Author: Philip Godfrey
What is RFM analysis?
Marketers typically have extensive data on their existing customers – such as purchase history, browsing history, prior campaign response patterns and demographics – that can be used to identify specific groups of customers that can be addressed with offers very relevant to each.
This is where RFM analysis, or customer segmentation, can help us.
RFM is a method used to analyse customer value. RFM stands for, Recency Frequency, and Monetary.
- Recency (R): How recently customers have made their purchases
- Frequency (F): How often customers have made their purchases
- Monetary (M): How much money customers have paid in total for their purchases
Why should I use RFM analysis?
Consider the two customers below – based on this information alone, who would you say is your most valued customer?
You would probably say that Customer 1 is your most valued customer?
If we add in some additional information, such as the number of purchases made in the last year, or when the last purchase was made, we start to see a different picture.
Now we can see that Customer 2 is likely a more valued customer, although they have spent less in the previous year, they have visited the business recently, and have made four purchases over that time period.
What business questions can RFM answer?
The RFM model is built on transactions between the user and the business, to create a robust data-backed method based on hard numbers. This customer data is graded, further analysed, and then segmented to engage customers as distinct groups.
This model helps businesses effectively analyse the past buying behaviour of each customer, to predict and shape future customer interactions.
The RFM model helps business get answers to highly specific questions such as:
- Who are my best customers?
- Which customer has the potential to buy more?
- Which customer has been churned out/has lapsed?
- Which customer can the business afford to ignore to effectively utilize budgets?
- Which customer can be converted by creating value through promotions?
- Which customer is likely to be loyal in the near future?
While there are countless ways to perform segmentation, RFM analysis is popular for three reasons:
- It utilizes objective, numerical scales that yield a concise and informative high-level depiction of customers.
- It is simple – marketers can use it effectively without the need for data scientists or sophisticated software.
- It is intuitive – the output of this segmentation method is easy to understand and interpret.
Limitations of RFM Analysis
While RFM analysis is a valuable approach for customer segmentation and analysis, it does come with some limitations.
- Only three dimensions of a customer are considered (Recency, Frequency and Monetary) and may not capture all aspects of customer behaviour, such as product preferences or engagement level.
- Based on historic information and doesn't account for changes in customer behaviour over time and lacks predictive power.
- Analysis relies on predefined metrics and may not be suitable for industries with unique customer dynamics or complex purchasing patterns.
Interested to know more?
If you would like to know how you can perform RFM analysis, keep an eye out for the next blog, which will take you through all the steps involved, including:
- preparing data
- identifying and labelling customer segments
- reviewing your customer base
- providing actionable insights
Comments
Post a Comment