Segmentation based on email dataIf you are a marketer for a retail company, you probably have one or more email campaigns running to engage your customers and tempt them to buy your products. But how do you determine which of your customers are the most engaged, and the most valuable? Is there an easy way to use customer data to segment your audience, so you can communicate with each group in a more personal way? There is, and it’s called an eRFM model.
What is an eRFM model?The classic RFM model is based on three values: Recency of purchase, Frequency of purchase and total Monetary value. Customers are scored on a 1-5 scale on each value, 1 being the lowest and 5 the highest. Combining the values leads to personal scores that give you an overview for all three measures in order (R – F – M). With these, you can evaluate your customers based on your business values. For example: 125, 251, 512 contain the same item scores (1, 2, 5), but on different values. If Monetary value is most important for your business, the first personal score (125) is preferred over the other two. However, with today’s email business, loyalty can be also defined within email activity. A simple adjustment to the values of the RFM leads to the eRFM. The eRFM model focuses on email engagement; Recency of last email activity, Frequency of email activity and total Monetary value (conversion). In this case Monetary value is the same for the eRFM and RFM. eRFM scores will give you a lot of information about your customers. Calculating these scores over time gives you the ability to see changes occurring within your customer base.
Use caseseRFM scores will give you a lot of information about your customers. Calculating these scores over time gives you the ability to see changes occurring within your customer base. Customers with a high Recency and Frequency, but low Monetary value appear to be interested in your business, but have not yet found the motivation to buy. Records with high eRFM, but low RFM are interested in your products, but not buying for now. You’ll need to find a way to motivate those customers to convert. An eRFM model will help you get to know your customers. This helps you create more effective marketing campaigns and communicate in a way that fits their needs and expectations. Use the eRFM scores to segment your database and address specific customers in targeted campaigns. If a group of records shows a change in their score after a campaign, this tells you much about its effectiveness. Another way you can use these insights is for creating targeted campaigns. They will turn your less engaged customers more engaged and keep your most engaged customers interested. For a retailer in household goods with a large number of physical stores as well as an online store, CloseContact devised a data model. Because we had access to online purchases only, we used their email data to produce an eRFM. After our analysis, they decided to market in-store discounts for those low RFM/high eRFM records.
TIPS: When creating campaigns based on (e)RFM and RFM models, we recommend excluding records with low RFM and eRFM from commercial mailings. These records appear to be uninterested in your products and emailing them is ineffective. Most importantly: do not forget your high eRFM/high RFM records. These are your loyal customers.