Who are your loyal customers?
Which of your customers are most likely to churn?
Who are your big spenders?
How many one-time customers do you have?
If you can’t answer these questions, RFM analysis is a good bet to find your best customers and maximize their spending.
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What is RFM Analysis?
RFM analysis is a customer segmentation technique based on the Pareto Principle (20% of customers are responsible for 80% of company revenues). RFM involves the use of three key customer behavior metrics:
- Recency – the number of days since the last purchase.
- Frequency – the number of orders placed within a specified period
- Monetary – the total amount of money spent by a customer in a specified time period
As RFM analysis is based on customer purchase history, it’s a powerful tool for eCommerce stores. Windsor circle reported significant success when using RFM for their retail customers:
- Eastwood increased their email marketing profits by 21%
- L’Occitane saw 25 times more revenue per email
- Frederick’s of Hollywood recorded conversion rates as high as 6-9% in their campaigns
In this article we’ll drill into specifics on why RFM analysis is important in ecommerce.
Find Your Best Customers
According to a RJMetrics e-commerce survey of 176 e-commerce retailers and 18 million customers, the top 1% of ecommerce customers are worth up to 18 times more than average customers. In addition, the study stated that after 24 months, 50% of the revenue comes from returning visitors.
The question is, how do you find those VIP customers? RFM analysis.
Here are quick steps to help you find your high value customers using RFM analysis.
Step 1: Access your customer purchase history. This should include the date of the most recent order, number of orders placed over your selected time period (a year will work best), the total value of all purchases made in that time period, and customer ID.
Step 2: Divide the customers into four equal quartiles, based on the distribution of recency, frequency, and monetary values. This will result in 64 different customer segments across the three variables.
Step 3: Score customers on a range of 1-4. With 1 being the highest score and 4 the lowest. For example, a customer who made a recent purchase will be within the first quartile in R (R=1). A customer who purchased the smallest quantity will be in quartile 4 in F (F=4).
Step 4: Sort the RFM Cell score for all customers. Customers with RFM score 111 are your ideal customers. While those with 444 are the least desirable.
When analyzing results, the top 20% are your best, most profitable customers and more likely your respond to your communication. Use this information to plan your marketing and offer personalized messages to your high value customers.
Personalize Offers
Personalised offers can boost conversion rates by up to 10%. RFM analysis allow brands to better customers and offer them personalised offers at the right time to incentivize a desired action.
Here’s how you can personalize your offers to customers with insights from RFM analysis:
- High RFM customers: These are customers who score the highest for recency, frequency and monetary value in your RFM analysis. As your best customers, advertise your high-end products and reward them for their positive buying behavior with unique offers. For instance, Starbucks rewards its high value customers through a customer loyalty program that rewards them with free refills, food and drink offers, and a personalized gold card.
- Medium RFM customers: Customers in this segment will most likely be your newest customers as they have a high recency, low frequency and low monetary value. Grow this segment and turn them into recurring customers with tailored promotions. For example, spend X amount of money to earn 3x points is one offering that can drive growth, especially among the most loyal members.
- Low RFM customers: With low recency, low frequency and low monetary value, this segment will be your inactive or least engaged customers. Nudge them to become medium RFM customers by offering them free trial periods or one-off discounts.
For hyper-personalization, combine RFM and psychographic information to build campaigns that’ll resonate with your customers. Don’t over solicit customers with the highest rankings, it’ll lead to email fatigue. Within reasonable limits, nurture customers with low rankings to become better customers.
Prevent Customer Churn And Build Customer Loyalty
Acquiring a new customer is 5 to 25 times more expensive than retaining an existing one. On the other hand, studies by Bain & Company, along with Earl Sasser of the Harvard Business School, have shown that a 5% increase in customer retention can lead to an increase in profits between 25 and 95%. Based on the above, most e-commerce brands use win back campaigns to tackle customer churn. This strategy, however, is reactive and consequently, not all customers return.
Smart marketers know the best way to address ensure customer retention and address customer churn is to be proactive and prevent it. Take a look at Verizon.
To cut the churn rate among its contract customers in half, Verizon Wireless used data mining techniques to anticipate those customers most likely to leave. Decide what “save” offer would be most effective for each customer and calculate how likely each customer would be to respond to the offer.
By mailing the right offer to those with a good likelihood of accepting it, the company saved some 60% of what it would otherwise have had to spend and reduced attrition from 2.6% to just 1.3% per month.
RFM analysis is a good churn indicator because it examines how recently a customer has purchased, how often they purchase and how much they usually spend. You can easily detect if there’s a drop-off in a customer’s purchases or average spend and identify customers who are ready to leave your business. By segmenting with this model, you can pinpoint at risk customers who are likely to churn.
This knowledge enables you to send the above segment of customers personalized campaigns to encourage purchase. When offering incentives to prevent customers from churning, ensure money is spent on customers more likely to increase their spend.
Conclusion
RFM is a way of making marketing decisions based on customer spending behavior and understanding customer value. While RFM is a valuable tool, however, don’t rely on it alone when developing your marketing plan. Combine it with insights from customer feedback and results of prior marketing initiatives to decide how best to communicate with your customers.