RFM Analysis is an excellent method for quantifying customer transaction history and hence identifying your customers' potential to make the right decisions for marketing investments.
For sure, we all want to boost our customer retention, loyalty, and lifetime value. But to do this it is necessary to have a customer base with the highest possible amount of Top Customers.
So let's check how well your customer base is distributed per RFM.
Before you dive into data analysis, let's ensure that we are on the same page. The idea is to segment customers based on the three metrics: recency, frequency, and monetary value.
- Recency (R): Days since last purchase
- Frequency (F): Total number of purchases
- Monetary Value (M): Total money after returns the customer spent
Monetary Value is based on Net Revenue calculations, which by default extracts VAT, Shipping Revenue, Discounts, and Returns from Total Revenue.
Net Revenue definition can be changed in Company Settings. Please, keep in mind that changing Revenue definitions will trigger a re-import of all data from your store that could take up to 24 hours.
According to these metrics, we divide your customers into groups to understand their potential.
How it Works
To conduct RFM analysis, we score your customers by ranking them based on each RFM attribute separately. Ultimately, we will get the percentiles of each of these numbers and then the quartiles. The quartiles will give us a score of 1 through 4, which we will combine to get an RFM score.
After ranking our customers based on the recency, frequency, and monetary value, we have individual R, F, and M scores; by combining their individual R, F, and M scores, we get the aggregated RFM score per customer.
Based on this, the following customer segments can be defined:
|Top Customers||RFM score of 111||Most valuable customers: made the highest amount of purchases, with the least days since last order and the highest monetary value.|
|Loyal Customers||RFM score of X1X||Customers that made a great number of purchases. This segment does not indicate performance regarding days since the last order or monetary value.|
|High Potentials||RFM score of XX1||Customers that made had a great AOV along their lifecycle. This segment does not indicate performance regarding days since the last order or number of orders in their lifecycle.|
|Small Buyers||RFM score of X13 or X14||Customers that place few orders will have a small monetary value. This segment does not indicate performance regarding days since the last order.|
|Dormant Customers||RFM score of 44X||Customers that placed few orders a long time ago. This segment does not indicate performance regarding the monetary value of those orders.|
|Worst Customers||RFM score of 444||Customers that placed few orders, a long time ago, with small monetary value.|
|Other||Customers who do not fall under the specifications above will be categorized as Other. All customers are assigned an RFM score; however, not all scores have a predefined RFM status.
You can create custom segments based on the RFM scores using our segment builder!
Once every customer has an RFM Score assigned, it is time to see the percentage of your database that belongs to each group. In this report, you will be able to see how many customers (both in absolute numbers and percentages) are within each score combination - that will give a look over your customer base's health!
Instead of analyzing your entire customer base as a whole, RFM analysis is a powerful tool to segment them into homogeneous groups, understand the traits of each group, and engage them with relevant campaigns. By knowing your customer distribution per RFM, you can identify and address potentials and risks in your customer structure:
- Top Customers: Reward them with unexpected benefits
- Loyal Customers: Provide the highest level of customer service to transform them into Top Customers
- High Potentials: Encourage them to place the next order, e.g. by using vouchers or goodies
- Small Buyers: Offer other relevant products and special discounts
- Dormant Customers: Reactivate them by an explicit reconnection
- Worst Customers: Revive the interest with a reach-out campaign, otherwise ignore them
How to use RFM Analysis to segment consumers?
- Click on Customer Segments > New Segments
- While selecting the different rules for your new segment, choose one or more related to RFM Score.
- You can push the segment created into Facebook, create a lookalike audience and use it as the target for your new marketing campaign!
Let's see how to work with RFM Analysis with an example.
You are thinking about sending a Rewards Campaign, but in the past targeting only consumers with high Order Count has brought you some bargain hunters that are simply not worthy of your investment.
If you want to acquire high-quality customers and focus your marketing efforts on your valuable customers, you should make sure that your audience meets all the criteria and stick to the Top Customers segment:
- With the RFM Analysis up and running, you can now create a segment that includes only the customers that got an RFM Score of 111 (your Top Customers).
- Push the segments into a Facebook Audience (learn more about our integration with Facebook Ads here)
- Create a 'Lookalike Audience' and use it for targeting your acquisition campaigns
If you don't have enough data to create the default Top Customers segment, don't worry! You can segment based purely on Average Order Value (AOV) and still create successful Lookalike Audiences.
What you Need
For this report to work properly, the following data must be imported:
- Order ID
- Order Date
- Stock Keeping Unit (SKU)
- Items Sold
- Item Price
- Customer ID
- Value Added Tax (VAT)
- Product Returns
- Shipping Revenue