We were slow tonight, so I decided to do some data analysis on my customers.
I track customers using their Lifetime Value (LTV) since that’s what really matters… how much will the customer make me over the course of their lifetime. I calculate this using the customer’s average gross margin per order, their frequency and how much their discount usage costs me.
I did a regression analysis using those factors against their LTV to find out which factor is most important.
What do you think it was?
Frequency won by a LONG SHOT. I expected it to be the most important factor, but not by as much as it is. The customer’s frequency explains 78% of my customer’s LTV! Discounts used only came in at 3%, and average ticket was 8%.
For every 10% in discounts given (as a percentage of sales price) I lose about $20.20 in LTV, but for every 0.1 additional orders per month I gain $75.10.
Interesting insights are sometimes gained from being slow.
I track customers using their Lifetime Value (LTV) since that’s what really matters… how much will the customer make me over the course of their lifetime. I calculate this using the customer’s average gross margin per order, their frequency and how much their discount usage costs me.
I did a regression analysis using those factors against their LTV to find out which factor is most important.
What do you think it was?
Frequency won by a LONG SHOT. I expected it to be the most important factor, but not by as much as it is. The customer’s frequency explains 78% of my customer’s LTV! Discounts used only came in at 3%, and average ticket was 8%.
For every 10% in discounts given (as a percentage of sales price) I lose about $20.20 in LTV, but for every 0.1 additional orders per month I gain $75.10.
Interesting insights are sometimes gained from being slow.
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