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
When individual customers are scored according to RFM metrics, marketers gain valuable insight into buyer behavior. 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.
- 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.
Other strategy frameworks:
- AIDA Model
- Ansoff Matrix
- Balanced Scorecard
- BCG Matrix
- Design Thinking
- Lean Startup Canvas
- Pestel Analysis
- Technology Adoption Curve
- Total Addressable Market