RFM analysis is a powerful tool for mobile 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 mobile app business.
Step 1: Collect Data The first step in building an RFM model is to collect data on your users’ activity within the app. This can be done by tracking various activities such as login frequency, time spent in-app, number of transactions made 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 activity. To do so assign scores with ‘5’ being highest:
- Users who have logged in or used the app within last day/week/month – scored as ‘5’
- Users who have logged in or used the app within last quarter/half-year – scored as ‘3’
- Users who haven’t logged-in 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 users making regular logins & usage such as weekly/monthly basis- scored “4” or above
- Those logging-in 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.