Calculate Churn Rate in Redshift

Making the most of your
Redshift
data

What is Churn Rate?

Churn rate is the percentage of customers who stop using your product or service during a certain time period.

Churn rate is calculated by dividing the number of customers you lost during that time period by the number of customers you had at the beginning of that time period.

For example, if you start your quarter with 400 customers and end with 380, your churn rate is 5% because you lost 5% of your customers.

Obviously, your company should aim for a churn rate that is as close to 0% as possible. In order to do this, your company has to be on top of its churn rate at all times and treat it as a top priority.

Churn rate is a very important metric because it is the most direct way to determine how successful your company is at retaining customers.

How do you calculate Churn Rate in Redshift?

It can be difficult to calculate Churn Rate directly inside of Redshift; that's where Causal comes in.

Causal is a modelling tool which lets you build models on top of your Redshift data. You simply connect Causal to your Redshift account, and then you can build formulae in Causal to calculate your Churn Rate.

What is Causal?

Causal lets you build models effortlessly and share them with interactive, visual dashboards that everyone will understand.

In Causal, you build your models out of variables, which you can then link together in simple plain-English formulae to calculate metrics like Churn Rate. This makes your models easy to understand and quick to build, so you can spend minutes, not days, on your models.

A comparison of formulae in Excel and Causal

When you're done, you can share the link to your model with stakeholders. They'll be able to view your model's outputs in a visual dashboard, rather than a jumble of tabs and complex formulae. The dashboards are interactive, letting viewers tweak your assumptions to see how they affect the model's outputs.

Causal lets you add visuals in a single click, letting you plot out graphs and distributions for metrics like Churn Rate.

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Start building your own Churn Rate models, and connect them to your Redshift data.

How to Calculate Churn Rate in Redshift

Making the most of your Redshift data

What is Churn Rate?

Churn rate is the percentage of customers who stop using your product or service during a certain time period.

Churn rate is calculated by dividing the number of customers you lost during that time period by the number of customers you had at the beginning of that time period.

For example, if you start your quarter with 400 customers and end with 380, your churn rate is 5% because you lost 5% of your customers.

Obviously, your company should aim for a churn rate that is as close to 0% as possible. In order to do this, your company has to be on top of its churn rate at all times and treat it as a top priority.

Churn rate is a very important metric because it is the most direct way to determine how successful your company is at retaining customers.

How do you calculate Churn Rate in Redshift?

It can be difficult to calculate Churn Rate directly inside of Redshift; that's where Causal comes in.

Causal is a modelling tool which lets you build models on top of your Redshift data. You simply connect Causal to your Redshift account, and then you can build formulae in Causal to calculate your Churn Rate.

What is Causal?

Causal lets you build models effortlessly and share them with interactive, visual dashboards that everyone will understand.

In Causal, you build your models out of variables, which you can then link together in simple plain-English formulae to calculate metrics like Churn Rate. This makes your models easy to understand and quick to build, so you can spend minutes, not days, on your models.

A comparison of formulae in Excel and Causal

When you're done, you can share the link to your model with stakeholders. They'll be able to view your model's outputs in a visual dashboard, rather than a jumble of tabs and complex formulae. The dashboards are interactive, letting viewers tweak your assumptions to see how they affect the model's outputs.

Causal lets you add visuals in a single click, letting you plot out graphs and distributions for metrics like Churn Rate.