RFM analysis is a powerful tool for Fintech businesses looking to understand customer behavior and segment their audience into high-value groups. In this article, we will guide you through the process of building an RFM model for your fintech business.
Step 1: Collect Data The first step in building an RFM model is to collect data on your customers’ transaction history. This can be done by tracking transactions records from different products like credit cards, loans, investments etc.,
Step 2: Score Users Based on Recency Once you’ve collected enough data, it’s time to score each user based on the recency of their most recent transaction. To do so assign scores with ‘5’ being highest:
- Customers who have made a transaction within last day/week/month – scored as ‘5’
- Customers who have made a transaction within last quarter/half-year – scored as ‘3’
- Customers who haven’t transacted in more than half year ago – scored as ‘1’
Step 3: Score Users Based on Frequency Next up is assigning frequency scores which would help us gauge how often users are using our services . As before , we assign scores with “5” being highest :
- Frequent users making regular transactions such as weekly/monthly basis- scored “4” or above
- Those making occasional transactions could be placed lower
Step 4: Score User Based On Monetary Value Lastly , monetary value scoring helps identify big spenders among our customers & hence should get higher weighting while those spending less should get lower weightage .For instance:
- High-spending customers could be given higher weights i.e., score of “4” or above.
- Low spenders would get lower weighting
By combining these three sets of scores together we create composite / summary score which helps identify high-value segments .
Step 5: Segment Creation Using these composite RFM Scores generated in Step #4 , we then divide our customer base into different segments like “high-value,” “medium-value” and “low-value” segments. This will allow us to create targeted campaigns for each group.
Step 6: Targeted Messaging Based on the high/medium/low value customer segmentation created in Step #5, businesses can tailor messaging and promotions towards specific segments . For instance, High-Value customers could be sent exclusive offers or rewards, while low-value customers may receive more general promotions to incentivize them to make a transaction .
Step 7: Continuous Analysis Finally, it’s important to continuously track and analyze RFM scores of users & re-segment them accordingly based on changes in behavior over time. By doing so we ensure that our marketing strategies remain relevant and effective.
In conclusion, by implementing an RFM model businesses can better understand their customers’ behaviour leading towards increased personalization ultimately resulting in higher engagement rates while simultaneously improving brand loyalty ultimately leading towards increased revenue streams.