RFM analysis is an effective tool for fitness app businesses looking to understand user 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 fitness app business.
Step 1: Collect Data The first step in building an RFM model is to collect data on users’ activity within the app. This can be done by tracking various activities such as workout frequency, time spent exercising, number of goals achieved etc.
Step 2: Score Users Based on Recency Once enough data has been collected, it’s time to score each user based on the recency of their most recent activity. To do so assign scores with ‘5’ being highest:
- Users who have exercised or used the app within last day/week/month – scored as ‘5’
- Users who have exercised or used the app within last quarter/half-year – scored as ‘3’
- Users who haven’t exercised or used our services 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 exercisers making regular logins & usage such as daily / weekly basis- scored “4” or above
- Those exercising occasionally 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 user 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 users could be sent exclusive offers or rewards, while low-value users may receive more general promotions to incentivize them to use the app more frequently.
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.