How to Learn from Churn
Transcript
So, I was in a board meeting today and when the slide came up that described what was going on in churn, one of the other directors said, oh, churn, I love churn.
And I was shocked. I looked at him through Zoom and he said, no, no, no, no, no. I don't mean I love churn. I mean, I love analyzing churn because there is so much leverage in churn, reducing it, and there is so much to learn from those customers that leave you.
There's so much to be learned from churn.
And I thought that was an amazing way at looking at it, and I wanted to share that with you today.
So a couple of things.
The first thing I want to share with you is how much leverage there is in churn.
So to demonstrate, I've created two companies, one that I'll call the red company and one that I'll call the blue company.
The blue company has twice as much churn as the red company.
Everything else is the same in the model I created.
They each get 50 customers per month.
They each charge $100 per month per customer, and I've just mapped out the first 100 months of their existence.
So here's what the blue company's revenue looks like over time.
You can see that it goes up, up, up, up, up, and then it starts to flatten out.
And the reason it flattens out is they lose as many customers as they're gaining at some point.
And now the red company, which has half the churn rate, spends the same on marketing, has the same revenue, the same 50 new customers a month, has that much extra revenue.
It's double.
The area under the curve is the revenue that you have acquired.
And literally look at it.
It is 2x.
This is how much leverage there is in churn.
If you cut your churn in half and everything else stays the same, ultimately you will have twice the revenue.
Each customer will stay twice as long and pay you twice as much.
Wow.
So there's leverage in churn.
So where's the learning in churn?
So one of the things that I love to do is understand why my customers leave.
Here's a simple example of a pie chart.
It's an example or a representation of the various reasons people leave.
In this case, you can see that the big orange section is the one that we've got to tackle.
If we can start solving that problem for our customers, fewer customers will leave and
our churn will go down.
I want to give you a real life example from one of our other companies.
I don't know if you have all understood or seen how to build a cohort chart, but a cohort
chart is where you take groups of customers, cohorts, and group them by some logical thing.
In this particular case, I'm going to show you it's going to be by what month they actually signed up for service.
And here it is.
Now, if you look at that chart, it's hard to read all the little numbers I know, but it's color-coded so that you can see.
Green is good.
Orange and red are bad.
Now, at the very top of the chart is the cohort from July of 19.
Those are the customers who came in July of 19, and you can see that they get red over time.
I'll tell you the answer, that after 12 months, only 43% of those customers are still with them.
If you read down vertically in that column, you'll see that on average, 12 months, roughly 50% of the customers were staying.
That's not good.
The average customer, only 50% of them stay after a year.
It's not a very good lifetime value.
Then they started experimenting.
They used the pie chart.
They understood what was going wrong, why people were leaving, and they changed some things in their business.
and you'll see in the section there's three months so for a quarter for 120 days they did an
experiment where half of the customers were treated one way and half of the customers were
treated the other way and you'll notice that it's a lot greener in that middle section as a matter of
fact it doesn't go quite out to 12 months but it looks like the blended average will end up being
about 75 after 12 months not 50. so they said wow this new version this is way better and so they
switched to 100% the new version below the second black line, and you can see that it's all green.
As a matter of fact, in month eight, the first cohort still has 95% of its users as customers
after eight months. The projection is that after 12, they'll still be north of 90%.
percent so that means churn has gone from roughly 50 percent to roughly 10 percent per year
a 5x change in churn well if you remember my first graph the red and blue a 2x meant twice the revenue
5x would mean five times the revenue hopefully this gives you a little bit of insight into why
churn is so important in your business hopefully you've seen a couple little tools cohort analysis
little pie chart projections that will help you think about churn differently and hopefully you
will be able to reduce churn in your business and grow your revenue faster helping you be more
successful
There is so much to learn from churn! In today’s MATH 101 video, Troy shares some great tools and analysis that you can use to think about churn as an opportunity to learn from your customers and grow your revenue faster.
Originally published on the MATH Venture Partners blog.