The RFM analysis is a marketing framework that seeks to understand and analyze customer behavior based on three factors: recency, frequency, and monetary. The RFM analysis allows businesses to segment their customer base into homogenous groups, understand the traits of each, and then engage each group with targeted marketing campaigns.
Understanding the RFM analysis
The RFM analysis differs from other methods such as demographic segmentation, where a customer base is targeted according to age, employment, gender, or other demographic data.
In the RFM analysis, the ultimate goal is to predict which consumers are most likely to make a repeat purchase.
To this end, individual consumers are segmented based on buyer behavior that includes:
Recency
The time elapsed since a customer bought or last engaged with a product.
Frequency
Expressed as the total number of transactions and engaged visits or the average time between transactions and engaged visits.
Monetary
Describing the intention of a customer to spend or their purchasing power. In other words, the total or average value of their transactions.
Implementing an RFM analysis for customer segmentation
Marketers gain valuable insight into buyer behavior when individual customers are scored according to RFM metrics.
Simple you start by defining the three main elements:
Recency: for instance, you can define it as customers who have purchased a product within the past 30 days.
Frequency: for instance, you consider them as those customers who performed two purchases per month are considered.
Monetary value: in this specific case, you can say that a customer that spends more than $75 is valued.
Based on that, you can build a table as follows:
Based on the above, the main conclusion is that customers 1 and 4 are the most valuable ones, as they are both recent and frequent, and both spent more than $75, thus high-value customers.
Thus, in this way, you can prioritize the business strategy toward those high-value customers.
The analysis will clearly identify the customer who spends the most money on a business, but it also provides information on:
Customers who contribute most to churn rate, or the rate at which a customer stops doing business with an organization.
Customers who have the potential to become valuable, repeat buyers.
Customers who are most likely to respond to push notification marketing.
Ultimately, the RFM analysis tells a business where each consumer is in their buying journey.
The journey will be specific to each individual and indeed each business, and it guides whether future offers should be made and when.
For example, a consumer who buys a pair of shoes with a high recency score and low frequency and monetary scores is likely a new customer.
As a result, the shoe company might send them follow up emails with shoe care tips and maybe the cross-promotion of laces or inserts.
Conversely, a consumer who buys another pair of shoes with low recency, high frequency, and high monetary score is a high-spending though disengaged consumer.
The shoe company might look at his purchase history to offer them a new pair of shoes at a price point they find attractive.
Industry-specific RFM analyses
Depending on the industry, a business should increase or decrease the relative importance of RFM values.
For example, a car dealership should place little importance on frequency – since new cars are not products that are bought monthly.
Recency is a better metric, as the company can target previous buyers who are more likely to upgrade models.
In the consumer staples industry, frequency and recency are more important than monetary – because most discretionary items are low value and need to be replenished periodically.
Lastly, a not-for-profit charity organization should value the monetary and frequency component above all. This is because charities are reliant on periodic donations to survive.
RFM Analysis Case Study: Clothing retailer
Here the RFM analysis might help identify their most valuable customers.
Indeed for any retail business, understanding where the high-value customers are is critical for the business’s long-term survival.
Here it starts by defining who are the highly valued customers.
For instance, for a clothing store with an average price tag of $50-100, the company might define the high-valued customer as the one that has bought in the last month (recency), has made at least two purchases in the last quarter (frequency) and spend over $100 (high value).
Based on that, the clothing store can identify those customers for which it’s worthwhile to have a specific promotional strategy to incentivize them.
For instance, for those high-value customers, the clothing store can unlock access to new lines as they come out before anyone else.
Thus, creating a sort of showroom for those customers first.
Those customers might be willing to pay more for that clothing line, provided they can get it before anyone else and have more options to choose from.
In this way, the clothing company can ensure to satisfy as many valued customers as possible, making their purchases even more frequent.
Higher value while instead leveraging on discounts and offers for the low-value customers, which might have more price sensitivity and might be fine to get clothes later, as long as they can save money.
This bucketing of customers might help. the company to generate specific campaigns with various pricing points organized between high-valued customers (who will get the offering before anything else, paying more but also getting exclusive access and more options to choose from).
And low-value customers (who will get the remaining products that high-valued customers didn’t want and that low-valued customers will get at a special discount).
Key takeaways
The RFM analysis argues that recency, frequency, and monetary traits are the best predictors of consumer buying behavior.
The RFM analysis can provide valuable insight into where consumers are in their journeys, which in turn dictates the most effective marketing strategies.
Depending on the industry and the nature of purchasing decisions, certain metrics in the RFM analysis should be given more weight than others.
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Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
