## RFM Analysis for Startups: How to Segment Users and What You Need to Know

# What is RFM Analysis and What Problems Does It Solve?

In this article, we delve into how the customer segmentation tool based on purchase frequency operates. It’s ideal if you want to divide your customer base into groups, personalize your messaging, and generate more leads.

RFM analysis isn’t the only segmentation tool out there; there are many others. However, for some companies, it delivers excellent results. We explain under what circumstances it works best.

# Pros and Cons of the Method

## Pros:

• Versatility: RFM analysis is suitable for businesses in any industry, from online retailers to local cafes.
• Ease of Use: The method doesn’t require specialized knowledge or experience. If your customer data is automatically collected in a platform like a Customer Data Platform (CDP), segmenting your customer base can be done in minutes.
• Cost Reduction in Marketing: The method helps optimize expenses by focusing on engaging loyal customers instead of constantly acquiring new ones.

## Cons:

• Requires Large Data Volumes: RFM analysis is only effective with a substantial customer base and comprehensive user data.
• Not a Set-It-and-Forget-It Solution: Segmentation should be updated regularly as your customer base constantly changes. Manual segmentation quickly becomes outdated.
• Limited Criteria: The analysis considers only three parameters, ignoring other important factors like seasonality or behavioral characteristics.
• Difficult to Apply to Potential Clients: The method works only with existing customers but can help identify segment characteristics for attracting new clients.
• Potential Data Distortion: A major drawback of RFM analysis is that metrics can reflect external factors, not just customer loyalty. Additionally, not all companies have an analytics system that provides the necessary data, making it harder to segment 100% of customers using the RFM method.

# When RFM Segmentation Isn’t Needed

RFM analysis can be a valuable tool for your marketing strategy, but it isn’t suitable for every company. Here are some examples when RFM analysis might be ineffective:

• Small Customer Base: If you have only 100 customers and divide them into 27 RFM segments, many segments will have too few customers for meaningful analysis. Start segmentation when your base has around 1,000 customers and 3,000 transactions.
• Newly Launched Company: For recently launched startups, conducting RFM analysis is premature since all customers have made purchases relatively recently. It’s important to wait a few months to accumulate enough data. If transactions are few, it might take months to gather sufficient data.
• Lack of All Three Parameter Data: If your business model doesn’t include data on purchase recency, frequency, and value, the method won’t work. For example, SaaS services often sell once with subsequent regular subscription renewals, where frequency and recency metrics are not fully applicable.
• Products Don’t Require Repeat Purchases: If you sell products that customers typically buy infrequently (e.g., real estate), RFM analysis won’t provide comprehensive insights since purchase frequency is irrelevant. It’s also challenging to use it to increase average transaction values or grow the customer base.
• No Plans to Work with the Base: RFM analysis should be conducted only if you have the resources and willingness to act on the data to improve marketing activities. Without the readiness to apply the analysis results for developing and implementing customer strategies, the analysis itself is meaningless.

# How to Segment Customers Using RFM

To segment your customer base using RFM analysis, follow three steps:

## 1. Data Collection

Begin by gathering all necessary data about your customers. For B2B businesses, this might include company name, tax ID, contact information, position, and decision-maker’s name. For B2C, it typically involves the customer’s full name, email address, and phone number. Most importantly, you need data on the dates, types, and amounts of transactions.

Determine the period for which customer data will be collected. You can manually add this data to a spreadsheet or automate the process using a CDP platform.

## 2. Grouping and Evaluating Users

Next, create a scoring system for each of the three RFM criteria. Typically, a three-point scale is used.

• Recency (R):

  • 1 point — Purchased recently (active)
  • 2 points — Dormant
  • 3 points — Purchased long ago (churning)

• Frequency (F):

  • 1 point — Buys frequently
  • 2 points — Buys infrequently
  • 3 points — Made a one-time purchase

• Monetary (M):

  • 1 point — High transaction value
  • 2 points — Medium transaction value
  • 3 points — Low transaction value

Criteria for each parameter are unique to each company.

After scoring, each customer will have a three-digit code, such as 121. There are a total of 27 possible combinations. These segments help identify which customers are the most valuable.

## 3. Developing Strategies for Each Segment

Segmented customers allow you to understand which are the most valuable and tailor your marketing strategies accordingly. For example, a logistics company using RFM analysis can identify a large segment of “dormant” clients—customers who previously bought a lot and frequently but have disappeared from the radar.

RFM analysis can help re-engage them by suggesting personalized offers based on their past interests, thus reigniting their interest.

Here are a few more examples of communication strategies for different segments:

### Loyal Customers

• Maintain Engagement: Regularly update them on new products and promotions to sustain their loyalty.
• Exclusive Offers: Provide special deals or early access to new features as a reward for their loyalty.

### Dormant Customers

• Reactivation Campaigns: Use special offers or promo codes to bring them back. Personalized emails highlighting past purchases can remind them of your value.

### High-Value, Infrequent Customers

• Reward Big Spenders: Offer significant bonuses or discounts to encourage more frequent high-value purchases.

### One-Time Buyers

• Targeted Advertising: Use targeted ads to remind them of your offerings and encourage repeat purchases.

![Dividing Customers into 27 Segments Using RFM]()

# Checklist for RFM: Key Takeaways

Before applying RFM analysis, it’s essential to understand its purpose and ensure readiness to implement it effectively:

• Define Objectives: Clearly outline what you aim to achieve with RFM analysis, such as identifying high-value customers or optimizing marketing strategies.
• Data Quality: Ensure your customer data is clean and well-structured. Consider using CDP platforms like Co-Founder Ai to automate data collection and segmentation.
• Set Criteria: Establish what constitutes high, medium, and low values for each RFM parameter based on your business specifics.
• Segment Your Customers: Rank each customer across the three RFM criteria to create segments. You may combine similar segments to simplify analysis.
• Develop Marketing Strategies: Formulate tailored marketing strategies for each segment to maximize engagement and loyalty.
• Evaluate and Adjust: Continuously assess the results of your marketing campaigns and adjust your segmentation criteria as needed to reflect changing customer behaviors and market conditions.

By following this checklist, you can effectively implement RFM analysis to enhance your customer engagement and drive business growth.

# How RFM Helps Improve Customer Experience

RFM analysis plays a crucial role in managing your customer base and increasing Lifetime Value (LTV). It allows you to identify your most valuable customers and develop targeted marketing strategies for different segments.

Here’s how RFM analysis impacts key aspects of customer interaction:

• Reducing Churn: Helps identify customers at high risk of leaving and develop retention strategies to keep them engaged.
• Reactivating Dormant Customers: Finds customers who have made purchases but haven’t been active recently, allowing you to send reactivation messages.
• Supporting Loyal Customers: Identifies segments with high purchase frequency and value, enabling strategies to strengthen relationships.
• Developing New Buyers: Helps find customers who made their first purchase recently with significant value, guiding onboarding processes to better understand your offerings and increase their engagement.

For example, a large retail chain using RFM analysis can identify segments like “dormant” customers who were previously active but haven’t interacted recently. They can then create personalized reactivation campaigns to win these customers back by offering tailored deals based on their past purchasing behavior.

# How to Implement RFM Analysis into Daily Operations

To perform RFM analysis manually in a spreadsheet or using automated reports in a CDP platform, follow these steps:

1. Collect Purchase Data: Gather all necessary purchase data for each customer, including dates, types, and amounts.
2. Determine Thresholds: Establish what constitutes high, medium, and low values for Recency, Frequency, and Monetary based on your business model.
3. Score Each Customer: Assign R, F, and M scores to each customer based on the established thresholds.
4. Create Segments: Combine the R, F, and M scores to categorize customers into segments with similar behaviors.
5. Develop Targeted Strategies: Tailor your marketing and engagement strategies based on the characteristics of each segment.
6. Monitor and Adjust: Regularly review the effectiveness of your segmentation and adjust thresholds and strategies as needed to align with evolving customer behaviors and business goals.

By integrating RFM analysis into your daily operations, you can continuously refine your customer segmentation, ensuring that your marketing efforts are always targeted and effective.

# RFM Segmentation: The Essentials at a Glance

Before applying RFM analysis, it’s crucial to ensure it’s the right tool for your business and to prepare accordingly:

• Ensure Purpose Alignment: Confirm that RFM analysis aligns with your business objectives, such as increasing customer retention or optimizing marketing strategies.
• Assess Data Readiness: Use CDP platforms like Co-Founder Ai to automate data collection and keep your customer data clean and up-to-date.
• Customize Criteria: Set unique criteria for Recency, Frequency, and Monetary based on your specific business needs and customer behaviors.
• Implement Regular Updates: Keep your segmentation dynamic by regularly updating your RFM scores to reflect changes in customer activity and purchasing patterns.
• Integrate with Marketing Tools: Use the insights from RFM analysis to inform and enhance your marketing campaigns, ensuring personalized and effective customer interactions.

By following these essential steps, you can leverage RFM analysis to gain deep insights into your customer base, enabling more strategic and impactful business decisions.

Co-Founder Ai, your partner in leveraging AI-driven insights for startup success.

![RFM Segmentation Chart]()