Customer Segmentation using RFM Model

While planning for marketing spend, or formulating a new promotion, retail marketers need to be careful about how they segment and target customers. The idea is to identify customer’s needs or issues and use them as a solution via various campaigns and promotions. One of the techniques for targeted campaign is to do RFM Analysis (Recency-Frequency-Monetary)

Recency: Recency 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: How frequently these customers purchase from you. The higher the frequency, the higher is the chances of them responding to your offers.

Monetary: 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:  Divide each parameter into various ranges depending on your data and assign scores to each parameter. By combining 3 scores, we get the RFM scores.

Steps: http://gain-insights.com/solutions/retail-analytics/customer-segmentation-using-rfm-analysis/

Insights:

• Customers with overall high RFM scores are loyal customers and need to provide loyalty points and offers to continue their engagement.
• Customer who have high recency but low frequency score are ones who look out for offers. For these customers, the company needs to run different discount offers.
• 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 analysis is one of the most powerful technique to help you identify your best customers and create better targeted campaigns.