In particular, a 1995 article by Tom Wansbeek and Jan Roelf Bult titled “Optimal Selection for Direct Mail” that appeared in the journal Marketing Science is largely responsible for the development of RFM analysis. Their research supported the Pareto Principle, which holds that 20% of a brand’s customers account for 80% of its sales.
RFM analysis enables marketers to boost sales by focusing on particular customer demographics (i e. based on information about a specific set of behaviors) with messages and offers that are more likely to be relevant. Increased response rates, client retention, client satisfaction, and client lifetime value (CLTV) are the results of this.
Each of these RFM metrics has been demonstrated to be successful at forecasting future consumer behavior and boosting sales. Customers are more likely to make a purchase in the near future if they recently made one. More frequent users are more likely to interact with your brand again soon. Furthermore, those who have spent the most are more likely to continue spending a lot in the future.
You can target customers with messages that best reflect their relationship with your brand using RFM analysis. For instance, you’ll probably have more luck recommending expensive items to customers who make large and frequent purchases. On the other hand, by rewarding them for their loyalty or providing referral promotions, you are more likely to increase the customer value of your relationships with customers who make purchases frequently but in small amounts.
How to use the RFM Model & Analysis to Drive Continuous Ecommerce Growth (2018)
What is the RFM model?
According to the RFM model, a company can use the factors of recency, frequency, and monetary value to understand its customers’ spending patterns and create policies based on that information. Marketing experts can use the model to rank customer spending patterns to identify which clients are most likely to generate higher and more reliable returns. Businesses can rank customers, comprehend buying patterns, and forecast future spending behavior by allocating numbers to customers in each category.
What is RFM?
RFM stands for recency, frequency, and monetary value in an acronym. By examining their purchasing patterns, these elements collectively can aid a business in better understanding its clients. A more thorough explanation of what each component means for marketing analysis is provided below:
The term “recency” describes how recently a customer made a purchase. This is a crucial point of analysis because customers are more likely to remember a brand for future purchases if they recently made a purchase. Customers who haven’t interacted with the brand in a while might be less likely to make more purchases.
Understanding this factor can assist businesses in deciding how much and what kind of engagement to use for each type of customer. For instance, a business might invest more time and money into re-engaging with clients who it thinks will make similar purchases in the future. A company can also create specific plans, such as special reminders or discounts, to persuade less recent customers to return as customers after an extended absence in an effort to preserve the potential business of those customers.
The frequency of a customer’s purchases from a business is called frequency. The kind of product, how much it costs, and how often a customer needs to restock it can all influence how many purchases a customer makes. Customers might, for instance, buy perishable goods like milk more frequently than other goods. Similar to how knowing customer engagement rates can help a business decide where to focus its marketing efforts. It can also assist the company in creating timelines for attracting customers during their most likely times to make another purchase.
Financial value refers to the amount of money a customer spends per transaction or over a specified time period. In some instances, a company might want to concentrate on clients who are more likely to spend more money per transaction, potentially increasing the return on its marketing investment. However, in other circumstances, a company can also concentrate on clients who make regular purchases, even if their individual transaction amounts are not as high.
What is RFM analysis?
Businesses can use RFM analysis as a technique to comprehend consumer purchasing behavior. Typically, a company can rate a customer’s recentness, frequency, and financial value on a scale of one to five, with five representing the highest level of engagement. Utilizing the company’s available purchase data, you can produce these rankings.
The business can learn which types of customers are generating the most profit by assigning numerical values to customer purchasing behaviors. It can also assist a business in identifying clients who have similar buying habits and then developing specific, focused strategies for each group. You can create buyer personas for a business by having an understanding of these groups, which describe specific customers and their needs and habits. In order to decide how a business should prioritize its customer acquisition and retention policies, it can also assist you in understanding how much revenue comes from new or repeat customers.
Why is RFM important?
RFM is crucial because it functions as a comparison tool that enables a business to comprehend the purchasing patterns of various groups using numerical data. RFM can enable sales and marketing professionals to create specially tailored procedures for each distinct group or need rather than targeting all customer groups with the same strategies. By focusing on specific needs, this can assist a business in using resources in plans that foster organizational growth.
Benefits of RFM analysis
The following are some advantages of conducting RFM analysis within a business:
Helps professionals understand customer behavior
Knowing more about customers’ purchasing habits is one of the most important advantages of RFM analysis. You can learn how customers are interacting with the business in order to make predictions about how they might interact in the future by examining patterns involving recency, frequency, and the dollar amount of transactions. Additionally, you can group customers according to their purchasing patterns. You can use this useful information to better target customers who have similar habits by understanding how customers interact with one another.
Creates actionable, data-driven insights
RFM analysis also has the advantage of assisting you in the creation of actionable insights based on customer purchasing information. You can concentrate on predicting future customer behavior using data and developing policies to improve predicted outcomes rather than developing policies or promotions based on what has previously worked. You may be able to try new tactics using this specialized, evidence-based process depending on the support the data provides for them.
For instance, RFM analysis may reveal that the company receives a lot of repeat business from its clients. From there, you can choose to experiment with a new digital marketing technique where you give customers a discount after their tenth visit to entice them to return more frequently. You can create policies that specifically target customers who already make frequent purchases by using data to understand how frequently customers make purchases.
You can create policies or promotions to increase the frequency of purchases or the amount spent per purchase by learning about your customers’ purchasing behavior. By increasing revenue and reducing potentially ineffective policies, this can boost the company’s profit. Instead, you can implement more effective, data-driven strategies.
For instance, you might discover through RFM analysis that most customers spend less per purchase than you initially thought. With this information, you could ditch promotional materials that promised discounts for big-ticket buyers and concentrate on a strategy to reward loyal customers. This can reduce costs associated with a less successful marketing campaign while also boosting revenue through more frequent purchases.
Building relationships with customers can help you sell them products because you’ll have a better understanding of them. As you gain knowledge of their experiences and behaviors, you can start to create rules that will enable them to get the most out of the business. This can be even more effective if you know what they want from the business.
For instance, you might discover that offering sales encourages customers to make more purchases. You can infer from this data that some customers may be motivated by savings You can improve your relationship with those customers by providing them with additional chances to save in the form of rewards programs, special deals, or promotions tailored to their preferred shopping experiences.
Outlines customer journeys
You can visualize the story a customer goes through when making a purchase from the business by using RFM data. You can use that narrative to develop policies and strategies. A customer journey map can show you how a customer discovers a business, what inspires them to make a purchase from the business, and how they experience the business while making a purchase.
Even though you might not have data for all of these areas, you can start imagining various customer journeys using the information you do have. From there, you can develop procedures to complete any gaps in the journey’s data. For instance, you might create a customer survey to learn what elements of the shopping experience encourage customers to come back and make more purchases.
Another advantage of RFM analysis is that it frequently uses data that a company already has. You can use sales data that a company may already record rather than spending time-consuming data gathering procedures. This data’s accessibility may allow for efficient RFM analysis, resulting in quicker policy implementation and outcomes.
Runs simple and consistent procedures
Despite the fact that you can choose the RFM analysis options that best suit the needs of the company, this process is frequently standard and simple to follow across departments. Particularly if you employ the conventional ranking system of one to five, every employee in the business can comprehend what each number means. This can enhance teamwork and raise the possibility of creating effective policies across departments. For instance, by comprehending the specifics of customer behavior, marketing and sales teams can work together to develop promotions that have an impact.
How do you use RFM models?
- Build RFM Model. You must give each distinct customer a recency score, frequency score, and financial score in order to create an RFM model.
- Divide the Customer Segment. …
- Select the Targeted Customer Group(s) …
- Craft a Personalized Marketing Strategy.