Advantages and Inefficacies of RFM Segmentation?
RFM Segmentation is a frequently used data analysis method to increase the ROAS of companies. It is mainly based on the customers' behavior and takes its name from the initials of the words Recency, Frequency, and Monetary metrics. There are benefits and attention points of RFM analysis. Let's examine these three metrics.
What are these R, F, and M?
Recency: Recency is a metric about when the customer last made a purchase. The recency score of the person who acted most recently according to today is scored as 5.
Frequency: The metric about how often the customer shopped in a specific period. We may find loyal customers with frequency analysis. Besides, companies can generate an ad audience in order to increase the number of loyal customers.
Monetary: The metric in which customers are segmented according to the spend or purchase power. Some customers don't interact often, but when they do, they also shop in bulk. The audience with low recency and frequency but high monetary scores will be selected as the target audience. In this way, companies increase the revenue by reaching the customers who have the potential to spend more money.
Process of RFM Analysis
After analyzing these three headings, these values are scored between 1 and 5. And three of them are averaged. Scores are ranked from 5 to 1 as from best to worst. Furthermore, dual models can be used to fulfill different sectors' needs or optimize the third one.
For example, if you are a domestic appliance seller, frequency is not an important metric for your customer analysis. So it would be best if you focused on RM criteria to optimize your sales. Another common model is the RF Model, especially if you are analyzing a single product. You can see the segments prepared by the RF criterion by looking at the picture below.
When carefully examined, it is possible to choose the ad's target audience. It is possible to upgrade the groups such as "need to attention," "promising," "potential loyalists" to loyal customers. In addition, groups such as "at-risk" or "about to sleep" are possible target groups to stop customer churn.
Benefits of RFM Analysis
Boost remarketing strategy: helps your customers to purchase more frequently with mail and other ad types.
More loyal customers: although some customers buy from you, they are not completely loyal to you. That is, you may need to make them feel special and show some attention. You can utilize your advertising and promotion activities by determining this audience with RFM analysis.
Reducing churn rate: retain customers is less expensive than acquiring new customers. With RFM segmentation, you can identify your customers who will churn and take action before they give up on you.
Increasing sales: One of the main ways to increase revenue is to increase sales, isn't it? RFM analysis can help you identify the audience you should target from among your customers.
In brand we trust: if your champion segment is strong, your brand visibility and credibility will increase. As you reward your champion audience, their attitude towards you will make it easier for other potential customers to come. This is one of the simple ways to gain organic customers.
Better is Possible
We made a short introduction to RFM analysis, define and counted the benefits. Also, I want to mention that although RFM analysis may not be sufficient to have big goals and increase their ROAS more. Unfortunately, RFM segmentation is not enough to optimize ads. So why?
- One of the first reasons is that RFM analysis measures the behavior of customers. Although it is used to measure the behavior of visitors, it cannot be said to be a beneficial method to optimize visitor data. However, many companies want to gain new customers as well as current customers. Accordingly, RFM segmentation may not help acquire new customers.
- Secondly, RFM analysis calculates with only three metrics: recency, frequency, and monetary. However, measurements made with these metrics may not always give complete results. There should be more and different metrics to understand the behavior of visitors and customers. Even data analytics tools such as Enhencer offer more advanced options than this.
- Thirdly, e-commerce companies are changing and developing day by day. Instant campaigns and strategies almost must in the data analytics and advertisements sector. Therefore, self-learning and automatic segment analysis with the machine learning algorithm are also more valuable and useful.
As a result, RFM can be a simple method. Nevertheless, there are better options for those who want to multiply the ROAS values by optimizing their advertising strategies, for instance, Enhencer. I would love to tell you about Enhencer, but I don't want to distract the audience with a long article. In the following article, we will compare Enhencer's AI-Based automatic segments with the RFM segments.