A Step-by-Step Guide to Performing RFM Analysis

In today’s data-driven business landscape, understanding your customers is essential to achieve sustainable growth. With countless interactions and transactions happening every day, it’s crucial to identify which customers truly drive your business forward.

This is where RFM Analysis comes in an effective, behavior-based segmentation process that enables businesses to categorize customers based on their purchasing habits. These segmented buckets provide a clearer understanding of customer behavior, allowing businesses to focus their efforts and tailor strategies in the right direction.

What is RFM Analysis and Why is it Essential for Businesses?

RFM stands for Recency, Frequency, and Monetary Value. RFM analysis is a customer segmentation method that leverages existing customer behavior data to assess, predict, and improve key business processes by focusing on three key factors:

RFM model-based customer segmentation allows businesses to understand their customers better and optimize the efforts for each customer segment. The analysis sets the direction for improving marketing effectiveness, refining customer retention strategies, and optimizing resource allocation.

Organizations today want to cut through the noise and proactively shape the behavior of their customers and prospects. RFM analysis stands out as a tool that helps businesses continuously refine their processes to boost customer engagement. Beyond segmenting customers and guiding resource allocation, RFM analysis enables businesses to build a strategic customer engagement framework. It helps organizations answer crucial questions, such as:

• Which customers need a little nudge to become frequent buyers?
• Who are the customers that are most likely to respond to targeted offers?
• How can businesses re-engage customers who have decreased their spending or interaction?
• Which customers represent the highest value and should be prioritized for personalized experiences or loyalty programs?
• Who has the potential for growth with just the right incentives or reminders?

By answering these questions, and optimizing processes, organizations can strengthen customer relationships and drive sustainable growth.

Step-by-step Guide to Conducting RFM Analysis

In this step-by-step guide, we will walk through how to conduct RFM analysis for customer segmentation using an illustrative example of a company. We’ve gathered specific data for this example, which includes customer ID, sales order number, sales amount, and order date. The data will help us assign RFM scores to each customer and segment them accordingly.

Step 1: Gather Customer Data

The first step in the RFM analysis is to gather the necessary data. For our example, we’ve collected the following types of data:

• Customer ID: Each customer’s unique identifier.
• Sales Order Number: Reference number for every purchase made.
• Sales Amount: The total value of each transaction.
• Order Date: The date when the transaction occurred.

Free RFM Analysis Excel Template

Download our free RFM analysis template and evaluate key customer metrics across Recency, Frequency, and Monetary values. Score and segment customers based on their behavior, and plan targeted actions to improve customer engagement with your business.

Download the free template

Step 2: Using the Excel Template for RFM Analysis

In this step, the team can use the Excel template to assign RFM scores. The template leverages a percentile-based method to dynamically calculate scores for Recency, Frequency, and Monetary based on your customer data.

Edstellar RFM Analysis Excel Template: Scoring for Recency, Frequency, and Monetary Scoring (0 to 9)

For each of the recent dates, the number of purchases from a customer and the total monetary value of the purchases from the customer, the Edststellar downloadable RFM Analysis Excel template applies a percentile-based method to assign scores. The template compares each customer’s performance (e.g., recency, frequency, or total spend) relative to others in the dataset and dynamically assigns a score from 0 to 9 based on the relative rank of each customer.

This ensures that customers are evaluated relative to others in the subset of data you're analyzing, with higher scores indicating better performance.

How to Use the Edstellar RFM Analysis Excel Template

• Input Data into the Excel: The team can input key customer data, such as Customer ID, Sales Order Number, Sales Amount, and Order Date, into the provided Excel template.

• Creating the Pivot Table:

A pivot table must be created with the following setup:

1. Customer ID should be placed in the Rows section of the Pivot to group the data by each customer.
2. Order Date should be moved to the Sum of Values section of the Pivot, and the Max of Order Date should be calculated to display the most recent purchase date for each customer.
3. Sales Order Number should be moved to the Sum of Values section of the Pivot, with the Count function applied to determine the total number of purchases made by each customer.
4. Sales Amount should be moved to the Sum of Values section of the Pivot to calculate the total spending for each customer.

• Customer RFM Analysis:

Once the pivot table is created, the following data should be moved into the “Customer RFM Analysis” tab of the RFM Analysis Excel Template:

1. Customer ID
2. Max of Order Date (Recent Order Date)
3. Sales Order Number (Sales Order Count per Customer)
4. Sum of Sales Amount (Total Spend per Customer)

• RFM Score Calculation: The template will then automatically calculate each customer’s Recency, Frequency, and Monetary scores using percentiles. These scores will be populated in the Recency, Frequency, and Monetary columns, respectively.

• Analyzing Customers Based on R, F, and M Factors: Once the scores are calculated, the team can use filters in the Excel sheet to list down the customers based on their Recency, Frequency, and Monetary scores. The filters allow the team to easily identify the most valuable customers and those who need more attention.

For a detailed walkthrough on creating and using the RFM Analysis Excel template, check out this video by renowned trainer David Langer. 

Step 3: Segmenting Customers based on RFM Scores

Once the Recency, Frequency, and Monetary scores are calculated, the team can segment customers into different categories based on their scores. These segments help identify customers who are the most valuable, as well as those who need more engagement or reactivation.

Use the calculated RFM scores to group customers into specific segments, such as:

• High Value (Top Performers): Customers with high Recency, Frequency, and Monetary scores. These customers are highly engaged and generate significant revenue. Focus on retaining them with loyalty programs or exclusive offers.
• Loyal Customers: Customers with a high Frequency score but potentially moderate Recency and Monetary scores. They purchase frequently but may not be as recent or high spenders. Consider offering personalized discounts or re-engagement campaigns to keep them active.
• Promising Customers: Customers with high Recency and moderate Frequency and Monetary scores. They are recent buyers but may not be frequent or high spenders yet. Encourage repeat purchases with targeted offers or incentives.
• At-Risk Customers: Customers with low Recency scores but higher Frequency and Monetary scores. These customers used to be active but have not purchased recently. A win-back campaign or re-engagement strategy may help bring them back.
• Low Value Customers: Customers with low Recency, Frequency, and Monetary scores. These customers are not engaging or contributing significantly. Consider engaging them through promotions or introductory offers to increase their activity.

Step 4: Strategizing Future Actions Based on Segments

After segmentation, the team can focus on specific strategies for each group:

• Retention and Loyalty: For High Value and Loyal Customers, design programs to reward loyalty, offer early access to products, or provide VIP benefits.
• Re-engagement: For At-Risk and Low Value customers, create win-back campaigns with special offers, reminders, or personalized outreach to encourage re-engagement.
• Upselling and Cross-Selling: For Promising Customers, target them with upsell or cross-sell opportunities tailored to their purchase history or preferences.
• Targeted Promotions: Use promotions or discounts to encourage more frequent purchases from Loyal Customers or incentivize Low Value Customers to increase their spend.

Why RFM Analysis is a Game-Changer for Growing Businesses

For startups and smaller retailers with limited marketing resources, RFM analysis proves to be an invaluable asset because of its:

• Straightforward Implementation: You don't need complex software or advanced analytics tools to get started. The beauty of RFM lies in its intuitive nature - the principles are easy to grasp, and the insights are immediately actionable. Even team members without extensive data analysis experience can understand and apply the findings.

• Cost-Effective Solution: Unlike many sophisticated marketing tools that demand significant investment, RFM analysis can be performed using basic business analytic tools you already have. With just a standard spreadsheet and your existing customer data, you can start segmenting your customers and uncovering valuable insights.

• Perfect Fit for Digital Marketing: While RFM analysis has its roots in traditional direct mail marketing, it's exceptionally well-suited for modern digital marketing strategies. Whether you're planning email campaigns, social media engagement, or personalized content delivery, RFM insights can help you craft targeted messages that resonate with different customer segments - all without breaking the bank. 

Maximizing Ecommerce ROI With RFM Analysis

This case study was taken by Prinkit Patel, a McKinsey Forward Graduate and Go-To-Market Strategist with expertise in Esports, luxury goods, and consulting. Let us have a look at this groundbreaking approach:

Company Overview

Bonding Gifts is a personalized gift store which operates in a competitive digital market. It spends 1.5 lacs INR daily on ads across platforms like Facebook, Instagram, and Google, and sees around 500 purchases per day.

The Challenge

With a large daily ad spend, Bonding Gifts needed to target the most responsive customer segments to ensure maximum ROI from their marketing efforts. They used RFM analysis to segment customers into three groups:

• High-Value Engaged Shoppers: Recent, frequent, and high-spending customers. They were targeted with loyalty programs and exclusive offers to foster brand advocacy.
• Potential Repeat Customers: Moderately engaged customers, nurtured with email marketing and personalized incentives to increase purchase frequency.
• Dormant Customers: Inactive customers, re-engaged with win-back campaigns offering exclusive deals to reignite interest.

Outcome

• Conversion Rate Increase: A 20% boost in conversions from targeted campaigns for Engaged Shoppers.
• Revenue Growth: A 25% increase in daily sales, adding 1.2 lacs INR to daily revenue.
• Optimized Ad Spend: A 15% reduction in ad spend, while achieving higher sales.

By applying RFM analysis, Bonding Gifts effectively segmented their customer base, enhancing marketing precision and significantly improving ROI.

Conclusion

Incorporating RFM into your marketing strategy helps you pinpoint your top customers and discover targeted ways to engage them based on their purchasing habits. Segmentation plays a key role in boosting engagement and driving conversions, no matter the size of your business. By leveraging RFM, you can enhance your marketing efforts across every stage of the customer journey. 

At Edstellar, we understand that maximizing your corporate training investment requires more than just delivering quality content - it demands a deep understanding of how your organization interacts with training programs. With our network of 5000+ verified corporate trainers and comprehensive training management platform, integrating RFM analysis becomes easy and impactful.