RFM (Recency, Frequency, and Monetary) Model provides auto-segmentation and bucket users into categories such as Loyal, Promising, At Risk, etc. based on their behavior. These auto segments can be used in multiple different ways such as user analysis, churn analysis, and campaign effectiveness. RFM can also be used predictive segmentation, customers who are more likely to respond to promotions and also for future personalization.
RFM analysis is a widely used marketing model for behavior-based customer segmentation. This was primarily used in the retail industry and making its way to digital marketing. It groups customers based on their transaction history – how recently, how often and how much did they buy.
RFM Analysis can be used to answer questions like -
- Who are your loyal customers?
- Which are the customers who are most likely to churn?
- Which customers are purchasing the most on your platform?
- Which are the customers who can be turned into the best customers with little effort?
- Which customers are most likely to engage with your campaigns?
Let's deep dive and understand how it works -
What is RFM Analysis
RFM analysis is a customer behavior segmentation method that uses customers' past interactions such as a visit to the platform or purchase of an item and based on these interactions divides customers into different RFM groups.
The factors for RFM Analysis
The primary factors are R, F, and M for this model, which are explained below-
R is RECENCY - Time since the last visit to the app/site or time since last purchase.
F is FREQUENCY - Total number time a user has visited the app/site or total number of purchases
M is MONETARY - Total money spent by a user or total time spent watching content
How RFM is calculated
Value for Recency, Frequency, and Monetary
Value for Recency, Frequency, and Monetary is the exact value for a specific user, for example, the recency value is 3 days ago, the frequency value is 5 times, and the purchase value is $1523.
Score for Recency, Frequency, and Monetary
MoEngage systems provide Recency, Frequency and Monetary score to each user based on the value of recency, frequency, and monetary. The values are ranked from highest to lowest and a score has been provided based on the rank. A combination of all these scores is the final RFM score for a user.
For example, consider the table given below. For user ABC, DEF and GHI RFM is calculated. Recency value is ranked from highest to lowest and respective scores have been provided. The same procedure has been repeated with Frequency and Monetary scores. After getting R score, F score and M Score, the RFM score is calculated for a user.
|User ID||R Value||R Score||F Value||F Score||M Value||M Score||RFM Score|
|ABC||1 day ago||5||10 times||4||$1000||4||4.33|
|DEF||5 day ago||3||4 times||3||$560||2||2.66|
|GHI||9 day ago||1||1 time||1||$100||1||1.00|
Segment Buckets for RFM Score
Users who are showing similar behavior on R, F, M and RFM scores are grouped into the same RFM buckets or segments. These segments are named with respect to user behavior. The list of segments and the respective description is provided below-
- Champions - User visited most recently, visited most often and spend the highest
- Loyal Customers - User visited recently, visited often and spent a great amount
- Potential Loyalist - Recent user, spent a good amount
- Recent Users - User visited most recently, but not often, has not spent much
- Promising - Average recency, frequency, and monetary scores
- Needs Attention - User has spent good amount but long ago (not visited recently)
- About To Sleep - Below average recency, frequency, and monetary values
- Price sensitive - User visited most recently, and also often, but has not spent much
- Can’t Lose Them - User has spent great amount and visited often but long ago (not visited recently)
- Hibernating - User's last visit was long back, visits are not ofter and has not spent much
- Lost - Lowest recency, frequency, and monetary scores.
Engagement Strategies for RFM Segments
The RFM segment so predicts user behavior and accordingly, marketers can take actions to make the best of these user segments. Here are some basic strategies to be used for different RFM Segments -
- Champions - Reward these users. They promote your products, they can be early adopters for your new launches.
- Loyal Customers - These user are responsive to your promotions, suggest them higher-value products. Also, ask them for reviews.
- Potential Loyalist - Engage them with long term offers like loyalty programs or membership rewards. Suggest other categories of products to them.
- Recent Users - For new users, make their onboarding experience smooth, provide assistance when needed.
- Promising - Make them loyal by creating brand awareness and giving free trials.
- Needs Attention - Need to bring back these customers, provide limited period offers, recommend products using purchase history.
- About To Sleep - User will be lost if not reactivated. Recommend them popular products provide discounts for memberships.
- Price sensitive - Users looking for the best deal, recommend them the highest rated products and send discount communications.
- Can’t Lose Them - Listen to their feedback, suggest them newer products and make them stick to your platform.
- Hibernating - Recommendations products for other categories and provide personalized offers.
- Lost - Make your presence known by different campaigns.