RFM analysis

RFM (recency, frequency, monetary value) analysis is a marketing method used to identify the best customers based on their spending habits. It helps to predict which customers are likely to buy products again and to estimate the income of both regular and new consumers.

The RFM model is based on:

Recency : How long ago a customer made a purchase, that is, how recently.

Frequency : How often a purchase is made

Monetary value : How much money a customer spends on their purchases

RFM analysis numerically ranks a customer into each of these three categories, typically on a scale of 1 to 5 (the higher the number, the better the result). The “best” customer would receive the highest score in each category.

What is the purpose of doing an RFM analysis?

This marketing tool comprises three main quantitative categories: the date of purchase of a product by a customer, the frequency of purchase by this person, and the amount of money this customer spends. It allows companies to rate their customers and identify those who add the most value.

In conducting the analysis, marketers compare the business benefit of an existing and a new customer.

RFM analysis helps companies manage their advertising budget wisely. It enables marketers to identify consumers with the same values ​​and segment them.

The customer segmentation allows brands to create specific campaigns, tailoring messages and meet your needs. The result is a higher level of customer satisfaction and a higher return on investment.

This method is essential because it allows us to know the customer’s behavior, which influences retention, the customer’s lifetime value and commitment. After performing the RFM analysis, the level of satisfaction, the interest in promotions and the volume of spending can be identified.

Elements of RFM analysis

To carry out the analysis and take advantage of the benefits of this technique, you must know what aspects to pay attention to.

RFM analysis begins with ranking customers based on the following key factors.

1-Recency

The more recent the purchase, the greater the likelihood that a customer will remember the brand and keep it in mind for the next transaction.

Recent customers are more likely to buy something than those who haven’t transacted for months. This data is important to businesses as it helps distinguish recent customers and encourages them to buy again soon.

2-Frequency

There are many aspects that influence how often customers buy. Among them, the type of product, the price and the need to have a product again.

Companies can anticipate demand. For example, products that customers buy today will sell out within a certain time frame. After a while, they will go back to the store to buy more. Because it is a repetitive process, brands can predict the next purchase date and direct their marketing efforts to remind customers to visit the store when products run out. In this way, brands can retain their customers, since they love to be served and of course, obtain rewards.

3-Monetary value

This factor focuses on the amount of money that each customer spends with a brand. Businesses encourage their consumers to spend more to meet their income goals.

Businesses pay attention to these 3 factors and rate customers from 1 to 5 (5 is the highest score). Customer ratings are calculated and the customers with the highest value (the best consumers or captive customers ) are identified . They then use this data to create personalized advertising campaigns, offers or promotions to improve ROI.

How to quantify RFM analysis?

Creating a ranking for your users according to RFM analysis is relatively simple. In general, what is usually done is to assign a score to each customer according to each of the three variables. You can use a scale from 1 to 5 or from 1 to 10.

A simple idea considers that:

• The more recent the conversion, the higher the recency score;
• The greater the number of conversions in a given period, the higher the frequency score; and
• The higher the lifetime value, the higher the monetary value score.

Finally, it is up to you to decide if the weight assigned to each of the three variables will be the same or if there will be some prioritization, since this is closely linked to your business objectives.

In the pet shop example, it makes sense to give more weight to frequency if the business objective is to optimize the sale of consumer products. On the other hand, an e-commerce that sells electronic equipment should probably prioritize the monetary value, considering that the conversion frequency tends to be lower.

What are the benefits of RFM analytics?

The main benefit of RFM analytics is the potential for optimizing the customer base you already have. For businesses with a high recurrence rate it’s even more important as it can save you a lot of the money, time and effort you put into acquiring new customers.

Retaining a customer from your base is about 7x cheaper than acquiring new customers, not to mention the benefits of creating evangelists that will take your brand to other customers organically. Additionally, increasing your retention rate by 5% can lead to a 25% to 95% increase in profit, according to a study by Frederick Reichheld with Bain & Company .

By taking actions based on RFM analytics, you can create a level of personalization that will impact and convert your customer base with far less time, money, and effort than it takes to acquire new customers.