RFM Analysis

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.

 

Definitions

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

According to these metrics, we divide your customers into groups to understand their potential. 

For more information on how the RFM analysis is performed, click here.

 

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!

Use Cases

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

To do so, create segments and identify all customers belonging to an RFM status, e.g. all your Top Customers. 

You can also create the segment from our segment suggestions with just one click.

Now, you can create dynamic audiences to reach out to these customers in a targeted way:

Level up your customer retention strategy and start right at the acquisition stage: Influence the quality of customers early on and only attract high-potential customers. To do this, create campaigns and target like-minded users to your Top Customers. Therefore, choose a lookalike of 1-3% depending on your original audience size.

 

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)
• Discount
• Product Returns
• Shipping Revenue