While planning for marketing spend, or formulating a new promotion, retail marketers need to be careful about how they segment and target customers. It would be a waste of marketing spend if, for example, an ad campaign is targeted to all the thousands of your customers. Such an untargeted marketing promotion is unlikely to have a high conversion rate and may even hurt your brand value.
Retailers now use sophisticated techniques to segment their customers and target their marketing efforts to these segments. RFM analysis is one such popular customer segmentation technique that can help retailers maximize the return on their marketing investments.
Under RFM analysis, each customer is scored based on three factors, namely Recency, Frequency, and Monetary value. RFM analysis can help companies identify customers that are most likely to respond to a new offer. Let’s look at each of these factors in details:
Recency: Recency is the most important predictor of who is more likely to respond to an offer. Customers who have purchased recently from you are more likely to purchase again from you compared to those who did not purchase recently.
Frequency: The second most important factor is how frequently these customers purchase from you. The higher the frequency, the higher is the chances of them responding to your offers.
Monetary: The third factor is the amount of money these customers have spent on purchases. Customers who have spent higher are more likely to purchase based on the offer compared to those who have spent less.
How it Works?
To perform RFM analysis, each customer is assigned a score for recency, frequency, and monetary value, and then a final RFM score is calculated.
Recency score is calculated based on the date of their most recent purchase. The scores are generally categorized based on the values. For example, a company may follow a category system of 1 to 5, score of 5 being the highest. In this case, customers who purchased within the last one month have a recency score of five, customers who purchased within the last 1-3 months have a score of four and so on.
Similarly, frequency score is calculated based on the number of times the customers purchased. Customers with higher frequency receive a higher score.
Finally, customers are assigned a score based on the amount they spent on their purchases. For calculating this score, you may consider the actual amount spent or the average spent per visit.
By combining these three scores, a final RFM score is calculated. The customers with the highest RFM score are considered to be the ones that are most likely to respond to their offers. In our example, the customers score will range from 111 to 555 (add up all these scores). Customers with a score of 555 are the best customers.
Customer Segmentation
RFM analysis can help retailers segment the customers and design offers and promotions based on their profile. Below are a few examples:
Customers with an overall high RFM score represent the best customers.
Customers who have a high overall RFM score but a frequency score of 1 are new customers. The company can provide special offers for these customers in order to increase their visits.
Customers who have a high frequency score but a low recency score are those customers that used to visit quite often but have not been visiting recently. For these customers, the company needs to offer promotions to bring them back to the store, or run surveys to find out why they abandoned the store.
RFM scores can be analysed together with the results of the campaigns to eliminate non-responsive customers and further improve the campaigns.
RFM score can be analysed together with the products they purchase to design highly targeted offers for each customer segment.
RFM score can be analysed together with other information about the customers such as their income levels, gender, whether they own a vehicle or nor, etc. to segment the customers.
To conclude, RFM analysis is a powerful technique to help you identify your best customers and create better targeted campaigns. However, RFM itself is not enough and retailers should focus on creating more detailed customer profiles including their demographics, behavioural and purchase patterns and use this information in conjunction with RFM to provide better value to customers.
