Retailing Applications 🛒📈

In the retail world, data mining is the difference between a thriving store and a failed one. Retailers like Walmart and Amazon use data to know what you want to buy before you even know it yourself.

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1. Market Basket Analysis (MBA)

This is the most famous retail application. It uses Association Rules to find which items customers buy together.

The "Beer and Diapers" Story 👶🍺

According to industry legend, a large retailer (Walmart) used data mining to discover a strange pattern: On Friday evenings, men who bought Diapers also tended to buy Beer.

• The Logic: New fathers were sent to the store to get diapers, so they bought beer to enjoy while staying at home with the baby.
• The Action: The store placed beer next to the diapers.
• The Result: Sales of both items increased significantly.

2. Demand Forecasting & Inventory

BI tells a retailer exactly how much of a product should be on the shelf at any given time.

• Safety Stock Optimization: Using algorithms to calculate the minimum stock needed to avoid "Stock-outs" (empty shelves).
• Seasonal Analysis: Predicting the massive spike in "Sweets and Crackers" before Diwali or "Umbrellas" before June.
• Perishable Management: In grocery stores, BI ensures that milk and bread are sold before they expire, reducing waste.

3. Dynamic Pricing & Promotions

• Dynamic Pricing: Changing prices based on supply and demand (similar to Uber surge pricing).
• Personalized Vouchers: If the system knows you have a dog (from your past purchases), it sends you a 20% discount on Dog Food to ensure you don't go to a competitor.
• Store Layout: Placing "High Margin" items (like expensive perfumes) next to "High Volume" items (like milk) to encourage impulse buys.

Summary

• Market Basket Analysis increases sales through better product placement.
• Demand Forecasting reduces waste and storage costs.
• Personalization (CRM) builds customer loyalty.
• The goal is to move from Passive Selling to Predictive Selling.

Quiz Time! 🎯

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