En utilisant l’analyse RFM pour construire des relations avec la clientèle
Developing and maintaining customer relationships in the retail sector has changed a lot in recent years. Before the Internet, it was all done in person. Today, you’ve got to maintain that relationship both in person and through digital means. One analysis method used to help retailers develop their customer relationships is the RFM Analysis.
RFM stands for Recency, Frequency, and Monetary. Here’s how to use RFM Analysis to make your customer relationships better.
- Récence – If you dig deep into your customer analytics, you will likely find that the majority of your business comes from a small minority of your customers. Often called the 80/20 rule, it states that 80% of your business comes from 20% of your customers. It may not be exact, but it’s a good rule of thumb. When you look at the data, you should pay close attention to recency. How many of your customers have shopped in your store in the last month, week, or day?
- Fréquence – Just because a person shopped at your store yesterday doesn’t mean they shop in your store frequently. But frequency is another factor retailers can use to gauge how deep their relationship with certain customers, and demographics, really is. Do you know what percentage of your customers return to your store frequently?
- Monétaire – The monetary aspect of your analysis has to do with how much money your customers spend in your store per visit. You may have customers who frequent your store often but who do not spend a lot of money per visit, while other customers may spend a great deal per visit and return less often. Don’t get hamstrung on one metric. Every customer is different. You want to focus on ways to make each customer relationship better.
How to Use the RFM Data
Your customers are individuals, so you should focus on each individual customer and not treat them all the same. That said, you can assign a number between 1 and 5 for each customer on each of these three metrics.
Rather than aggregate these scores, you can put your customers in data groups based on their rankings for each of these metrics. A 532 ranking would indicate a customer who visited your store recently but visits with average frequency and spend only a few dollars when they shop at your store. A 235 ranking would indicate a customer who has not shopped in your store recently but visits with average frequency and spends a lot of money when they do.
Neither customer is better. Retailers should use the data to look for ways to improve relationships with each type of customer in their database instead of honing in on the “best” customers to the neglect of others.