There are many ways to achieve this. For us, one of the popular ones is RFM analysis. And why RFM analysis?
RFM analysis (Recency, Frequency, Monetary) is a popular customer segmentation technique that allows companies to focus on valuable customers and increase their loyalty. Here are some statistics that illustrate how RFM analysis supports e-commerce companies:
- 30–40% of companies using RFM analysis report a higher return on investment in marketing campaigns thanks to targeting different customer segments with tailored offers. An RFM-based implementation allows e-shops to reduce customer acquisition costs by up to 20%, because they focus on retaining existing customers. (McKinsey & Company. "The Economic Impact of Customer Retention M Analysis." 2022)
- E-shops that use RFM analysis regularly see a 10–15% increase in customer retention thanks to more effective customer retention strategies. Companies working with RFM segments have a 25% lower churn rate (the rate at which customers leave), which increases customer lifetime value (LTV). (Harvard Business Review. "Improving Customer Retention with RFM Analysis." 2021)
- Regularly sending targeted offers to customers with a high "Recency" value leads to a 25% increase in the average purchase frequency of these customers. In the segment with the highest "Frequency", e-shops see up to a 40% higher conversion rate when sending personalised offers. (Bain & Company. "Customer Frequency and Recency Influence in E-Commerce." 2023)
- Companies that use RFM analysis to identify loyal customers generate on average 30–50% of their revenue from these customers, targeting them with personalised campaigns. (Accenture. "Impact of Customer Loyalty Programs with RFM Segmentation." 2021)
- 40% of e-shops working with RFM analysis are better able to tailor their product offer to individual customer segments, which leads to lower storage costs and higher product conversion. (Gartner. "Strategic Value of RFM Analysis in E-commerce." 2022)
What Do You Need to Carry Out RFM Analysis?
You need data. As a rule, this is data:
➡️ from the website (Google Analytics)
➡️ order data
➡️ customer data
➡️ product data
It is best to have data for at least the last 6 months, but data a year old or an even longer time series is ideal. You also need an infrastructure in place that allows the data to be calculated automatically and RFM analysis to be used without further intervention. This is where we come in.
First of all we set up a data warehouse (most often BigQuery), where all the data from the sources you have available and that are relevant is stored. We then process the data and produce the analysis itself. We code, explore and obtain useful insights. After that we create a dashboard in Looker Studio or in Power BI, or Tableau. Then we teach you and your team to work with the analysis and to interpret its results, so that you achieve the goals you want.
How Demanding Is the Implementation?
The implementation itself takes a matter of weeks. As a rule we can produce the first version of the analysis within 6 weeks (if you have the required data available). The data is then interpreted and the team learns to work with it. You can make the first changes based on the analysis already in the first month after it is created. Settled work with the whole concept then depends on the activity of your marketing team.
Who Works with RFM Analysis and What Does It Look Like in Practice?
The most frequent users are marketing teams, who adjust campaigns and evaluate their contribution, the reduction of customer churn and more. The analysis is then also used by the company's management.
The work consists mainly of understanding the individual customer segments and their needs. The output of the analysis is a division of customers into groups with specific needs. Some of the groups may be price-sensitive, others not. Some customers may be so-called seasonal, and some may fall into an at-risk group that is at risk of not buying a second time. The marketer must understand these segments and work with them effectively. They may offer one group a discount, another a free product, send a personalised thank-you to a third and anything else. That depends on the marketer's creativity and experience.
The result of this modification is more precise campaign targeting, which leads to higher profits at lower costs. There is also a lower customer churn rate and an increase in the number of repeat purchases.
What Are the Costs of and Investment in RFM Analysis?
With RFM analysis we are talking about three kinds of investment.
The first is the creation of the RFM analysis itself. Here we are talking about tens to hundreds of thousands of crowns (CZK), depending on the size of your e-shop, customer base and product base. This investment is one-off and concerns only the creation of the whole concept, which also includes the creation of a data warehouse and data processing, if needed.
Next come the costs of running the solution. These include the costs of running the data warehouse, the costs of running the visualisation tool and also the costs of data processing. It depends on the volume of data, but some clients are in the order of hundreds of crowns a month, others in the order of low thousands of crowns a month.
Finally, there are the costs of the groups.
How We Carried Out RFM Analysis and What It Brought
"Thanks to RFM analysis we finally understood who our customers are and we can target them in a personalised way. Especially in the second half of 2024 it was our main marketing focus and we gradually redesigned all campaigns so that they reflect the RFM segments. We can already see that this activity across the whole of marketing has brought 10% higher profits than blanket outreach," says Petr Voves Jr., owner of the e-shop Ochutnej ořech.