RFM analysis: learning to segment customers using a modern tool

We have already blogged about why it is important for a business to segment its customer base correctly. You can find out what audience segmentation is in this article, and learn more about the features of working with segmentation from our social networks.

Modern ways of dividing the audience into separate parts allow you to better understand the needs of buyers and anticipate their behavior. In the process of communication, each client forms his own history of communication with the brand. RFM analysis is a modern method of user segmentation, with the help of which such a picture of interaction will appear before the business in the most complete and correct way.

What is RFM in simple terms?

The Pareto principle underlies many marketing strategies. The logic of this rule of thumb is simple: 20% of efforts bring 80% of results and vice versa - 80% of actions bring only 20% of success. In marketing language, it sounds like this: 20% of customers provide 80% of the company's profits.

RFM customer analysis is a segmentation method that allows you to identify the active part of consumers with the highest engagement and KPI. Also, it is used to determine:

1. Segment of "warm" customers to work with them in the future.
2. "Cold" audience, which is not worth spending time and resources on.

The RFM (Recency, Frequency, Monetary) method works with three indicators and divides the audience into groups accordingly. Each of them can have two subgroups.

Based on the results of RFM, the company receives data to build a separate strategy for interacting with each of the client segments. This does not mean that some customers will become more valuable and others less. However, this tool makes it easier to determine how to communicate with a specific group of users. For example, set up an SMS-messaging with a personal offer for loyal customers, and offer a promotional code to “sleeping” customers.

Cases where RFM analysis is most useful

Please note: RFM customer analysis is primarily relevant for B2C companies with an established audience of 10,000 or more consumers. Small local businesses and even B2Bs with a limited number of contacts can also use this method, but in their case, it will be advisable to reduce the number of segments.

The distribution according to RFM customer analysis is focused on businesses that have regular sales, and on a large scale: marketplaces, online stores, and the like.

RFM marketing should be applied in order to:

• increase profit from e-commerce;
• increase the effectiveness of SMS-messaging;
• increase the conversion rate in advertising campaigns.

Depending on the niche and business goals, the analysis tool will be based on different metrics. For example, purchase history, number of visits to online stores, and user response.

How to conduct RFM customer analysis

The first step in any segmentation is collecting information. The following data are taken for analysis:

• Full name, contact phone number, email.
• The date of the last purchase or interaction.
• The number of purchases for the selected period (most often, a calendar year is taken as the main timeline).
• Amount spent during the selected period.

When all the items are filled, go to the main indicators of the RFM analysis of customers. As we found out above, this segmentation method comes down to three concepts:

1. Recency indicates when the last purchase or target action was made.
2. Frequency is a purchase frequency indicator.
3. Monetary represents the amount spent by the customer.

As a result of the distribution of the audience, up to 30 groups or more can be formed. In some cases, only 3-4 segments are sufficient.

Recency

Most often, when making a selection of users according to the criterion of "recency", companies focus on several timelines. As a rule, three or four time intervals, for example:

• From 213 days (7 months) - bad.
• From 60 to 213 days (from 2 to 7 months) - normal.
• Up to 60 days - good.

For businesses with a serious flow of sales, it is optimal to divide the "Recency" into more periods - from 4.

Each group is assigned a score (from 1 to 3) depending on the methodology. For example, in one metric, “1” may be the highest score, and in another, the lowest score.

Frequency

The "Frequency" criterion is set depending on the field of activity of the company. The subject of monitoring by this criterion can be: purchases, clicks on the button in the message, visits to the site, and contact the support service.

By analogy with "Recency", here we can distinguish the following groups:

• One purchase is bad.
• 3 to 5 is normal.
• From 5 - good.

We assign a score.

Monetary

When analyzing in terms of costs, it is worth starting from the average check indicator. It is not advisable to set the desired purchase amount for you since the corresponding percentage of consumers will be very small. For example, if your company has an average customer check of $250, the segments would look like this.

• From $ 300 - good.
• From $150 to 300 - normal.
• Up to $150 - bad.

Next comes the sorting and assignment of points according to the criterion Monetary.

Advantages and pitfalls of RFM segmentation

RFM customer analysis, like any method, has pros and cons.

Advantages:

1. Result orientation. Focusing on the activity of customers who have made purchases will help to more accurately target marketing efforts to those who have already shown interest in products or services.
2. Individual approach. Leads to more personalized and targeted marketing and customer service.
3. Segmentation is the basis of marketing optimization. The division of the target audience into small segments simplifies the decision on the composition, focus, and priority of marketing actions.
4. Relevance of the data. Uses the most up-to-date and recent customer activity data, so is more relevant.

Disadvantages:

1. Limited in the analysis of customer behavior. Three main factors (Recency, Frequency, and Monetary) may not be enough to fully understand the behavior of customers and their motivations.
2. The absence of context. Doesn't take into account context that may affect audience behavior, such as seasonal trends or changes in preferences.
3. Limitations of the analysis in dynamics. Does not always capture changes in user behavior over time.
4. Difficulty in interpreting the results. Customers, even with the same RFM scores, may have different profiles and needs.

A common disadvantage of RFM is that additional analytics and context are required to better understand customer behavior and develop marketing strategies. The method is useful for initial segmentation.

In conclusion

Some additional tips for using RFM customer analysis:

• Use various variables to segment the audience to get a more accurate idea of its behavior.
• Go beyond allocation based on recent user activity, purchase frequency, and purchase size. Consider other factors such as location, interests, and shopping habits.
• Use segmentation to develop personalized marketing campaigns that will be important and relevant to each consumer group.
• To interact with each segment point by point, use SMS, WhatsApp Business and Viber for Business in Decision Telecom.

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