Shopify RFM Matrix
RFM Matrix: Treat The Best Customers Like Royalty, Win Defectors Back.
What Is Our Loyalty Report (RFM Matrix)?
Our Loyalty Report is an advanced form of an RFM report. It uses all of your brand data to carry out detailed analysis building on top of these three core factors:
- Recency (R): Measures time since last purchase; recent buyers are more open to new offers.
- Frequency (F): Counts purchases in a period; more often means more loyalty.
- Monetary (M) Value: Totals spending over time; higher spenders are more valuable.
The Loyalty report combines them with our own scoring methods. We are able to automatically segment customers enabling you to identify your most loyal and frequent buyers, those who might need incentives to make them your best, and others who may be at risk of drifting away.
What might’ve taken long hours to compile is now provided instantaneously. Our targeted approach allows for more personalized and efficient marketing efforts, aiming to enhance customer loyalty, increase retention, and drive sales
Why You Should Be Using Our Loyalty Report.
Leveraging RFM (Recency, Frequency, Monetary) Analysis, with our advanced Loyalty Reporting. Not only identifies your most valuable customers but also pinpoints those at risk of churn that you need to re-connect with immediately and everyone else in-between.
You can use our loyalty matrix reports to identify…
- Which customers should you email to decrease churn or defection?
- What Customers can I upsell higher priced goods too?
- Who will engage with you the most?
- Who & How should i advertise limited offers to only the groups that need it most.
- How can I communicate better with my loyal customers?
Identify at-risk customers & Win Back Those Lost
Use Our App To Optimise Your Touch Points…
- Gain back dormant/lost customers
- Stop customers from going elsewhere
- Turn good customers into your best customers
- Keep your best customers your best.
With an automatically generated list of customers in each customer segment, learn the behaviours of each segment to perfect your retention. Maximise your campaigns to optimize ROI.
Deep Dive into the RFM Matrix
The e-commerce industry is now worth $6.33 trillion (2024).
Over the next few years, the industry is expected to grow at 8.8% each year. At this rate, the growth could increase. You have to grow with the industry to keep up with it. More growth means more customers. More customers means understanding your customers is now even more critical. Failure to do so could mean the failure of your store.
But if you’re able to understand the RFM matrix, you’ll be fine. Let’s get started on this deep dive.
First, customers are organized into segments based on their Recency and Frequency. The matrix below shows the result.
There are 7 groups that customers are sorted into:
- Best Customers: Customers who have been grouped into this category have purchased from the store within the last 30 days and have made 4 or more orders
- Loyal Customers: Customers who are grouped here have last ordered from the store 1-6 months ago and made 4 or more orders like the Best Customers
- Promising Customers: Customers who are here have only made 2-3 orders so far and have been at your store anytime within the last 6 months
- Recent Customers: Customers who have recently ordered from your store within the last 30 days (like the Best Customers) but have only made 1 order.
- Defecting Customers: Customers who ordered 1-6 months ago but only made 1 order.
- At-Risk Customers: Customers at your store 6-12 months ago.
- Dormant Customers: Customers who were at your store over 12 months ago.
These are the most important segments to analyze, as they generate the largest ROI when you invest in learning about them.
To make sure you understand, let’s do 2 examples:
Customer A makes 2 orders; last did so 4 months ago. Looking at the matrix above, having 2-3 orders and last ordering 1-6 months ago puts them in the Promising Customers category.
Customer B makes 6 purchases and last ordered 2 weeks ago. Customers who have made 4 or more orders and ordered within the last 30 days will be placed in the Best Customers Category.
Once these customers are sorted into groups, we score their monetary value based on this group’s Average Order Value, LTV, Total Sales, and unrealized sales.
How to Use the RFM Matrix to Benefit Your Business
Since you now know how the RFM Analysis works, let’s dive deeper into aspects of the business that can benefit you.
When you get the segments from your RFM analysis, you can answer many questions to help your business grow. 90% of your questions that require an analysis will be answered with an analytics app like By the Numbers
Here are 5 aspects of your business where the RFM Analysis can help uncover insights and drive decisions.
- Targeted Marketing – Improve how you communicate with customers
- How do my Loyal Customers respond to my Google Ad campaigns?
- Which marketing channel should we allocate more of our budget to target our Best Customers?
- Product Recommendation – Tailor products to your customers
- What do the purchasing patterns of our Best Customers look like?
- What products are frequently bought together amongst those that frequently visit?
- How do we pair the right products with the right customer to create a better user experience?
- Inventory Management – Have the right products ready to go
- Based on recent sales, which items should be prioritized for restocking due to high demand?
- Which products were highly associated with this segment from those who last visited the store a while ago?
- Churn Prediction – decrease your churn and optimize the customer experience
- Which customers indicate a high likelihood of churning, and what does their customer journey look like?
- Is there a correlation between products and At-Risk Customers? How about for Marketing Channels?
- Customer Retention – Keep your Best and Loyal customers happy and grow these segments
- Which initiatives have been associated with our Loyal Customers?
- Which behaviours are associated with our Best Customers (i.e. once the customer has made 3 separate orders, are they likely to visit the store more frequently)?
The Issue With Using Shopify
Let’s walk you through how to do this whole process on your own manually:
- Collect the data of all your customers from your Shopify store
- Export this data onto an Excel or Google Sheets document
- Organise your data set to group customers first based on their recency
- Then, organize your data set to further group your customers based on their frequency
- After creating the groups, analyze the monetary value of each customer in each group
- Calculate the LTV of each customer
- After this, for each group, calculate their average LTV to see their monetary value.
This process not only be too much work but takes up time. As your customer base grows, so will the time it takes to analyze all customers properly. There’s also room for human error using this method.
You will also have to update your customer base manually using this method.
This method takes too much time and money from you (conducting a data analysis could cost up to $100,000 a year).
Shopify’s reports aren’t detailed. This is why an app like By the Numbers is needed. Having automated analyses that offers real-time updates is beneficial to your store. You can then use these segments created for further analysis.
It also helps that By the Numbers offers a data analytics service at a fraction of the cost of other options.
Here’s an example of how you can conduct further analysis on our app:
If we were to answer the question, “What’s the Time Between Orders for our Best Customers?” we first access the report Time Between Orders and change the segment to “Best Customers” (created from the RFM Matrix) All Segments from the analysis are premade for you and receive real-time updates.
As you can see, the time between orders drastically decreases from the 5th to the 6th order.
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