After learning what is the RFM algorithm in my previous post. I would like to write today about my adjustments and improvements that helped me during these years as an online marketer.
I call it the Priority Potential RFM or just PPRFM.
P - Priority (who to contact first)
P - Potential (predicting lifetime value of a customer)
R – Recency (most recent deposit)
F – Frequency (how many times he deposited)
M – Monetary (Lifetime Deposits)
Please see attached diagram I did for an online gaming company:
Remember we are not changing the RFM algorithm we are just adding two new add-ons.
Priority - everything should work according to who do you want to contact first and why.
Potential - In this section you put everything you think might influence a customer to buy your product or not. It can be various things so try to think what are the characteristics that differentiate your purchasing customers from your customers that still didn't buy from you and segment them according to that.
For example your database shows that Americans ages 45 - 62 have high life expectancy and high CLV. So when a new customer from Arizona, USA, age 61 signs up with you. He will have higher potential than a customer from Arizona, USA age 24.
Remember that every customer has the potential to spread your story to other potential candidates, so treat them with respect and segment them correctly.
I call it the Priority Potential RFM or just PPRFM.
P - Priority (who to contact first)
P - Potential (predicting lifetime value of a customer)
R – Recency (most recent deposit)
F – Frequency (how many times he deposited)
M – Monetary (Lifetime Deposits)
Please see attached diagram I did for an online gaming company:
Remember we are not changing the RFM algorithm we are just adding two new add-ons.
Priority - everything should work according to who do you want to contact first and why.
Potential - In this section you put everything you think might influence a customer to buy your product or not. It can be various things so try to think what are the characteristics that differentiate your purchasing customers from your customers that still didn't buy from you and segment them according to that.
For example your database shows that Americans ages 45 - 62 have high life expectancy and high CLV. So when a new customer from Arizona, USA, age 61 signs up with you. He will have higher potential than a customer from Arizona, USA age 24.
Remember that every customer has the potential to spread your story to other potential candidates, so treat them with respect and segment them correctly.
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