CRM and Data Mining 🤝❤️

In business, it is 5 to 10 times more expensive to find a new customer than to keep an existing one. CRM (Customer Relationship Management) uses data mining to ensure that current customers stay happy and loyal.

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1. RFM Analysis: Measuring Customer Value

To manage relationships, we first need to "score" our customers. The industry uses the RFM Framework:

• Recency: How long has it been since their last purchase? (Frequent visitors are more likely to buy again).
• Frequency: How often do they buy? (Regulars are the "bread and butter" of the business).
• Monetary: How much total money have they spent? (Big spenders get VIP treatment).

2. Customer Segmentation

Using the RFM score, we can group customers into clusters:

• Champions: High R, F, and M. (Reward them with exclusive previews).
• Loyal Customers: Frequent buyers but maybe low spenders. (Upsell them to better products).
• At-Risk: Used to be frequent but haven't visited in months. (Send a "We Miss You" 20% discount).
• Lost Customers: Low R, F, and M. (Ignore them to save marketing costs).

3. Churn Prediction & Lifetime Value (CLV)

Churn is when a customer stops doing business with you.

• Prediction: Data mining looks for "Signals" of churn. For example, in a mobile network, if a user starts checking the "Cancel Plan" page, the system flags them for a retention call.
• Customer Lifetime Value (CLV): Predicting the total profit a customer will bring over their entire life with the company.
  • Formula (Simple): Avg. Value of Sale x Number of Transactions x Retention Period.
  • Decision: If a customer's CLV is high, we spend more to keep them.

Summary

• Retention is cheaper and more profitable than Acquisition.
• RFM Analysis is the gold standard for measuring customer health.
• CLV helps decide how much to spend on keeping a customer.
• The goal is Personalized Communication ("Marketing to 1").

Quiz Time! 🎯

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