Do you know who your best customers are? Finally, find out with RFM Analysis! A valuable tool to understand your customer base and how to adapt your marketing strategy to different buyers.
Imagine you had to sell charity tickets to your neighborhood’s beneficent bazaar this Christmas. Would you approach a close friend the same way you would approach an ex-coworker you haven’t seen in a long time?
Finding the best people to contact would be a good start to sell those tickets. The approach – your communication and strategy – used would most likely be different too.
In a business setting, we are faced with a similar question: how do different customers behave and relate to your business, and how to better engage with them?
Who are your best customers? How can you strategically plan your approach and content for different consumer types? How can you reach each one with the right message at the right time? What is the overall strength of your client base and its trend?
That is the magic of RFM (Recency, Frequency, Monetary value) analysis & segmentation. This marketing technique seeks to identify each customer’s profitability and segment them from best or most profitable to least profitable.
This is done by classifying each customer according to:
- Recency: how long since their last purchase?
- Frequency: how frequently have they shopped in a certain period (number of purchases in a specified time frame, for example)?
- Monetary Value: how much they spent in that period?
In this analysis, each of these three parameters is ranked from 1 to 5 (5 being the best grade possible). Together these three scores are known as an RFM cell.
Buyers in your database are sorted into groups according to their “grades” or “scores” (the “best customers” would be those in the group or cell “555” and “least ideal” ones in the “111”). You can see an example of segmentation by RFM in the chart below.
Champions would be the best and most loyal customers, spending a lot more than the rest of your customer base, and frequently. They can even behave as promoters of your brand as well. A special approach is highly recommended. Even if they are not your largest customer group, keeping them engaged can help your brand in more ways than bringing in revenue.
The red and orange segments here are individuals that were good purchasers in the past but are not engaging with your company lately and therefore are at a high risk of churn. Being attentive and employing a special strategy and communication is also essential here (if you desire to regain them as frequent solid customers).
With this in place, you can use the RFM segments as a starting point to understand your customer motivations better.
A good way to dig in deeper is enriching your insights with more qualitative data. Enriching your segments allows you to create sub-segments for each RFM group. For example, tagging each consumer in your RFM clusters with current Net Promoter Scores (NPS) or qualitative survey data (transformed into categorical variables).
Collecting survey information, NPS, demographics, and other important data and mixing with RFM modeling allows you to find what drives your customers to purchase or to leave. It also helps to define the best potential customers in the future and estimate their Lifetime Value (CLV).
To have an idea of how this additional information is useful, consider the following situation: you have two great ex-customers that haven’t bought your product in a while. They could be in the same RFM segment right now, but for different reasons. Imagine that one of them lost his job while the second one is unhappy with your customer service or had constant past delivery issues. Would you communicate with them in the same way? Or would your strategy be different?
A possible solution is engaging them as different sub-segments:
- 1st sub-group – customers experiencing income issues (happening a lot around the globe with current COVID measures).
You could implement a mailing and marketing automation strategy focused on keeping your company at the top of their minds while they navigate this crisis.
When the economical situation normalizes or they are employed again, they are still engaged with your brand and more likely to return as paying customers.
- 2nd sub-group – churned consumers unsatisfied with service.
Reestablishing their trust and convincing them that your company has improved can transform your relationship. Possibly even turning unsatisfied customers into your “top fans” in the future.
This understanding of your customer base and segmentation allows you to be more efficient when trying to achieve your key goals and using your resources.
If your main goal for this quarter is reducing churn, for example, you would know which customers to focus on: putting more effort on the “churned past best customers segment” (RFM 155 for example) can become important. If, on the other hand, your main KPI involves upselling, your efforts should be in more appropriate segments in that period (such as loyal consumers that spend frequently and recently but spend small amounts, for example).
This year, with lockdown measures in place, new spenders (including shoppers that are transitioning from offline to online purchases for the first time) might also be another interesting segment to investigate and try to build a long-term relationship with.
Enriched data (i.e. additional transaction data or categorical data from surveys, etc.) can help you understand how your consumer’s behaviors are changing and evolving as this new situation unfolds, and adapt as needed.
Summing up, RFM shines as a tool when you are looking to:
- Understand your customer base;
- Know who your typical best customer is, and are searching for future customers with similar characteristics;
- Find the best upselling opportunities;
- Allocate your sales team efforts efficiently, for better results;
- Communicate successfully with your customers;
- Understand patterns regarding how customers migrate from cell to cell over time (trends in how customers jumped across segments);
- Estimate or predict CLVs.
There are many ways you can do an RFM analysis and segmentation. With specific RFM software or SaaS applications or by creating your own method, using R scripts for example (R is a statistical open source programming language & software environment).
As we have discussed, sorting your customers into segments or groups can be as simple as following the usual RFM methodology or going into very detailed clusters, i.e. micro-segments. It all depends on what you want to achieve.
RFM has its limitations, of course, but using data to satisfy your customer base in a personalized way, estimate their CLV and plan your marketing efforts in tune with your goals is, in my view, a great feat.
Do you want to get hands-on tips and insights on the RFM analysis from a data scientist?
Register for our RFM workshop and learn about one of the most popular, easy-to-use, and effective segmentation methods to enable marketers to analyze customer behavior.