- Among the first wave of punk rock bands
in the 70s was The Clash.
Do you remember that lyric,
should I stay or should I go?
That line predicted one of the most challenging measures
for business.
It's called customer churn.
Customer churn is serious business.
It's a measure of how many customers stop using your product
or your service.
In order for a company to grow,
new customer acquisition has to exceed churn.
It's just that simple.
We spend lots of time and lot of attention
attracting new customers,
but it's active customer engagement
where brands convince their customers
if they should stay or they should go.
Imagine you have a bucket that you're trying to fill
with new customers.
With churn you have a leaky bucket,
and some of those customers are falling out.
In the financial services sector the average churn rate
is 15%.
In telecommunications, where there's more consolidation,
the average rate is around 1%.
Either way, these numbers mean lost revenue.
But there's hope.
Machine learning can help you to tune into
why your customers churn.
This way you can patch up that leaky bucket.
How do you do it?
The scientific method is always a good place to start.
So establish a hypothesis.
Why do you think customers move to competitors,
or forego the use of your category all together?
Then gather as much data as you can.
Some examples of what creates churn are,
price to value ratio, a bad customer support experience,
or a disruptive product or service
might turn the market completely upside down.
That's what happened to the taxi business
when Uber came on the scene.
If you can properly operationalize this sort of prediction
then you're user experience systems can be deployed
to help reduce the impact.