There's been much discussion around capturing and reporting reasons for churn. But, I haven't heard much about tracking churn back to ICP (Ideal Customer profile).
We've all had customers that should have never been sold our products. They're simply not a good fit and the effort it takes to make them successful sucks energy and resources away from customers that fit well.
I'm curious to know if anyone has looked at churn by relating it back to ICP to come up with a "% of ICP" as a predictor of churn, possibly bringing # of support tickets and average time to closure into play as indicators of % of ICP.
Here's an example.....bear with me here as I'm sure the formula could use tweaking....
The sales team closes a deal for ABC Company. The customer is a great fit for our products. During the first year, they open 4 support tickets but three of them are asking for enhancements/recommendations for new features. Only 1 ticket is a real support issue and is closed within the normal, average closure time (overall for the company).
The deal for DEF Company is sold at the same time. DEF has a few more issues but is a pretty good fit. During the first year, they open 27 support tickets all of which are for issues using the products. The average time to ticket closure is 2x the average time (measured against all tickets) - which indicates that their issues are likely not standard.
XYZ, also sold at the same time, has tons of issues and can't get anything to do what they want it to do. They average 1 ticket per week (52/year) and it takes the support team an average of 56 hours per ticket to close.
Using a formula like this: % of ICP = 100 - ((# support tickets per month/ave. overall support tickets per month) * (# hours [or days] to closure/overall ave. hours [or days] to closure))
If the average # of support tickets per customer per month is .7 and average time to closure is 16 hours per ticket:
ABC % of ICP = 100 - ((.33/.7) * (16/16)) = 100 - (.47 * 1) = 100 - .47 = 99.53%
DEF's % of ICP = ((2.25/.7) * (36/16)) = 100 - ((3.21 * 2) = 100 - 6.42 = 93.58%
XYZ's % of ICP = ((4.33/.7) (56/16)) = 100 - ((6.19 * 3.5) = 100 - 21.67 = 78.33%
This analysis would provide insight to Sales and Marketing to target more companies like ABC and DEF and stay away from companies like XYZ.
Is anyone doing anything like this?
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