Let's start by looking at the shortcomings of RFM analysis:
Limited scope. RFM analysis focuses on ideal customers, which may not be representative of real businesses. In reality, most people shop less frequently and spend less money.
Lack of ability to respond to data in real time. In today's world, companies are evolving rapidly. Self-learning algorithms and automated segment analysis are becoming increasingly valuable and effective customer management tools.
B2C focus: RFM is not well suited for B2B environments where customer purchases are less regular.
Pros and Cons of the NPV Model
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The RFM method does not provide chinese singapore b2c cell phone number data a complete picture of consumer behavior. But we should not write off classical methods of analysis. Let's consider the advantages of RFM:
Identification of valuable customers. With RFM analysis, a brand can identify its most valuable customers. For example, find the most active segments that have the greatest impact on profit (LTV or lifetime value of the customer).
Increase CLV and profitability. Customer lifetime value is one of the key indicators. Its improvement has a positive effect on business. RFM analysis of the customer base allows the company to focus on the most valuable buyers and adjust its communication strategies, including advertising, to them.
Reduced Churn. RFM analysis helps reduce customer churn. The company receives valuable information about why each segment stays with the brand.
Retaining the target audience: By collecting customer data and creating personalized experiences, a brand can better retain customers based on their individual preferences (for example, clothing style or food tastes).
When developing a strategy to attract more valuable customers, it is important to set specific goals. For example, promoting a product range, offering recommendations for additional products, or cross-selling.