Thought leaders in the commerce industry, across all channels, are emphasizing the importance of RFM analysis for identifying your business's best customers. What can RFM can do for you? Magento BI analysts Sal Calvo and Kerri Nunnamaker will spend time on March 22 walking through each valuable component of recency, frequency, and monetary analysis. What you’ll learn: • Exactly how valuable a recently acquired customer is in the long run • How to use seasonality trends to predict repeat purchasing frequency • How our Professional Services team can identify and rank the most valuable customers in your dataset

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Recency, Frequency, Monetary
(RFM) Analysis in Magento BI
Presented by Salvatore Calvo and Kerri Nunnamaker
March 22, 2017

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What's RFM analysis?
• A new industry standard for identifying your
top customers, and who you can expect
to become top customers
• A way to benchmark your business's
success in retaining customers and
revenue
• A technique you can use with
the commerce data you already own

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A walkthrough of the three cornerstones:
• Customer recency habits
• Buyer frequency behaviors
• Monetary (LTV) analysis
A case study on how to score customers
based on RFM activity, and predict LTV
growth in Magento Business Intelligence
Today's Agenda...

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A brief history – where did RFM come from?
• Origins in the brick-and-mortar retail industry, where customers
couldn't be identified with certainty
• Based on assumption that a valuable customer had a combo of
recent, frequent, and expensive purchases
• Effective jumping off point for developing a marketing strategy
• Incredibly powerful in the ecommerce age with your data

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Step 1: Understanding customer recency
Recency implies that a client who
has recently interacted with your
business, all else equal, is more
valuable than one you hasn't.
• What % of your customer base
has made a purchase in the
last 60 days?
• How many of your recent
purchasers are 1st-time
buyers, vs. Repeat buyers?
• What are popular products or
trends in recent purchases?

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Recency in Magento BI

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Step 2: Understanding buyer frequency
Frequency assumes that a repeat
buyer is inherently more valuable
than a new buyer.
• What is your business's cost of
acquisition (in $)?
• What is your business's churn
rate? Its retention rate?
• How long does the average
buyer wait before returning?
Actions to accelerate customer
frequency...

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Frequency in Magento BI

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Step 3: Drilling into monetary activity
Monetary = the assumption that
valuable customers will
continue to be valuable.
• What's your current average
lifetime LTV?
• How does average LTV grow?
First 30 days, 90 days?
• Are there segments of
customers (demo, products
bought, location) with different
LTV profiles?

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Monetary in Magento BI

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The road so far...
RFM
1. Recency
2. Frequency
3. Monetary
Extensions
1. Assigning RFM scores to create unique customer groups
2. Use cases for assigning scores

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How to assign individual scores
Let's consider monetary
score.
Out of all customers, assign
the top 20% of customers
who have spent the most
revenue a score of 5.
Assign the next top 20% of
customers who have spent
the most a score of 4.
And so on...

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How to assign RFM scores
Concatenate the scores for all 3 attributes
Customer A:
• Recency: 5
• Frequency: 1
• Monetary: 1
Customer B:
• Recency: 1
• Frequency: 4
• Monetary: 5
Overall score: 511 Overall score: 145
New Customer Churned Customer

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What are the use cases?
1. Targeted email lists
2. Identifying actions within certain customer groups that may be
causing this behavior
3. A/B testing within groups

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Targeting email lists
Customer A:
• Recency: 5
• Frequency: 1
• Monetary: 1
Overall score: 511
Customer B:
• Recency: 1
• Frequency: 4
• Monetary: 5
Overall score: 145
New Customer Churned Customer

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Targeting email lists
New Customers
Churned Customers

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Investigating customer groups
1. Are there any common trends for how these customers were acquired?
– Season of year
– Sale
– Promotional campaign
2. Are there any common products these customers purchased?
3. Are there any common events these customers participated in?
– Sales
– Product reviews
– Surveys
– Support requests
Consider the best customer group, RFM scores of 555.

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A/B testing within groups
A/B testing within groups which have similar behaviors allows better
conclusions on whether the action you're testing worked.
Consider the "shopper" group, RFM scores such as 342 or 452.
Can you get these shoppers to engage without providing a large
discount? Do they engage just the fact that there is a discount
available?
• Test an email campaign
– Test A: 10% coupon code
– Test B: 5% coupon code

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To sum it all up
• Defining RFM
• How you can create RFM analyses in your account today
• Assigning RFM scores
• Using scores and customer groups to create targeted email lists,
identify common behaviors within groups, and perform A/B testing
Reach out to support@rjmetrics.com today!

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Questions