Marketing RFM Analysis Simplified: 5 Step Approach Made Easy

RFM Analysis allows companies to increase eCommerce Sales and use the data to understand their customer’s behavior. RFM Analysis has a lot of upsides as it allows you to deploy personalized Marketing and increased engagement. It also allows you to create targeted offers for specific groups. If you wish to maximize Cohort Analysis to increase Retention, RFM Analysis is one of the many efficient ways to go about it.

This blog talks about RFM Analysis in detail digging into its benefits along with the 5-step approach to carrying out RFM Analysis for any data. It then wraps up with the applications of RFM Analysis. 

Table of Contents

• What is RFM Analysis
• Benefits of RFM Analysis
• RFM: Focused Use of Marketing Budgets
• Role of RFM in Customer Retention
• 5-Step Approach to RFM Analysis
  • Step 1: Relevant Data Assembly
  • Step 2: Setting Up RFM Scales
  • Step 3: Score Designation
  • Step 4: Segment Classification
  • Step 5: Personalization of Strategies for Relevant Segments
• Understanding the Applications of RFM Analysis
• Conclusion

What is RFM Analysis

RFM is an abbreviation for Recency, Frequency, and Monetary Value. RFM Analysis segments customer behavior in a data-driven fashion. The central idea governing RFM Analysis is the segmentation of customers based on:

• Last Purchase
• Frequency of Purchase
• Overall Expenditure

The metrics mentioned above are proven indicators of a customer’s intent to engage in Marketing offers and messages. To elicit the point further, you can take each component of RFM Analysis as follows:

• Recency: Recency as the name suggests refers to the total time elapsed since a customer’s last transaction or interaction with an organization’s product/website. A high Recency indicates a customer’s positive sentiment when considering your brand for a purchase decision lately. You can score Recency using custom-built filters such as bought in the last 7 days/ 1 month/ 3 months and so on. 
• Frequency: Frequency refers to the frequency of interaction of a customer with the brand during a particular time. A high frequency indicates high customer retention and engagement. Frequency can be calculated by analyzing the total number of purchases completed by customers in a specific period.
• Monetary Value: This refers to the total amount of money spent by the customer during a particular period. Accordingly, big spenders would be treated differently than the people who don’t spend as much. An important secondary factor could be Monetary Value divided by the frequency which gives you the average purchase amount.

Benefits of RFM Analysis

The RFM Model is based on the transactions between the user and the company, helping it identify the firm’s best clients. Traditional methods focused on using variables like Psychographic and Demographic factors to group its customers by utilizing sample audiences to predict the behavior. Since these were carried out manually and relied heavily on skilled researchers, the old methods were prone to human error. 

Conducting an RFM Analysis on your targeted customer group along with sending personalized campaigns to high-value targets has the following benefits for say an eCommerce store:

• Increased Conversion Rate: Personalized campaigns lead to higher Conversion Rates since customers are engaging with the products they are interested in.
• Increased Revenue/ Profits: RFM Analysis can help you understand the necessary tweaks that need to be made to your Marketing campaign for specific focus groups to increase your Sales and outreach in the process.
• Increased Personalization: By effectively dividing your customer base into customer segments, you can focus on creating personalized offers for each segment to help boost your Sales numbers.
• Increased Response Rate: RFM Analysis allows you to elicit a greater response rate from your customers because you have a highly personalized campaign to cater to their specific needs, leading to an increase in customer transactions and effective interactions.
• Increased Customer Retention: RFM Analysis gives you the right set of tools to increase your Customer Retention Rate because this allows you to go after the customers that are likely to Churn and come up with personalized campaigns to appeal to their interests as a first step of going after not-so-happy customers. For customers that are happy with your product, you could roll out new incentives and offers based on the findings of your RFM Analysis, that make them want to stick with you.      

RFM: Focused Use of Marketing Budgets

Customers have many options and marketplaces to buy products of their choice. It is becoming difficult for businesses and online stores to attract more customers as everyone needs shares of customer wallets. Understanding customer behavior is still a priority task. Segmenting customers into different groups help businesses focus more on winning customers that are more likely to convert.

Social Media Marketing plays an essential role in gathering customer behavior data and spreading brand value in the eyes of interested customers. With the availability of many options, customers have high expectations for value and price. Businesses need to create relevant, attractive, and personalized messages that enable them to attract more customers. 

RFM allows businesses to target different segments of customers with varying but equally relevant messaging. It also helps businesses detect varying patterns in user behavior with the help of RFM data. RFM helps companies know more about customers and recognize the interested customers that are more likely to be converted or customers that are about to churn out to active customers. RFM helps companies use the resources effectively by targeting the right customers and utilizing the marketing budget accordingly and increasing the overall impact of marketing on the business.

Role of RFM in Customer Retention

A business can not survive without customers because customers are the ones who pay companies for products and services. Small businesses face difficulty in acquiring customers because of their low reach and lack of brand awareness. It leads to spending huge amounts of money on marketing to acquire customers. Customer Retention plays a bigger role in this equation of acquiring customers and generating profits. It depends on the satisfaction of the customer with the products and services the business offer. 

Positive word of mouth is the most impactful way to attract customers maintain a positive image among other customers. Low Churn Rates are the easiest way to grow a business because it enables customer satisfaction. RFM helps businesses build and strategize different customer journeys for every segment of customers, establishing loyalty and trust.

5-Step Approach to RFM Analysis

So far, this article has talked about the various benefits of RFM Analysis. RFM Analysis is effective and intuitive when it comes to giving businesses the ability to recognize the latest buyer persona trends. It also allows businesses to identify and focus on converting critical customer segments. For instance, customers that are on the verge of churning then end up becoming active users. Here are the steps involved in conducting RFM Analysis for your business:

• Step 1: Relevant Data Assembly
• Step 2: Setting Up RFM Scales
• Step 3: Score Designation
• Step 4: Segment Classification
• Step 5: Personalization of Strategies for Relevant Segments

Step 1: Relevant Data Assembly

As mentioned before, RFM Analysis deals with historical customer transactional data. The first step, therefore, is an assembly of all the RFM data for every customer in ascending order.

Step 2: Setting Up RFM Scales

Businesses need to come up with custom-built filters needed for the segmentation of the customer base. Based on the different suggested filters, in the description of Recency, Frequency, and Monetary Values you should come up with a filter for your RFM data before you even begin the analysis. Here is a sample set to get you started.

Step 3: Score Designation

Based on the sample table above, each customer is designated a score. You are converting absolute values of transactions into portions of transactions that are similar, based on RFM. Once you have converted all the absolute values into scores, you can discard them since you will only be using the scores for analysis and segmentation. After you have assigned the scores, you can group the customers who have similar scores across three criteria. You can use the figure given below as a reference.

Step 4: Segment Classification

The customer segments will be classified based on the different characteristics of the grades received by the customer base. Given 5 score segments and 3 criteria, the number of possible unique segments is 125(5*5*5). The number of unique segments to be used by a business would be a subset of this number depending on the nature of the business. The standard labels used are shown below.

These segments can be summarized below, giving you an idea about what each segment means and indicates.

Step 5: Personalization of Strategies for Relevant Segments

Once you are done with the segmentation part, you can move on to developing personalized campaigns for each customer segment.

• Champion customers: They are given greater access to products and act as the first point of contact for feedback before launching it for the rest of the customer base.
• Loyal customers: They can be offered a higher level of service to ensure they feel more valued and continue their customer journey with your brand. 
• Recent customers: They can be filled in on the other products that they might want to try out based on their preferences so far, in an attempt to convert them into loyal customers.
• At-risk customers: They can be offered freebies, offers, and discounts to ensure they don’t end up becoming a part of the Customer Churn statistic. This can be done simultaneously by the business.

Therefore, by minimizing the waste of resources through focused and effective targeting, RFM Analysis allows businesses to use their Marketing budgets wisely while propelling the overall impact of Marketing on the enterprise.

Understanding the Applications of RFM Analysis

Personalization lies at the heart of RFM Analysis. With the advent of social media and technology, customers have the ease of choosing viable alternatives and express displeasure for the same; customer expectations have gone higher regarding the quality of brand interactions. Creating relevant and personalized messaging, catering to the user needs, and staying in touch with the buyer persona is a must in this digital world. Here are a few key applications of RFM Analysis in an enterprise:

• Strategy for Effective Messaging: You can create personalized and customized messaging with the help of RFM Analysis. It allows you to align the various messages you might send to a customer, and keep sending the same kind of messages to prevent any annoyance or dissatisfaction on the customer’s side. This will then lead to greater customer satisfaction.  
• Strategy for a New Project: RFM Analysis helps you filter out the most valuable customers from the least valuable ones. During the launch of a new project, you can come up with a strategy to engage the Champion customers that will improve the customer perception through Word of Mouth (WOM) Marketing leading to a greater outreach translating to a greater number of sales. 
• Strategy for Media: After the completion of RFM Analysis, the results give you a deeper insight into user needs and when are the different customer segments most likely to interact with you. A media strategy can be devised that combines different mediums and formats, for different durations to target different segments based on their characteristics. 

Conclusion

This article gives you a roundabout of RFM Analysis while touching upon the points like its benefits, importance, the steps involved in performing an effective RFM Analysis on your customer data. It finally wraps up with the vast applications of using RFM Analysis for business.

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