentiment.

E-commerce analytics encompasses various use cases and examples:

1. Basket Analysis: Extracts insights from customer purchase data to identify patterns and correlations between products for personalized recommendations.
2. Customer Segmentation: Segments customer base for targeted marketing campaigns and communication, leading to increased conversion rates.
3. Conversion Rate Optimization (CRO): Analyzes website traffic and user behavior to optimize conversion rates and enhance the online shopping experience.
4. Inventory Management: Optimizes inventory levels by analyzing historical sales data and forecasting future demand, minimizing excess inventory and associated costs.

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