RFM Analysis

Not all of your customers are equally valuable. It is essential to understand who your high-value customers are as well as your low-value ones. In general, there are three main drivers of customer quality: Recency, which describes the time since the last order, Frequency, which looks at the number of orders, and Monetary Value, which evaluates the total net revenue. We look at these three factors within the RFM Analysis.

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 (R): Days since the last purchase
• Frequency (F): Total number of orders
• Monetary Value (M): Total money after returns the customer spent

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

How it Works

To perform an RFM analysis, we score your customers by ranking them on each RFM attribute separately. Ultimately, we will get the percentiles of each of these numbers and then the quartiles. The quartiles give us a score from 1 to 4, which we combine to get an RFM score. The lower the score, the better a customer is ranked.

There are up to 64 different combinations of RFM scores. The report shows how many customers are assigned to each score combination - giving you a snapshot of the health of your customer base! To help you get started, we provide the following predefined RFM statuses:

• Top Customers (RFM score: 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: 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: 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: 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: 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: 444)
  Customers that placed few orders, a long time ago, with small monetary value.

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!

To easily see a visual of the distribution of RFM categories among your customer base, you can simply hover over the RFM categories. Using Loyal Customers as an example- this would look like so:

Additionally- when you select Recency, Frequency, or Monetary, we show the thresholds being considered for each RFM category:

Use Cases

Rather than analyzing your entire customer base as a whole, RFM analysis is a powerful tool for segmenting customers into homogeneous groups, understanding the characteristics of each group, and targeting them with relevant campaigns. This approach increases response rates, retention, satisfaction and LTV.

Each RFM metric effectively predicts future customer behavior and revenue growth. Recent purchasers are more likely to buy again soon, frequent shoppers tend to stay engaged, and high spenders often continue to contribute with higher revenues.

With RFM Analysis, you can tailor messages to customer relationships. For example, suggesting high-value items to frequent high-spenders may yield better results. Conversely, encouraging loyalty or incentivizing referrals can strengthen relationships with customers who make frequent but smaller purchases. Here are some ideas on how to address opportunities 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. 

Alternatively- you can also create the segment from our segment suggestions with just one click.

Read our full blog post about RFM analysis for Shopify stores here.

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