Think about a percentage of your existing customers is actually being inactive and another part was lost. Do you know how much big is this part into your database?
Sending a message tailored to the customer group will generate much higher conversions.
Isn’t that right? Isn’t that should be?
All marketing campaigns should pick up a target segment first and then create promotional materials that will resonate with that audience.
RFM analysis is a handy method to find your best customers, understand their behavior, and then run targeted email/marketing campaigns to increase sales, satisfaction, and customer lifetime value.
Customers / User segmentation isn’t something that is alien in the marketing world. The big brands have this down to a T, and the little guys are just waking up to the power behind having a laser-focused strategy – laser-focused on user segmentation.
– Neil Patel on how user segmentation works in content marketing
How RFM analysis becomes useful?
As you may have already understood, the RFM matrix serves precisely to identify the potential hidden in our customer database.
RFM makes identifying customer groups easy.
RFM considers recency, frequency, and monetary values for each customer. Combines them, and then groups them into different customer segments for easy recall and campaign targeting. RFM analysis is super useful in understanding the responsiveness of your customers and for segmentation driven database marketing.
What are the questions that RFM answers?
- Who are my best customers?
- Which customers are on the verge of churning?
- Who has the potential to be converted into more profitable customers?
- Who are lost customers that you don’t need to pay much attention to?
- Which customers you must retain?
- Who are your loyal customers?
- Which group of customers is most likely to respond to your current campaign?
RFM is a scientifically proven process
This concept was originally introduced by Bult and Wansbeek in 1995. It was used effectively by catalog marketers to minimize their printing and shipping costs while maximizing returns.
It’s based on the Pareto Principle: 80% of the results come from 20% of the causes. Similarly, 20% of customers contribute to 80% of your total revenue. People who spent once are more likely to spend again. People who make big-ticket purchases are more likely to repeat them.
Pareto Principle is at the core of the RFM model. Focusing your efforts on critical segments of customers is likely to give you a much higher return on investment!
RFM Value Calculations
Wondering how to calculate RFM scores for your customer database? It’s really simple if you follow these steps. We need a few details of each customer:
- Customer ID / Email
- Recency (R) as days since last purchase: How many days ago was their last purchase? The most recent purchase date from today to calculate the recency value (in days).
- Frequency (F) as the total number of transactions: How many times has the customer purchased from our store? For example, if someone placed 5 orders over a while, their frequency is 5.
- Monetary (M) as total money spent: How much money has a customer spent? Simply total up the money from all transactions to get the M value.
The table below is an example of what you should obtain after all calculations:
The calculation is based on a time range, I suggest a minimum of 3 months, but it can be calculated for a longer time range too or many different slots. For example, it’s interesting to calculate the RFM matrix and compare the results for the previous 3 or 6 months.
RFM Score Calculations
Once we have RFM values, we assign a score from one to five to recency, frequency, and monetary values individually for each customer. Five is the highest value, and one is the lowest value.
RFM values and RFM scores are different. Value is the actual value of R/F/M for that customer, while Score is a number from 1–5 based on the value.
To calculate the score, we first sort values in descending order (from highest to lowest) and set the scores. The most recent purchases are considered better and hence assigned a higher score.
Two methods to calculate the scores on a scale of 1–5 points.
Different businesses may use different methods of RFM formulas for ranking the RFM values on a scale of 1 to 5.
1 – Fixed ranges
Example: If someone bought within the last 24 hours, assign them 5. In the last 3 days, score them 4. Assign 3 if they bought within the current month, 2 for the last six months, and 1 for everyone else.
The scale can be adapted to the individual business since they decide what range they consider ideal. As the business grows, score ranges may need frequent adjustments.
If there are recurring payments but with different payment terms — monthly, annual, etc — the calculations can go wrong.
2 – Quantiles
It consists to make five equal parts based on available values, dividing by in 5 equal parts.
If we take 100 customers and we make five equal ranges of percentile, the score of 18 will fall in the 0–20 range, which would be the 1st quintile. A percentile value 51 will fall in the 50–75 range, and hence 4th quintile.
It remains the recommended method to calculate the scores because it solves a lot of problems in fixed range methods and it works with any industry since ranges are picked from data itself, they distribute customers evenly and do not have cross overs.
Example of RFM calculation based on fixed ranges
In this table, you can see how to set the scoring for Recency.
In this second table we set the score for Frequency.
The last one, we set the score for Monetary.
Now, we can put all togheter and group by scores identifying who purchased recently, are frequent buyers and spend a lot are assigned score of 555 – Recency(R) – 5, Frequency(F) – 5, Monetary(M) – 5.
Understanding 50 customer segments can still be hard, we can summarize the analysis into 11 customer segments in the following table.
|Customer Segment||Recency Score Range||Frequency & Monetary Combined Score Range|
|Customers Needing Attention||2-3||2-3|
|About To Sleep||2-3||0-2|
|Can’t Lose Them||0-1||4-5|
The minimum analysis time should be at least 30 days. The more data, the better. Each user can determine the duration of the RFM model analysis based on its customer acquisition, activation and retention periods.
The RFM duration should cover all customer life cycle stages as much as possible. For retail and e-commerce, a three-month period is the industry standard for RFM model analysis.
– Articles Series
I published an article series based on RFM matrix to explain different applicable actionable tactics to grow your business.
In case you missed something, the series includes the following articles: