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In today’s eCommerce landscape, understanding your customers is crucial. Customers are key to business success, and in terms of marketing, it's estimated that 80% of your total sales come from just 20% of your top customers.
Have you ever considered whether there’s a way to quantify and score your customers? What if we told you that there is a method that can simplify this process? It’s called RFM analysis.
What is RFM Analysis?
RFM Analysis is a marketing technique used to identify and target the most valuable customers of a business. It is based on the idea that past behavior is a good predictor of future behavior, and that certain combinations of recency, frequency, and monetary value (RFM) indicate a customer's value to the business.
Why Is RFM Analysis Important?
RFM Analysis involves analyzing customer data to determine how recently a customer made a purchase, how frequently they make purchases, and how much they spend during each transaction. This data is then used to create segments or groups of customers based on their RFM scores. These segments can create targeted marketing campaigns and identify opportunities to upsell or cross-sell products to customers.
RFM Analysis is often used in conjunction with other marketing techniques, such as customer lifetime value (CLV) analysis and customer segmentation, to create more effective and personalized marketing strategies.
Key Components of RFM Analysis
- Customer segments: Groups of customers based on their RFM scores.
- Targeted marketing campaigns: Marketing efforts aimed at specific customer segments based on their RFM scores.
- Upselling and cross-selling: Strategies to encourage customers to purchase additional products or services.
- Customer lifetime value (CLV) analysis: A technique used to calculate the value of a customer to a business over their lifetime.
- Customer segmentation: The process of dividing customers into groups based on characteristics such as demographics, behavior, or interests.
How to Conduct RFM Analysis
To perform RFM analysis, you will need data on your customer's purchase history, including the dates of their purchases, the number of purchases they have made, and the amount they have spent. You can use this data to calculate each customer's RFM scores.
Step 1: Collect Customer Data
The first step is to collect customer’s data which include- transactional history, purchase amount, purchase frequency, demographic data, recency data.
Recency:
To calculate the recency score, you can use the customer’s most recent purchase. Customers who have made a purchase more recently will have a higher recency score.
Frequency:
To calculate the frequency score, you can use the total number of purchases made by the customer. Customers who have made more purchases will have a higher frequency score.
Monetary:
To calculate the monetary value score, you can use the total amount spent by the customer. Customers who have spent more money will have a higher monetary value score.
Step 2: Score customers for RFM values
The scores for each of these three factors are typically assigned on a scale from 1 to 5 or 1 to 10, with higher scores indicating more valuable customers.
Step 3: Segmentation of the customer’s
Depending on the specific business objectives and desired customer outcomes, we segment customers accordingly. For example,
- Top-performing customer
- At-risk customers
- New customers
- Dormant customers
Step 4: Applying Insights
The final step is to tailor in marketing and retention efforts. Here are few examples of how you can apply insights
- Personalized Email campaigns
- Loyalty programs
- Customer win-back
- Target social media
- Active website experience
Example of RFM model:
In this example, the customer with the highest RFM score is Customer 6, with a score of 2000, indicating that this customer is the most valuable based on their recency, frequency, and monetary value.
The second most valuable customer is Customer 4, with a score of 1700, followed by Customer 1 with a score of 1500.
RFM Analysis Examples
Here are a few examples of how businesses have used RFM Analysis to improve their marketing efforts:
- A clothing retailer used RFM Analysis to create targeted email campaigns based on customers' RFM scores. They found that high-value customers were more likely to make a purchase when they received personalized, relevant email offers.
- A subscription-based meal delivery service used RFM Analysis to identify and target lapsed customers. They found that offering discounts and promotions to these customers increased the likelihood of them renewing their subscriptions.
- A B2B software company used RFM Analysis to identify and target high-value customers with upsell and cross-sell opportunities. They found that these customers were more likely to make additional purchases when they received personalized recommendations based on their purchase history.
- A sporting goods retailer used RFM Analysis to create targeted in-store promotions based on customers' RFM scores. They found that customers who had a high monetary value score were more likely to respond to promotions for high-ticket items, while customers with a high frequency score were more likely to respond to promotions for lower-priced items.
Common Challenges in RFM Analysis
- Data quality and completeness
- Lack of historical data
- Difficulty in segmentation
- Integration with other systems and tools
- Overlooking non-purchase behavior
RFM Analysis for eCommerce
RFM Analysis can be a useful tool for eCommerce brands to identify and target their most valuable customers. In an eCommerce setting, recency, frequency, and monetary value can be calculated based on the customer's purchase history.
- For recency, you can use the customer’s most recent purchase date.
- For frequency, you can use the total number of purchases made by the customer.
- And for monetary value, you can use the total amount spent by the customer.
Using these metrics, you can create RFM segments of your eCommerce customers and use them to create targeted marketing campaigns and identify opportunities for upselling or cross-selling.
For example, you might target high-value customers who have made recent purchases and have a high frequency of purchases with offers for related or complementary products. Or, you might target low-value customers who have not recently made a purchase with promotions or incentives to encourage them to purchase.
RFM Analysis for Subscription Businesses
RFM Analysis can be particularly useful for subscription businesses, as it can help identify and target the most valuable customers based on their past behavior. Recency, frequency, and monetary value can be calculated differently in a subscription business than in a traditional retail setting.
- For recency, you can use the date of the most recent subscription payment or renewal.
- For frequency, you can use the number of times the customer has renewed their subscription.
- For monetary value, you can use the total amount the customer has spent on their subscription over time.
Using these modified definitions of recency, frequency, and monetary value, you can create RFM segments of your subscription customers and use them to identify upselling, cross-selling, and retention opportunities.
For example, you might target high-value customers who have recently renewed their subscriptions and have a high frequency of renewals with offers for additional products or services. Or you might target low-value customers who have not renewed their subscriptions recently with retention offers or incentives to continue their subscriptions.
Related Read: Cohort Analysis
Conclusion
RFM analysis can help brands identify their most valuable customers and develop targeted marketing strategies. However, analyzing customer data can be challenging, particularly for organizations that don’t have a strong data foundation.
That's where Saras Analytics comes in. Our data extraction and consolidation services can help you overcome data silos and identify your most profitable customers in conjunction with other growth analytics. By leveraging the power of RFM analysis, you can make data-driven decisions that increase customer satisfaction, loyalty, and profitability.
Contact Saras today to learn more about how we can help you gain actionable insights from your customer data.
