1. Sentiment Analysis and Feedback
1.1. Customer Feedback data
1.1.1. Review Text
1.1.2. Rating
1.1.3. Sentiment Score
1.1.4. Date and Time
1.1.5. Source
2. Fraud Detection and Prevention
2.1. Transaction Data
2.1.1. Timestamp
2.1.2. Amount
2.1.3. Payment Method
2.1.4. Transaction Location
2.2. Purchasing Data
2.2.1. Product Description
2.2.2. Product Category
2.2.3. Product Price
2.2.4. Quantity Purchased
3. Customer Segmentation and Personalization
3.1. Customer Demographics
3.1.1. Gender
3.1.2. Age
3.1.3. Income Level
3.1.4. Location
3.1.5. Occupation
3.2. Customer Behavior
3.2.1. Customer satisfaction Score
3.2.2. Average order Value
3.2.3. Frequency of Purchases
3.2.4. Recency of Purchases
3.3. Customer Preferences
3.3.1. Loyalty program membership
3.3.2. Product preference
3.3.3. Communication Channels
3.3.4. Promotional Preferences
4. Demand Forecasting and Inventory Management
4.1. Sales Data
4.1.1. Date and Time
4.1.2. Quantity Sold
4.1.3. Price
4.2. Product Data
4.2.1. Product Name
4.2.2. Category
4.2.3. Cost Price
4.2.4. Selling Price
4.2.5. Attributes
4.2.6. Availability
4.3. Inventory data
4.3.1. Stock Levels
4.3.2. Reorder Point
4.3.3. Lead Time
4.4. External data
4.4.1. Weather
4.4.2. Inflation
4.4.3. Market trend
5. Dynamic and Competitive Pricing Strategies
5.1. Competitor Data
5.1.1. Competitor price
5.1.2. Competitor promotions
5.1.3. Market Share
5.1.4. Product Availability