RFM Analysis: Calculation, RFM Segmentation & How it Works
What Does RFM Mean?
RFM stands for recency, frequency, and monetary value.
Recency: How recently a customer has shopped with you.
Frequency: How frequently a customer has shopped with you during a given period.
Monetary value: The average purchase value or cumulative value of a customers purchases during a given period.
Recency, as explained, is how recently a customer has shopped with you. There are industries in which this is more or less important than others, along with situations in which it's not relevant at all. For example, new customers could have a high recency score, but may not be very likely to continue shopping with you in reality. This can be counteracted by weighting the respective factors of recency, frequency, and monetary value differently within your analysis.
The frequency score that somebody receives is based on how many times they made purchases within a given period. Frequency will be more valuable to certain businesses than others - for example, luxury watch dealers will be far less concerned with frequency than bakers. The period in question will also vary based on the industry, with timeframes for car companies being closer to 20+ years, while the timeframe could be a matter of weeks or months if the business in question is a coffee shop, for example.
Monetary value is exactly what it says on the label. How much revenue or monetary value your customer contributes to your business within a period of time. It's similar to other measures such as customer lifetime value or average order value, which are also very important metrics to be aware of. There are very few businesses that would choose to value recency and frequency above monetary value, but some exceptions include subscription models such as Netflix or Amazon Prime Video.
What is RFM analysis?
RFM analysis measures the likelihood of a customer shopping with you again in the near future, based on how recently they have shopped with you, along with their purchase frequency and average purchase value. These 3 metrics are compiled to create an RFM score for each customer, which represents the likely course of future customer behavior.
What is RFM analysis used for?
RFM analysis is used for predicting future customer behavior, which in turn helps companies with forecasting demand for coming periods. However, RFM analysis is not only good for forecasting demand or predicting customer behavior in the next couple of quarters. RFM analysis gives valuable insights into customer value, along with how profitable targeting certain customer segments in coming periods may be.
RFM analysis, more specifically the RFM score, can be used for many things. First of all, it accounts for customer lifetime value in a certain sense, which is a huge indicator of whether or not a company should continue to pursue a customer or not. Particularly when partnered with customer lifetime value analysis, RFM analysis can give an extremely good gauge of whether or not pursuing a given customer would likely be profitable - allowing you to identify who your most valuable customers are.
Understanding the Components of an RFM Score
After an RFM analysis is completed, each customer receives a score that is the sum of their 'ratings' on the respective variables of recency, frequency, and monetary value. Understanding what the variables stand for is essential to enjoy the full degree of the insights that RFM analysis can reveal. For example, a certain customer may have a poor score because they rank highly in terms of monetary value, but not in terms of frequency or recency. While the score for this customer may not be breathtaking, it in fact could be a (potentially) extremely high-value customer that simply purchased once and never received any nurturing thereafter.
Not only this, but it can also be that a customer with an average - or even slightly above average - score is indeed a barnacle, meaning that they shop frequently and have a high degree of loyalty, but their actual monetary value for the business is very low. Therefore, understanding how exactly the RFM score is arrived at is essential.
For more information on what it means to have customers with high levels of loyalty but low levels of profitability here, in an article that discusses customer relationship groups and what the respective groups can mean for your business. It shows that frequency is not necessarily always a good thing and that chasing customers with a high monetary value isn't always the most profitable tactic.
Customer Segmentation with RFM Analysis
Using RFM analysis to subsequently employ RFM-based customer segmentation is a method that is widely used by many businesses with great effect. The reason why RFM segmentation is so effective is that it's based on the RFM scores that customers receive, making the scale much more easily definable and clearly segmented than can often be the case with other measures of customer value.
Below are some rudimentary examples of how customers may score in RFM analysis, along with what this score may mean if you look at it on a level beyond the three variables at face value.
When you look at the different profiles here, you can see that certain customers have seemingly different and seemingly similar profiles. So, what are the commonalities, the differences, and the things that matter most? This is where understanding the inputs or components of RFM analysis comes in handy.
If you look at the last two customer profiles, for example, you can see that the only difference between the two profiles is the recency with which the customers have purchased. This is extremely relevant, as you can understand that this customer may be highly profitable, but may have already passed through the window within which you can capture them and retain their business - this is evident with the second last profile, as the customer is highly profitable but likely not worth chasing as the lack of recency will make them less likely to convert.
On the other hand, you can look at the last customer profile and see that the addition of more recent purchases gives only a marginally higher RFM score, but the difference between your likelihood in nurturing this customer is significantly different. This relates back to the concept of customer relationship groups, where you can also have customers with an extremely high level of loyalty, but little profitability for the business due to their high maintenance levels.
Let's take the third customer profile, for instance. This customer will likely have a strong RFM score, but further inspection reveals the lack of benefit that serving this customer comes with. In the context of customer relationship groups, these customers would be referred to as barnacles due to their high levels of loyalty (maintenance) and low levels of profitability.
When employing RFM analysis in order to segment your customers, it's best to segment based on the respective variables. RFM analysis is typically done on scales of 3 or 5, meaning that you can categorize your customers easily into groups of low, medium, or high recency, frequency and monetary values. Whether measuring on a 3 or 5 degree scale, you'll be left with customers that rank from low to medium to high, which gives you 27 (minimum) ultimate combinations of R, F & M.
With 27 possible combinations, chances you'll have at least 27 distinct customer segments. In understanding the makeup of the scores, along with what works for your business, you can then create larger segments that have very specific targeting tactics.
Wondering how you should target certain customers based on their RFM attributes? You can look at frequency and recency as a measure of customer loyalty in a certain sense here, which ultimately means more servicing for this customer. Subsequently, you can then determine whether or not their monetary value is worth investing in, and whether or not you see potential for it to be increased.
Limitations of RFM Analysis
When using RFM analysis, you should be aware of the fact that it is purely a prediction of the likelihood that existing customers will continue to purchase with you in the future periods. It does not account for customer satisfaction or your average customer retention rate, for example.
One key limitation of RFM analysis is the fact that it is based entirely on historical purchasing data and does not rely on any other customer attributes in order to predict potential future purchases. Factors such as the demographic information of the customer, along with the channels through which they shop, for example, are also not assessed in RFM analysis. In order to market towards your customers in a more customized and effective way, you should include and measure the factors or attributes you'd like to consider if you were to launch a personalized marketing strategy.
Weighting Factors within RFM Analysis
Within your analysis, you have the option to weight different factors differently. In other words, you can give these factors more priority or more value than the other factors. For example, if you deal with luxury goods such as watches, recency may not be as important to you as monetary value or frequency.
In certain companies, the number of segments derived from the analysis is sometimes reduced purely by getting rid of one of the factors entirely - namely recency, frequency, or monetary value. In fact, many companies have worked to make unique adaptations to the RFM scale to fit their business. If you look at subscription models such as Spotify or Amazon Prime Video, their revenue per customer is quite constant. As such, it makes more sense for companies like Amazon Prime Video to measure session length, for example, as opposed to measuring monetary value. However, there will be certain segments that are represented favorably as a result of their session length, in which case you can employ weighting to offset these effects.
Operationalizing RFM Insights
When it comes to actually putting the insights you've uncovered to work, there are a few things that you need to have in place first. If you look at customers that have the potential to be profitable if their frequency score is increased, such as new customers, at-risk customers, or customers that are "hibernating", the first thing that comes to mind is the fact that these customers need to be nurtured and activated. In order to do this, you need to have content in place, along with an accompanying flow that guides customers through an ideal journey.
When it comes to nurturing customers, you have to engage them with content. What's important to note is that the content you use should be relevant for them - even better if you can customize or personalize it in some sense. Beyond providing engaging and relevant content, you need to watch your timing. No, not the time of day. The F of your content and messages - frequency.
Many companies - their marketers in particular - think that sending customers as many emails as possible is the best way to engage them and convince them to purchase further. In fact, loyal customers aren't born from incessant emailing. To make customers interested in your brand and products, you have to be interesting, strangely enough.
Don't think that you only have one chance to capture a loyal customer. Your past, current, and future customers will all encounter your brand multiple times before their first, hundredth, or final purchase - provided you're doing your part to increase or maintain brand awareness. Exposure isn't something that you have to limit, but it's a matter of quality over quantity when it comes to email flows or other customer communications.
Craft template emails that offer customers a reason or incentive to choose you, sure. But don't forget that relevant content based on what those people really love is much more likely to convert. Cover some of your main customer profiles - particularly the target ones - and cover some scenarios. This gives you the chance to come across more personally and to have more tailored content. This is great when you have customer data, but also in scenarios where you're trying to catch repeat customers.
In conclusion, RFM analysis is a tried and tested analysis technique that has stood the test of time. Not only that, but RFM analysis is incredibly flexible. The principles can be adjusted quite easily in order to suit different business models, and this tactic has been employed widely by large corporations. RFM analysis gives invaluable forecasting capabilities to companies of all sizes and industries, which allows them to effectively target customers with a reliable prediction of their potential ROI.
As a result of RFM analysis, you'll be able to look into customer segments based on value, identifying commonalities among customers that contribute significantly to your business. Once you've identified the customers that are most valuable, take a look back at how they were treated and what stands out. Finally, remember that not every RFM score that glitters is gold. Employ weighting within your analysis where necessary and don't forget that a high RFM score isn't always a reason to try and sell - it could be more profitable to get to know the customer first.