RFM
Analysis For Successful Customer Segmentation
Analysis RFM in Big
Data
RFM (Recency, Frequency,
Monetary) analysis is a proven marketing model for behavior based customer
segmentation. It groups customers based on their transaction history – how
recently, how often and how much did they buy. RFM helps divide customers into
various categories or clusters to identify customers who are more likely to
respond to promotions and also for future personalization services. RFM
analysis evaluates which customers are of highest and lowest value to an
organization based on purchase recency, frequency, and monetary value, in order
to reasonably predict which customers are more likely to make purchases again
in the future.
What are Recency, Frequency and Monetary?
· Recency : How
much time has elapsed since a customer’s last activity or transaction with the
brand.
· Frequency :
How often has a customer transacted or interacted with the brand during a
particular period of time.
· Monetary :
Also referred to as “monetary value,” this factor reflects how much a customer
has spent with the brand during a particular period of time.
RFM analysis is
popular for three reasons:
- Utilizes objective, numerical scales that yield a
concise and informative high-level depiction of customers.
- Simple, marketers can use it effectively without the
need for data scientists or sophisticated software.
- Intuitive, the output of this segmentation method is
easy to understand and interpret.
RFM work system
RFM analysis classifies
customers with a numerical ranking for each of the three categories, with the
ideal customer earning the highest score in each of the three categories. So,
for example, depending on the purchase cycle of your company's product or service
you might evaluate customers for recency on a scale of 1-10, with a score of 10
indicating the customer had made a purchase from your company within the last
month, and a score of 1 indicating that their last purchase was 10-12 months
prior.
Once a company has
decided on its 1-10 scale for each of the three categories, it can review its
CRM and give each customer a score for each category. Then, by adding up the
three combined scores, companies can run an RFM analysis to determine which
companies are most likely to buy again soon, and use that information to
prioritize how they're reaching out to and creating value for those high-value
customers.
Recency
|
Frequency
|
Monetary
|
R-Tier-1 (most recent)
|
F-Tier-1 (most frequent)
|
M-Tier-1 (highest spend)
|
R-Tier-2
|
F-Tier-2
|
M-Tier-2
|
R-Tier-3
|
F-Tier-3
|
M-Tier-3
|
R-Tier-4 (least recent)
|
F-Tier-4 (only one transaction)
|
M-Tier-4 (lowest spend)
|
An RFM analysis helps
you find commonalities and differences between customers who repeat purchase
and customers who don't to help you learn where there are gaps in your customer
experience.
SYLVIA
DIVARDA
106218079
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