A key way to make your marketing effective is to try to understand the "value" of your individual customer. Termed 'Customer Lifetime Value (CLV)', there are many models that measure this metric. One of the models is the RFM model.
Developing a comprehensive and practical model of customer profitability requires answering the question: Who are my most profitable customers? Is the RFM Model and RFM analysis relevant even today?
One of the models that has been in use for years to segment your customers to calculate their CLV is the Recency, Frequency, Monetary (RFM) Analysis statistical model.
Using it for customer segmentation provides a picture of the past, showing what your customers were like, and serves as a good indicator of what your future goals should be.
What is RFM Analysis?
RFM analysis is based on the Pareto principle, AKA the 80/20 rule. The rule states that '80% of the business comes from 20% of the clients'.
Therefore, understanding who that 20% of your clients are is essential, right?
RFM helps categorize customers into clusters to identify those who are more likely to respond to promotions, as well as to upsell or cross-sell.
Interested in knowing how the RFM Analysis will work for your business? Click here, and our experts will get in touch with you.
How RFM Analysis Can Help You Maximize Customer Lifetime Value
If you're looking to improve your business's bottom line, one of the best places to start is by increasing customer lifetime value.
One of the most effective ways to achieve this is through RFM analysis. RFM stands for "recency, frequency, and monetary."
In other words, it's a way of measuring how recently a customer has made a purchase from you, how frequently they make purchases, and how much they spend.
By understanding these three factors, you can more effectively target your marketing efforts. You can also increase customer lifetime value.
RFM analysis is easy to do, and its results are invaluable.
There are a few things you need to do before you begin analyzing your customers:
- Decide which customers to analyze.
- Decide what your "recency" (latest) will be.
- Decide what your "frequency" (period of buying) will be.
- Decide what your "monetary" (money as a value) will be.
- Analyze your customers.
- Make some decisions based on your analysis.
How RFM Analysis Works (in a nutshell):
Recency: Refers to the last time someone purchased from your business. This means that a client who has made a recent purchase is more likely to repeat the purchase compared to one who hasn't purchased in a long time.
Frequency: Refers to the number of times a client has purchased in a given period. The logic here is that a customer who makes frequent purchases will likely return more often compared to one who rarely makes purchases.
Monetary Value: Refers to the amount a client has spent in the same period. Obviously, one who has bought more is expected to return more often than one who has not.
What is A Good RFM Score?
The RFM score shows the value you assign to every variable applied in the RFM analysis process; in short, the values of recency, frequency, and monetary.
The RFM score is a statistical measure that helps you identify various consumer types, ranging from the most valuable to the least useful.
A good RFM score indicates the likelihood of a customer making a purchase based on their past behavior.
A high RFM score indicates a higher likelihood of retaining the customer, as they are more likely to be loyal and return in the future.
A low RFM score indicates a lower likelihood of retaining the customer.
The RFM score formula is:
[Score of Recency x Weight of Recency] + [Score of Frequency x Weight of Frequency] + [Score of Monetary x Weight of Monetary]
To compute RFM scores, you first need the values of the attributes for each customer. Attributes may include:
- Last purchase date
- Number of transactions within the previous year
- Total sales attributed to the customer…. and so on.
Once this is done, you will have to assign a number for each RFM attribute. It can be anywhere between 1 and 5.
RFM analysis ranks each customer for each factor on a 1 to 5 scale (5 is highest). If you decide to code each RFM attribute into five categories, the highest score will be 555, and the lowest will be 111.
The three scores together form the RFM' cell' for each customer, ranking their historical propensity to buy, with a '555' customer ranking at the top and one with '111' at the lowest rung.
In this way, using RFM to segment customers, one can analyze each group to understand which one has the highest CLV.
But here's the thing: Not all customers are created equal.
RFM Model (RFM Analysis) may work for small and medium-scale enterprises because of its:
- Inherent simplicity
- Effectiveness in direct marketing campaigns
- Affordability
- DIY nature
However, if you are a large company with the necessary resources at your disposal, using RFM in conjunction with predictive analytics models is highly recommended.
Predictive analytics does a far better job of forecasting sales and offers better ROI segmentation based on RFM. However, this is a costly method, and not everyone can afford it.
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Role of RFM Model in Customer Retention
The RFM model is a well-known customer retention tool that businesses use to segment and target their customers.
The RFM model is a predictive tool that forecasts the likelihood of a customer making a future purchase.
It is also a customer retention tool, used to segment and target your customers.
With its help, you can distinguish between customers who are not interested in your product and those who will be interested in it.
How can I Improve My RFM Model?
While there is no definitive answer to this question, as it depends on the specific circumstances of your model and business, there are some obvious ways in which you might improve your RFM model.
These include ensuring that your data is of high quality, accurately reflecting customer behavior; using a robust method for calculating recency, frequency, and monetary value; and regularly testing and refining your model to optimize its performance.
What You Must Watch Out for in the RFM Model
You must be aware of the following:
There's always the impulse to target customers with the highest rankings, but that would be wrong.
Here's why: It's likely that customers who generate the highest revenue may be casual. This information gives a clear signal that you should focus more on this group.
There's also the instinct to ignore customers with low scores, and that, too, would be erroneous.
To avoid over-solicitation of high-ranking customers, as it could lead to resentment, making them flee from your business.
However, the most significant issue with the RFM model is the assumption that your best buyers will also be the most responsive to your marketing campaigns.
Yes, historical behavior does provide a roadmap for the future, but it's not truly predictive.
As Anusha Acharya writes, this assumption overlooks the possibility that customer behavior may change over time or has already changed.
RFM Models: Use Cases/Examples
RFM models are commonly used in marketing to identify the most valuable customers, allowing for targeted campaigns.
A successful RFM campaign targets products to consumers who have indicated an interest in those products (that is, customers who have purchased those products or indicated they would be).
Note that the model also features filters to target only new customers, requiring highly targeted marketing campaigns.
In other words, campaigns that target new customers must be relevant to them.
RFM models can also be used for forecasting and planning purposes.
Take, for example, a company that manufactures mobile phones: one could use the RFM model to forecast the types of new models to introduce in the future, based on their "revenue lift."
Customer Segmentation Using RFM Analysis
Customer segmentation using RFM analysis is a process of dividing customers into groups based on their purchase frequency, recency, and spending behavior.
This segmentation can be used to target different marketing campaigns to other groups of customers.
For example, customers identified as high-value, who make frequent purchases, may be targeted with loyalty programs or special discounts.
Demographics analysis: Customer segmentation based on demographics, such as age, gender, family status, and household income, is a handy tool for businesses.
The demographic information can be used to segment customers into groups of people who share the same demographic characteristics.
By collecting demographic information on a customer and their behavior, you can target the customer with more effective advertisements.
Customer segmentation based on demographics is the process of dividing customers into groups based on their demographic characteristics.
New Clients: Where's The Frequency Score?
Another issue for the RFM analysis is the inclusion of new clients. New clients often purchase inexpensive products solely to test the company's services, which skews the model.
However, the more concerning problem is – how do you attract new customers?
New customers have only made one purchase, so they can't have a "good" frequency count, even though they may perform well in the "Recency" and "Monetary" scores.
So how does one account for them? Remember, the 'F' in RFM stands for frequency, so that value cannot be derived for new clients.
To overcome this hurdle, the data science team at Express Analytics has developed a layer on top of the RFM Analysis to identify potential among new customers, which helps pinpoint those customers most likely to become high-profile ones in the future.
Evolution of the RFM Model
Since its inception over forty years ago, the RFM Analysis has undergone numerous evolutions.
Each iteration and variation involves incorporating new components to enhance the model's predictive ability.
Some examples of RFM analysis: Ya-Yueh Shih and Chung-Yuan Liu (2003) proposed two-hybrid methods that exploited a weighted RFM-based method (WRFM-based method) or the preference-based Collaborative Filtering (CF) method to improve the quality of product recommendations.
Their findings indicated that the proposed hybrid methods were superior to the other methods.
Rust and Verhoef (2005) provided a fully personalized model for optimizing multiple marketing interventions in the intermediate term.
This was achieved by conducting a longitudinal validation test to compare the model's performance with that of segmentation models used for predicting intermediate-term and customer-specific changes in gross profit.
This battery of models tested included demographic model, RFM model, and finite mixture models.
Their results show that the proposed model outperforms traditional models in predicting effectiveness in the intermediate term (CRM).
Start segmenting your customers with RFM today >>>> Talk to Our Experts
How RFM Analysis Helps Improve Business Understanding
RFM analysis is a marketing technique that uses customer purchase data to determine the value of each customer.
This information is then used to target customers with specific marketing messages.
RFM analysis is a powerful marketing tool that helps businesses gain a deeper understanding of their customers.
By analyzing customer purchase data, businesses can identify their most valuable customers and tailor marketing messages to target them effectively.
The purpose is to use customer purchase data to gain insights into customers' motivations and preferences.
By learning what customers buy, businesses can gain a deeper understanding of the types of products and services that interest them.
This can then be used to help businesses develop and implement more effective marketing campaigns.
Without a doubt, the RFM Analysis is a valuable marketing analysis and segmentation tool for many B2B businesses.
How well do you know your customers? Only when you genuinely understand them can you deliver the best customer experience. Our customer data platform, Oyster, helps you keep the Recency, Frequency, Monetary Value (RFM) score. With RFM analysis, your business can assess a customer's propensity to make a purchase.
References:
The ABCs of RFM – and How it Can Help You Retain Your Customers