Identify Your Best Customers Using RFM Segmentation
Published on: August 01, 2022
Last updated: September 23, 2024 Read in fullscreen view
Last updated: September 23, 2024 Read in fullscreen view
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What does RFM stand for?
- Recency (R) – How many days ago customer made a purchase? Deduct most recent purchase date from today to calculate the recency value. 1 day ago? 14 days ago? 500 days ago?
- Frequency (F) – How many times has the customer purchased from our store? For example, if someone placed 10 orders over a period of time, their frequency is 10.
- Monetary (M) – How many $$ (or whatever is your currency of calculation) has this customer spent? Simply total up the money from all transactions to get the M value.
RFM (Recency Frequency Monetary) analysis or RFM segmentation is an effective customer segmentation technique to improve your marketing.
Instead of reaching out to 100% of your audience, target only specific customer segments that can prove beneficial for your business in future.
Thus, RFM analysis will help you strengthen your relationship marketing and increase customer loyalty.
Benefits of RFM
The RFM model has several advantages for customer loyalty.
- RFM is simple and easy to implement. You only need three data points for each customer, which you can easily obtain from your transaction records.
- RFM helps you identify your most loyal and profitable customers, who are likely to have a high lifetime value and a low churn rate. You can then focus your marketing efforts and rewards on these customers, increasing their satisfaction and retention.
- RFM helps you segment your customers into different groups based on their behavior and preferences. You can then tailor your communication, offers, and incentives to each group, enhancing their relevance and engagement.
Drawbacks of RFM
The RFM model also has some limitations
- RFM does not account for other factors that may influence customer behavior, such as product quality, customer service, brand image, or competition. It may also miss some potential customers who have a high propensity to buy but have not purchased recently or frequently.
- RFM assumes that customer behavior is stable and consistent over time, which may not be true in dynamic and changing markets. Customer needs, preferences, and expectations may evolve over time, requiring you to update your RFM segments regularly.
- RFM does not measure customer satisfaction, loyalty, or advocacy directly. It only relies on transactional data, which may not capture the emotional and psychological aspects of customer loyalty.
How to use RFM?
To use the RFM model, you need to follow some steps.
- Collect and analyze your customer transaction data and assign each customer a score for recency, frequency, and monetary value. You can use different scales and methods to score your customers, depending on your business goals and criteria.
- Segment your customers based on their RFM scores. You can use different techniques to segment your customers, such as clustering, quartiles, or ranges. You can also create different names and labels for each segment, such as champions, loyalists, at-risk, or dormant.
- Design and execute your marketing strategies for each segment. You can use different tools and channels to communicate with your customers, such as email, SMS, social media, or phone. You can also use different types of offers and incentives, such as discounts, coupons, freebies, or loyalty programs.
Key Takeaways
RFM Segmentation is a frequently used data analysis method to increase the ROAS of companies. It is mainly based on the customers' behavior.
RFM segmentation is not enough to optimize ads. So why?
- One of the first reasons is that RFM analysis measures the behavior of customers. Although it is used to measure the behavior of visitors, it cannot be said to be a beneficial method to optimize visitor data. However, many companies want to gain new customers as well as current customers. Accordingly, RFM segmentation may not help acquire new customers.
- Secondly, RFM analysis calculates with only three metrics: recency, frequency, and monetary. However, measurements made with these metrics may not always give complete results. There should be more and different metrics to understand the behavior of visitors and customers.
- Thirdly, e-commerce companies are changing and developing day by day. Instant campaigns and strategies almost must in the data analytics and advertisements sector. Therefore, self-learning and automatic segment analysis with the machine learning algorithm are also more valuable and useful.
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