What is RFM Analysis and Why is it so important
Since I started to work as data-marketing consultant, one of the goal was to handle the heavy digital processing work, so that internal marketing teams have time to breathe, and focus on creating relevant strategies for each customer groups.
The question I asked myself was: "how to design a segmentation that saves time, is easy to use, and has instant access to the people hidden in the data?"
Not long ago, I realized the benefits of RFM analysis-one of the most powerful indicators of customer retention. This is why I integrated it on every consultancy program, driving great results for my cliens.
I got extraordinary results since I'm using it, x3, x4 and x5 increasing in sales and revenues used as base analysis to drive the marketing strategies.
What is RFM (Recency, Frequency, Monetary)?
RFM segmentation is a method to identify the most important types of customers grouping by scores based on their recency, frequency and monetary values. The purpose is to predict which customers are more likely to buy again in the near future.
Knowing this "customer features" allow companies to target specific customer groups based on their behaviors.
Thereby using wisely these insights you can generate an higher response rates, increased loyalty, and better customer lifetime value.
How can RFM help you?
- Delivering targeted communication that resonates better
- Best channel that works for the segment
- Enables you to deepen your relationship with your customers
- You can try and test your pricing options
- Focussing on most profittable customer segments
- Upsell and cross-sell products for each specific segment
Over the past years, I have been building Customer Retention strategies for various types of e-commerce. In a nutshell, by knowing how recently a customer bought from you, how many orders he placed, and the total value of those orders.
Interesting data that you can include for each persona is information regarding:
- Persona X is interested mainly in product X but also buys product Y
- Persona X, is there a difference in behaviour between new and returning Persona X.
- Persona X visits mainly these pages but also visits these other pages.
- Use the Behaviour Flow report in Google Analytics to see where Persona X lands and
where they go after to answer these questions:
– Where are they leaving the site?
– Are they finding what they are looking for?
Testing, Testing and More Testing
Now you have data that can help you create different personas, but the changes on the test site are always important. It may not be a good idea to completely change the structure and navigation of your website, because returning visitors may be confused and unable to find the products or information they have been looking for on your website.
Progressive changes may be better, and always use A/B or multivariate testing to test the hypotheses to ensure that these changes will be positive, and users will actually be better involved in new changes or making new conversions.
Now you have data that can help you create different Customer Segments, and exploring the data using Datastudio, you may find some UX issues for Segment X or something about Cart or Checkout Funnel that it doens't works well for some other Segments.
However, the same design does not apply to the same Customer Segment and you should use Recommendation tools in order to provide a better Customer Experience for them.
Building a website for multiple personas is challenging, which is why testing and evaluation is essential to ensure that the target audience participates and becomes a customer.