What is Customer Lifetime?
Customer lifetime is the amount of time that a customer uses your product or service.
Customer lifetime can be calculated in a few different ways. The first is by taking the average time that a customer has been using your product or service. This is calculated by dividing the total number of months that your customers have been using your product or service by the total number of customers you have.
Another way to calculate customer lifetime is by taking the average time that a customer has been a paying customer. This is calculated by dividing the total number of months that your customers have been paying customers by the total number of paying customers you have.
The third way to calculate customer lifetime is by taking the average time that a customer has been a paying customer and subtracting the average churn rate. This is calculated by dividing the total number of months that your customers have been paying customers by the total number of paying customers you have minus the churn rate.
For example, if you have 100 customers and your churn rate is 5%, your customer lifetime is (100 x 12) / (100 - 5) = 120 months.
How do you calculate Customer Lifetime in Expensify?
It can be difficult to calculate Customer Lifetime directly inside of Expensify; that's where Causal comes in.
Causal is a modelling tool which lets you build models on top of your Expensify data. You simply connect Causal to your Expensify account, and then you can build formulae in Causal to calculate your Customer Lifetime.
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 Customer Lifetime. This makes your models easy to understand and quick to build, so you can spend minutes, not days, on your models.
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 Customer Lifetime.
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