What is RFM analysis ?
RFM analysis is done in order to understand the health of a customer's relationship with a retailer and the value of a customer to a retailer by analyzing the historical sales data for the respective customers. The outcome of this analysis is used in direct marketing, customer centric promotions and loyalty programs.
The modules used in the analysis are :
RECENCY :
How recent did a customer purchase ?
This explains when was the last purchase made by the customer, by this we can select the list of customers who has purchased from the retailer in the last 90 days or 180 days or 365 days.
FREQUENCY :
How frequently a customer purchases ?
This explains what is the frequency of purchases the customer makes with a retailer. This is calculated based on number of purchases made by a customer in the last 90 days or 180 days or 365 days.
MONETARY VALUE :
How much does a customer spend ?
This explains how much does a customer spend for a stipulated time period similar to the previous two scales. This also provides information on customer spend per purchase and over a period of time i.e. a customer might not make big shopping per visit but totaling their purchase over the same period of time will project a bigger purchase value compared to a customer who has done a single big purchase.
VOLUME :
How many products a customer has purchased ?
This is an add on to the RFM analysis which indicates how many products a customer has purchased or volume quotient of a customer's purchase pattern over a period of time. This in combination with Monetary value of the customer shows whether the customer purchases high value products or a general shopper buying small value products for regular use
SCALE OF MEASUREMENT
Recency, frequency and monetary bench mark scales may vary from retailer to retailer based on their product assortment and the lifespan of the product sold. Based on a retailer's product portfolio they can choose a scale based on a benchmark set for each of the module. We can look at how this can be done through a simple example :
A is a supermarket store having products in convenience, home appliances and grocery categories.
Sameer had purchased at A 5 times in the last 30 days as on 30-Nov-15, his last purchase was on 27-Nov-15. He has purchased for 1000 Rs., 2500 Rs., 1500 Rs., 1500 Rs. and 2200 Rs. respectively. Total quantity of products purchased by him is 20
Rachel had purchased at A only once in the last 30 days as on 30-Nov-15, her last purchase was on 2-Nov-15. She has purchased for 10500 Rs. Total quantity of products purchased by her is 1
Based on the above purchase data we can easily arrive at the below observation through data analysis:
Sameer and Rachel are both high value shoppers but there Is a lot in difference between the two which this analysis throws light on, that can help the retailer can use to arrive at customer centric promotion to increase sales and frequency of customer visits.
SAMEER'S PURCHASE ANALYSIS :
- He is a recent shopper who has visited the supermarket in less than 15 days
- He has purchased 5 times in the last 30 days so he is a frequent shopper
- He is a high value shopper as per the retailer's bench mark. His total purchases are worth 8700 Rs.
- He is a high volume shopper as well and based on his value and volume of purchase we can see that he buys less expensive products that are more of a necessity than luxury
RACHEL'S PURCHASE ANALYSIS :
- She is an old shopper who has not visited the supermarket in the past 15 days
- She has purchased only once in the last 30 days so she is a occasional shopper
- She is a high value shopper as per the retailer's bench mark. Her total purchase is worth 8700 Rs.
- She is a low volume shopper as well and based on her value and volume of purchase we can see that she buys expensive products that are more of a luxury than a necessity
Customers who have not purchased in the stipulated time period can be contacted by customer service manager or marketing team can mail the customer attractive promotions based on their historical purchases to resume trade.
NOTE : RFMV analysis is not a standardized analysis matrix in CRM. This is a customized version used by me in analyzing customer value. I feel this provides better depth into customer purchase pattern there by helping a retailer analyse his customer' value better
RFM analysis is done in order to understand the health of a customer's relationship with a retailer and the value of a customer to a retailer by analyzing the historical sales data for the respective customers. The outcome of this analysis is used in direct marketing, customer centric promotions and loyalty programs.
The modules used in the analysis are :
RECENCY :
How recent did a customer purchase ?
This explains when was the last purchase made by the customer, by this we can select the list of customers who has purchased from the retailer in the last 90 days or 180 days or 365 days.
FREQUENCY :
How frequently a customer purchases ?
This explains what is the frequency of purchases the customer makes with a retailer. This is calculated based on number of purchases made by a customer in the last 90 days or 180 days or 365 days.
MONETARY VALUE :
How much does a customer spend ?
This explains how much does a customer spend for a stipulated time period similar to the previous two scales. This also provides information on customer spend per purchase and over a period of time i.e. a customer might not make big shopping per visit but totaling their purchase over the same period of time will project a bigger purchase value compared to a customer who has done a single big purchase.
VOLUME :
How many products a customer has purchased ?
This is an add on to the RFM analysis which indicates how many products a customer has purchased or volume quotient of a customer's purchase pattern over a period of time. This in combination with Monetary value of the customer shows whether the customer purchases high value products or a general shopper buying small value products for regular use
SCALE OF MEASUREMENT
Recency, frequency and monetary bench mark scales may vary from retailer to retailer based on their product assortment and the lifespan of the product sold. Based on a retailer's product portfolio they can choose a scale based on a benchmark set for each of the module. We can look at how this can be done through a simple example :
A is a supermarket store having products in convenience, home appliances and grocery categories.
Sameer had purchased at A 5 times in the last 30 days as on 30-Nov-15, his last purchase was on 27-Nov-15. He has purchased for 1000 Rs., 2500 Rs., 1500 Rs., 1500 Rs. and 2200 Rs. respectively. Total quantity of products purchased by him is 20
Rachel had purchased at A only once in the last 30 days as on 30-Nov-15, her last purchase was on 2-Nov-15. She has purchased for 10500 Rs. Total quantity of products purchased by her is 1
Based on the above purchase data we can easily arrive at the below observation through data analysis:
Sameer and Rachel are both high value shoppers but there Is a lot in difference between the two which this analysis throws light on, that can help the retailer can use to arrive at customer centric promotion to increase sales and frequency of customer visits.
SAMEER'S PURCHASE ANALYSIS :
- He is a recent shopper who has visited the supermarket in less than 15 days
- He has purchased 5 times in the last 30 days so he is a frequent shopper
- He is a high value shopper as per the retailer's bench mark. His total purchases are worth 8700 Rs.
- He is a high volume shopper as well and based on his value and volume of purchase we can see that he buys less expensive products that are more of a necessity than luxury
RACHEL'S PURCHASE ANALYSIS :
- She is an old shopper who has not visited the supermarket in the past 15 days
- She has purchased only once in the last 30 days so she is a occasional shopper
- She is a high value shopper as per the retailer's bench mark. Her total purchase is worth 8700 Rs.
- She is a low volume shopper as well and based on her value and volume of purchase we can see that she buys expensive products that are more of a luxury than a necessity
Customers who have not purchased in the stipulated time period can be contacted by customer service manager or marketing team can mail the customer attractive promotions based on their historical purchases to resume trade.
NOTE : RFMV analysis is not a standardized analysis matrix in CRM. This is a customized version used by me in analyzing customer value. I feel this provides better depth into customer purchase pattern there by helping a retailer analyse his customer' value better
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