RFM Analysis
RFM analysis is a data-driven marketing technique used to segment customers based on their past behavior and interactions with a business. The acronym “RFM” stands for Recency, Frequency, and Monetary value, which are the three key metrics used to evaluate customer behavior:
Recency: How recently did the customer make a purchase? Customers who have made recent purchases are generally more engaged and valuable to the business than those who haven’t interacted for a long time.
Frequency: How often does the customer make purchases? Customers with a higher frequency of purchases are likely to be more loyal and active.
Monetary Value: How much money does the customer spend? Customers who have spent more money are considered more valuable to the business.
The RFM analysis process typically involves the following steps:
Data Collection: Gather transactional data that includes details such as purchase dates, order values, and customer IDs.
RFM Calculation: Calculate the recency, frequency, and monetary value for each customer based on the data collected.
Customer Segmentation: Divide customers into segments based on their RFM scores. For example, customers who recently made high-value purchases and frequently interacted with the business will be in a high-value segment.
Customer Profiling: Understand the characteristics and behaviors of each segment to tailor marketing strategies and campaigns for each group.Best customers: These customers have the highest RFM scores. They are your most valuable customers and should be the focus of your marketing efforts.
Marketing Strategy: Develop targeted marketing strategies for each customer segment to improve customer engagement, retention, and overall revenue.
Overall, RFM analysis is a powerful tool that can help you improve your marketing and increase your profits. By understanding your customers purchase behavior, you can target your marketing campaigns more effectively and reach the customers who are most likely to buy from you.
Source: RFManalysis
Project: Github
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