Know Your Relationship Status With Your Customers
Predictive algorithms that tells who loves you and who doesn’t.
Flexible RFM Model
- Segment your customers based on their Recency [R], Frequency [F] and the Monetary [M] Value of Interactions with your brand. You can either use R,F, and M parameters or any combination of RF, RM, and FM.
Stop guessing
- Accurately group your customers into “churn risk”, “champions”, “loyal”, “price sensitive”, “needs attention”, and more categories.
Measure continuously
- Identify how many customers have moved across segments - from “churn risk” to “promising” to “loyalist” to “champion” over a given time frame.
Win more happy customers
- Instantly add your “churn risk” or “needs attention” customers to a campaign and delight them with personalized communication.
Flexible RFM Model
- Segment your customers based on their Recency [R], Frequency [F] and the Monetary [M] Value of Interactions with your brand. You can either use R,F, and M parameters or any combination of RF, RM, and FM.
Stop guessing
- Accurately group your customers into “churn risk”, “champions”, “loyal”, “price sensitive”, “needs attention”, and more categories.
Measure continuously
- Identify how many customers have moved across segments - from “churn risk” to “promising” to “loyalist” to “champion” over a given time frame.
Win more happy customers
- Instantly add your “churn risk” or “needs attention” customers to a campaign and delight them with personalized communication.
What top brands say about MoEngage
20%active userbase
We decided to utilize MoEngage Funnel Analytics to understand user behavior trends on the app and the clicks on various discount banners. Using this data, we were able to add custom segments such as category/brand preference and more to run relevant engagement campaigns.
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Frequently asked questions
What is RFM analysis?
RFM analysis is a way of determining which customers are of the highest and lowest value to an organization. RFM stands for Recency, Frequency, and Monetary.
RFM analysis examines:
Recency - how recently a customer has purchased the product or visited the website/app,
Frequency - how frequently do they purchase or visit the website/app, and
Monetary - how much does the customer spend.
Why is it needed?
RFM method or RFM model segmentation is used to identify and segment customers into different groups based on their behavior. Thus, allowing marketers to target these customer segments with communications that are relevant and personalized for their particular behavior.
Who uses RFM analysis?
RFM segmentation and analysis is used by marketers and catalog retailers.
What are the use cases for RFM analysis?
Use Case 1: Measure the success of your engagement efforts. Visualize the flow of customers from low-value to high-value RFM segments.
Use Case 2: Prevent customer churn by identifying and auto-targeting high-risk customers. Set recurring campaigns that dynamically target churn-risk customers.
Use Case 3: Identify customers who are most likely to engage on specific channels.Group customers into different RFM customer segments and see which segments are likely to engage with your campaigns.
Use Case 4: Find champion buyers for specific products and categories. Send hyper-targeted deals and discounts that are relevant and personalized to each customer segment to guarantee success.
How customers can leverage MoEngage RFM in their campaigns?
Here’s what makes MoEngage RFM Analysis different:
⎯⎯ Get a complete picture with accurate database samples.
⎯⎯ Build custom RFM models. Run RF, FM, and RM analysis if one of the signals is not relevant.
⎯⎯ Get insights into actions within minutes by creating custom segments with the click of a button.
⎯⎯ Run highly targeted campaigns on the same screen.