RFM metrics
RFM stands for Recency, Frequency and Monetary value, three important customer characteristics. Therefore, RFM metrics are used for segmentation and analysis of customer behavior to gain a better understanding of the customer. Recency, which refers to the last time a customer made a purchase, is a sign of retention. Frequency refers to how often the customer initiates transactions, which provides insight into the level of engagement. The monetary value represents the total spend a customer makes on your products and services. When using RFM metrics, customers are scored in each of these three categories and the average of the total scores is calculated.
RFM metrics are very informative. Companies wondering who their best customers are or which customers are most at risk of churn can find answers using RFM analysis. After the average scores are calculated, customers can be segmented in different ways that represent their value to the company and the type of services they need. For example, customers with a low score are likely to churn. So re-engage them with phone calls or emails. Similarly, high-scoring customers represent brand loyalty and should be rewarded with personalized discounts and offers. Also, each RFM component can be considered separately while segmenting customers instead of using an average score.
Listings in RFM metrics
RFM stands for Recency, Frequency and Monetary value, three important customer characteristics. Therefore, RFM metrics are used for segmentation and analysis of customer behavior to gain a better understanding of the customer. Recency, which refers to the last time a customer made a purchase, is a sign of retention. Frequency refers to how often the customer initiates transactions, which provides insight into the level of engagement. The monetary value represents the total spend a customer makes on your products and services. When using RFM metrics, customers are scored in each of these three categories and the average of the total scores is calculated. RFM metrics are very informative. Companies wondering who their best customers are or which customers are most at risk of churn can find answers using RFM analysis. After the average scores are calculated, customers can be segmented in different ways that represent their value to the company and the type of services they need. For example, customers with a low score are likely to churn. So re-engage them with phone calls or emails. Similarly, high-scoring customers represent brand loyalty and should be rewarded with personalized discounts and offers. Also, each RFM component can be considered separately while segmenting customers instead of using an average score.
