RFM Analysis is a powerful tool that automatically provides you with customer groups based on their loyalty levels. This gives you instant access to who your Brand Champions are, who your At Risk customers are, and everyone in between.

RFM in Peel allows you to dig into each of those automated customer segments and learn more about those customers, and even create an Audience for those groups in a single click.

It’s all about going deeper into your customer groups and gaining clarity on what messaging, product offers and promotions will resonate with them at their given stage in the buyer’s journey.

You can get RFM analysis for your Shopify store right now and try out the power that comes with knowing what/when your customers need to hear from you.

What is RFM Analysis?

“RFM” stands for “Recency, Frequency & Monetary.” RFM Analysis is a measurement technique that uses existing customer behavior data, based on recency, frequency and monetary value of purchases, to predict how customers will act in the future as well as to group them into behavioral segments.

RFM can help you answer these questions and more:

• Who can you upsell higher-value products to?
• Who can you send personalized messages to keep them from churning?
• Who is most likely to engage with your brand?
• Who can you offer limited-time offers to and try to reactivate?

Bottom line: RFM Analysis quantitatively determines your best customers, customers who need to be re-engaged ASAP, and everyone in between.

RFM Analysis & New Homepage in Peel

With RFM Analysis being such a valuable tool for discovering customer groups based on different buying stages, loyalty levels, and needs for engagement, we built the new Peel homepage around it.

We know that ecommerce teams have various stakeholders who have completely different user habits. There are the daily users, others who are getting reports weekly, and leadership teams who may only be exposed to Peel once a month in reporting from their teams.

With that in mind, we wanted to build a page that no matter when you look at it, at whatever frequency, it’ll deliver immediate value and give you the overarching story of your business performance. 

Users can now hop into Peel and get a quick snapshot of what’s going on with their customers and be able to take quick action. The new homepage is the perfect place to get started in Peel.

Here’s a breakdown of what you’ll find on the page.

New Homepage Top Section

The first thing you’ll find on the new homepage is the top section that has a super valuable snapshot of your north star metrics – number of orders per customer, lifetime value (LTV) per customer, and percentage of returning orders.

On the top left, you’ll see the one ring to rule them all in ecommerce: your orders per customer. The ideal goal here is 3 orders per customer and if you achieved that, your green ring would be entirely closed on the screen.

Realistically, we advise our customers to push for 2+ purchases per customer. Anything in that 2-3 range or above means your business is thriving with lots of repurchasers and loyal customers. 

Next are your average LTV over 24 months and your percentage of returning orders weekly (vs new customers stacked in a mini 7-week bar chart). These are quick ways to see how much your customers are spending over time and how many of your orders are coming from repeat purchasers. Both are incredibly important metrics for any ecommerce business to keep an eye on and improve, so we deliver that info at a glance here (you can still go to those specific metric reports and dive in with segmentation and all the usual Peel-y stuff if you want, but this is your quickest, high-level view!).

The final piece of the top section is a little bit of helpful inspiration. You’ll find recommendations there that’ll point out and link to a great report or metric to view, or a good idea for an action you can take immediately within Peel. There are so many stellar nuggets of information in there crafted by our years of experience with DTC brands. We always want to spark useful growth ideas for our customers.

RFM Analysis 

As with all of our analyses with Peel, there's a massive chasm that exists in the effort between how you would compute the metric yourself and the accessibility in our platform. RFM Analysis is no exception, so we'll go over both sides of the coin.

How to do RFM analysis yourself

If you have data on all of your customers and are not using Peel already, you can follow these steps to compute RFM segments on your own.

All customers in your account will be given an RFM segment using their score of 1 to 5 (integer values) on three metrics:

• Recency (number of days)
• Frequency (number of orders)
• Monetary (LTR - Lifetime Revenue)

A score of 5 means a customer is in the top 20% group, and a score of 1 indicates a customer is in the bottom 20% group.

How to:

1. Create a table with all of your customers
2. In a “Recency” column, enter a formula to count how many days since the customer’s last order
3. In a “Frequency” column, enter a formula to count how many orders each customer placed
4. In a “Monetary” column, enter a formula to count lifetime revenue of each customer
5. Using code, pivot table, or formulas, you’ll now want to create five groups of customers for each of the three columns and make sure each group has the same number of customers. Customers with a high score (most recent, highest frequency and highest monetary) will get a score of 5. We’ve included some Python code below as an example function:

‍

import numpy as np
import pandas as pd

def write_score(customer_data, metric, reverse=False):

	bins=[
		-1, 
		np.percentile(customer_data[metric], 20), 
		np.percentile(customer_data[metric], 40),
		np.percentile(customer_data[metric], 60), 
		np.percentile(customer_data[metric], 80), 
		customer_data[metric].max()
	]

	labels = [1, 2, 3, 4, 5]
	if reverse:
		labels.reverse()

    customer_data[f"{metric}_score"] = pd.cut(
	       customer_data[metric], 
         bins=bins, 
         labels=labels
		).astype("int")

# Get raw values
cohort_df["recency"] = (today - cohort_df['max_order_date']).days
cohort_df["frequency"] = cohort_df["number_of_orders"]
cohort_df["monetary"] = cohort_df["sum_of_revenue"]


# Get score
write_score(cohort_df, "recency", reverse=True)
write_score(cohort_df, "frequency")
write_score(cohort_df, "monetary")

‍

Once all the scores are filled, you’ll want to group the Frequency and Monetary scores together to place customers on the grid. You do so by taking the average of two scores and round it down because scores have to be integer values 1 through 5.

Now that all customers have a score, you can assign the group of each customer based on their scores, see our grid for details, and count the number of customers. From there, you’ll want to compute the average number of orders per customer, the lifetime revenue and the average number of days since the last order per customer for each of the groups.

How Peel computes RFM automatically for you

In the middle of the new homepage is where you’ll find your RFM Analysis visualized into a grid showing your RFM Segments. 

On your x axis is the Recency of orders bucketed into 5 groups based on days since the customer’s last order.

The y axis is a combination of Frequency (total number of orders) and Monetary Value (LTR) bucketed into 5 groups. Based on their scores, all of your customers are organized 10 predefined groups on the grid:

*Note: One frequent point of confusion with RFM Analysis is that the sections of the grid are not proportionally scaled to the percentage of customers in that group. We totally get that it’s a logical thing for users to expect the sizes of the sections on the grid to correlate with the size of the group, but that’s not the case. Instead, the segments on the grid are predefined by the scores they represent.

As you can see on the image, the customers grouped into these 10 segments:

• Champions
• Loyal Customers
• Potential Loyalist
• New Customers
• Promising
• Need Attention
• About to Sleep
• Can’t lose them
• At Risk
• Hibernating

On the right of the grid, you can filter for R, F, or M to the average number of days each of those groups took to come back and repurchase, how many orders on average each group is making, and the average monetary value in LTR for each group.

Taking Action with RFM Analysis

You can see what each of your segments mean for your brand here on the final section of the new homepage:

This is taking the manual lift of extracting your customer data and figuring out their buying habits and loyalty levels through code, pivot tables, or formulas to create actionable segments like we detailed above; it’s all precomputed for you here.

From there, you need to strategize on how to engage them and we offer hints on the page of what you can do: 

What loyalty offers do your “Champions” and “Loyal Customers” deserve from you at this point in their purchasing journey?

What about your “Potential Loyalists” segment? How can you get them over the line into the higher tiers?

“Can’t lose them” are customers who have spent a lot and made a lot of purchases but you haven’t seen them in a long time. How can you get them engaged with your content and your products? What do you need to share with them?

Each segment has their own needs and this is the perfect place to start personalizing their journey with your brand. 

The final piece of the puzzle is the Create an Audience option. All you have to do is select any of your RFM segments from the list and click to create an automatic Audience. On the Audiences dashboard, you’ll be able to see a breakdown of all that segments’ important growth metrics, plus a location map of where these customers are. It’s everything you need to build a killer retention campaign. 

Plus, Audiences allows you to push that customers list to Klaviyo, Facebook and Attentive to take action immediately. Check out the full breakdown of Audiences here if you want to learn more.

From there, you can use our Audience Traits feature to get a high level view of some of the most important insights on your RFM segments like:

• Top Product
• Top Discount Code
• Top City
• Top SKU
• Top Customer Tag
• Top Attributed Channel
• The UTM’s Source/Campaign that customers in the Audience were acquired on

These are just more tools for you to use in personalizing content and campaigns to your RFM Audiences.

Get Started with RFM Analysis

Peel’s new homepage design and inclusion of the new RFM analysis feature are really in service of helping you understand your current customer behavior so you can make improvements to your strategy and drive more of those crucial 2nd and 3rd purchases.

If you want to get started seeing your RFM analysis and building more personalized retention campaigns, you can try Peel free for 7 days.