RFM analysis
RFM analysis is one of the first and most popular targeting models in database13 and direct marketing, as used by catalog-order, charity donations, digital companies, etc. RFM is a marketing analysis tool used to identify and quantify the value of a customer to a business based on three specific factors: Recency, Frequency, and Monetary value. The acronym RFM stands for:
•
Recency: How recently a customer has made a purchase. This metric is based on the premise that customers who have purchased recently are more likely to respond positively to new offers than those who have not purchased in a long time.
•
Frequency: How often a customer makes a purchase within a given time frame. A higher frequency indicates a higher level of customer engagement and loyalty.
•
Monetary Value: The total amount of money a customer has spent during their relationship with the company. This metric helps businesses identify their most valuable customers in terms of revenue contribution. In digital services a proxy of monetary value can be time spent with the service, for example a website or a mobile app.
Although RFM analysis itself is more descriptive and segmentative in nature, it provides valuable inputs for predictive analytics. The RFM scores can be used as features in machine learning models to predict future customer behaviors, such as the likelihood of making a purchase, the expected monetary value of future purchases, or the probability of churn. By incorporating RFM data into broader predictive models, businesses can anticipate future trends and adjust their strategies accordingly.
How RFM Analysis Works
RFM analysis involves segmenting customers into various groups based on the three dimensions mentioned above. Typically, each dimension is assigned a score, often on a scale of 1 to 5 (with 5 being the highest). Customers are then scored based on their transaction history data:
Recency: Customers who made a purchase very recently get the highest score, while those who purchased a long time ago get the lowest score. Frequency: Customers who purchase more frequently get a higher score.
Monetary: Customers who spend more money get a higher score.
The final RFM score for each customer is often represented as a concatenated string of these three numbers (e.g., 5-5-5 for a customer who scores the highest in all three categories).
Examples of Targeting Strategies Using RFM Analysis
1. Differentiating Campaign Messages:
•
High RFM Scores: Customers with high scores across all RFM categories (like 5-5-5 or 4-4-4) are your most valuable and loyal customers. Target them with exclusive offers, loyalty programs, and premium services that acknowledge their value to your business.
•
High Recency, Low Frequency and Monetary: These customers are recent but not frequent shoppers and haven't spent much yet. Target them with incentives to make another purchase or to join a membership that encourages frequent buying.
•
High Frequency, Low Recency: Customers who used to shop frequently but haven’t made recent purchases might be slipping away. Re-engagement campaigns using personalized emails or retargeting ads14 can remind them of what they're missing.
•
Low on all RFM metrics: possibly at risk of churning or already disengaged. They could be targeted with "Reactivation Campaigns" offering very tempting deals, or feedback surveys to understand their disengagement, possibly paired with a re-engagement incentive.
In general, we can say:
•
155 customers need a reminder,
•
551 customers need to be upsold (make them to spend more),
•
515 customers need a sticky recurring relationship (make them to buy more often). 2. Creating Customer Loyalty Programs: RFM can help in designing tiered loyalty programs where customers with higher RFM scores get more benefits. This not only rewards the best customers but also motivates others to improve their RFM scores by interacting more with the brand.
3. Launching New Products:
•
When introducing new products, companies can target customers with high Monetary and Frequency scores as they are more likely to try new offerings and spend more.
4. Preventing Churn:
•
Customers with a once-high but rapidly declining RFM score might be at risk of churning. Targeted intervention through special offers, surveys to inquire about their changing needs, or personalized communications can help retain these customers.
5. Optimizing Marketing Spend:
•
RFM helps in identifying less profitable or low-engagement segments where marketing spend can be reduced. Conversely, more resources can be allocated to high-value segments identified by RFM, enhancing ROI. How to calculate the RFM scores
A requirement is to gather Customer Transaction Data: collect data on each customer’s purchasing history. You need at least the following information:
•
Customer ID
•
Order Date
•
Purchase Amount
Method 1
The typical method starts sorting customers based on recency, from highest to lowest (more recent purchases at the top). Customers are then split into quintiles and given the first quintile a score of 5, and so on. The, customers are sorted and scored by frequency and money/time (both from highest to lowest). Finally, all customers are ranked concatenating R, F and M scores.
Step 1: Calculate Recency, Frequency, and Monetary Values for Each Customer
•
Recency (R): Measure how many days ago each customer made their last purchase. The most recent purchase gets the lowest score. For example, if it’s currently October 1, and a customer last purchased on September 30, their Recency is 1 day. Frequency (F): Count how many purchases each customer has made during a specific period (e.g., over the last year).
•
Monetary (M): Calculate the total amount each customer has spent during that period; it also can be the average amount per purchase spent in a period.
Step 2: Rank Customers for Each RFM Metric
•
Rank customers based on each metric. You can either use quintiles (dividing the ranked customers into five equal groups), quartiles, deciles, etc., depending on how detailed you want the segmentation to be.
•
Assign a score from 1 to 5 (if using quintiles) for each RFM metric. The best performing customers (e.g., most recent, most frequent, highest spenders) receive the highest score (5), and the least performing receive the lowest score (1).
Step 3: Assign RFM Scores
•
Combine the individual R, F, and M scores into a single RFM score. This is often done by concatenating the scores into a three-digit number (e.g., 451, where 4 is the Recency score, 5 is the Frequency score, and 1 is the Monetary score).
Example
Suppose you have three customers with the following transaction data:
•
Customer A: Last purchased 10 days ago, has 15 transactions, total spend of $1500.
•
Customer B: Last purchased 20 days ago, has 5 transactions, total spend of $500.
•
Customer C: Last purchased 5 days ago, has 10 transactions, total spend of $1200.
After ranking these customers within your data set and assuming quintiles for simplicity in this example:
•
Recency: C(5), A(4), B(2) – fewer days ago is better.
•
Frequency: A(5), C(4), B(2) – more transactions are better.
•
Monetary: A(5), C(4), B(1) – higher spend is better.
RFM Scores would then be:
•
Customer A: 545
•
Customer B: 221
•
Customer C: 454
These scores help identify which customers need attention and which should be targeted for retention, up-selling, or re-engagement campaigns. Method 2
Another method gives different points or different weights to R, F and M/T and assigns a score to each customer as the sum the points for each customer. Then, customers are ranked by descending score. The company now can target customers by score.
For example:
•
Recency: Last purchase in the last 3 months: 25 points; 4 to 12 months: 20 points; Did not purchase in the last 12 months: 0 points
•
Frequency (based on total purchases as recorded in the database): Has purchased a total of less than 10 books: 10 points; 11 to 20 books: 20 points; 21 o more books: 50 points
•
Monetary: Has purchased a total of less than $50: 10 points; between $51 and $150: 20 points; $151 or more: 50 points
Method 3
A third method involves the following: If we possess historical data on customer responses to past marketing activities, we can correlate these responses with R, F, and M codes. The procedure begins by splitting the historical data into two samples:
•
Calibration (also referred to as training or trial) is used to estimate the probability of response and its relationship with RFM scores.
•
Validation (also known as hold-out, testing, or prediction).
The method then calculates the probability of response as a function of the three RFM codes using the calibration sample. This model is subsequently validated with the validation sample. Utilizing this model, the company can target specific groups for a promotional campaign.
For example, a catalog retailer with 400.000 customers, sending a mail with a promotion to all customers would maximize sales but not profits, as many customers might not respond. It would be more profitable to send the mail only to customers with a higher likelihood of responding. The company could conduct a pilot study with a sample size of 40.000 customers, sending them a $150 discount coupon. The response rate observed was 808 customers (2,02%), but some responded more than others: response rates for R codes 5, 4, 3, 2, and 1 were 4,50%, 2,80%, 1,50%, 1,05%, and 0,25%; for F codes were 2,45%, 2,22%, 2,08%, 1,67%, and 1,68%; for M codes were 2,35%, 2,05%, 1,95%, 1,90%, and 1,85% respectively (Source: Kumar & Reinartz, 2006). Some limitations and extensions
Coding involves transforming continuous data into discrete data, which inevitably results in information loss. Additionally, RFM scoring is somewhat arbitrary. Another limitation is that the assumed relationship between R, F, or M and response may differ from reality, necessitating case-specific analysis. For instance, the relationship could be either positive or exhibit a U-shape due to the inventory effect.
Beyond the methods previously discussed, there are further extensions to consider:
•
RFM as Classical Linear Regression: In this model, the dependent variable is customer response (0 or 1), and the independent variables are the customers' original recency, frequency, and monetary values before coding. This model requires the estimation of only four parameters. With this analysis we can estimate the real relationship between positive response and recency, frequency and money spent.
•
RFM as Logistic Regression: This is similar to classical regression but better accommodates the discrete nature of the dependent variable.
We will now demonstrate a few examples of how to compute RFM scores.
Example: Empowering Change15
Empowering Change, a Non-Governmental Organization (NGO), aims to raise funds for its impactful initiatives. They have a vast database containing historical transactions from thousands of donors. However, targeting every donor might not be the most efficient strategy. To optimize their fundraising efforts, Empowering Change wants to leverage the RFM method to segment their donors based on their donor value. A subsample of the database is given in Table 1. Reference date for recency: 31/12/2006
To calculate the RFM score for each donor we need transaction data which should include the following:
•
a unique customer id
•
date of transaction/order
•
transaction/order amount
The segmentation starts with recency, then frequency, and finally monetary value. We start sorting customers based on recency, i.e. days after last purchase, in increasing order (most recent purchasers at the top). Customers are then split into quintiles (five equal groups) and given the top 20% a recency score of 5, the next 20% a score of 4, and the rest 3, 2 and 1. Then, customers are sorted and scored for frequency – from the most to least frequent, coding the top 20% as 5, and so on. The process continues for money or time as well. Finally, all customers are ranked by concatenating R, F, and M values. This example shows that RFM analysis can be useful even if the database is small of only 15 transactions whereas it would be more powerful when the database grows.
RFM analysis assigns scores to each customer based on her past behavior. Using the quintile system explained above, 125 different scores (5x5x5) can be assigned. A customer score can range from 555 (highest), to 111 (lowest). Usually the best customers are in quintile 5 for each factor (555), meaning she has purchased most recently, most frequently and has spent the most money or time. Depending on the objective of the targeting strategy, different donors should be targeted. For example, donor 03 which has RFM score 144, has made a high number of donations with high monetary values but not for a long time. Something might have gone wrong with this donor, for example, she has switched to another NGO or has stopped donating. At this situation, marketers can contact this donor and get feedback about how to do it better. Moreover, it is possible to plan a customer reactivation program to get her attention.
Donors 06 and 01 could be targeted to raise the highest amount of money needed for an emergency. can you summarize it for exam purpose with the main idea the most important concept which might be tested in the exam