SaaS metrics – A guide to measuring and improving what matters

Here's a bit of gold - (though it seems like an obvious duh to me now) he recommends creating a Customer Engagement Score. Essentially, you allocate more points for the features that seem the most sticky. Later, go back and look at the customers who actually churned, and validate that you picked the right features as a predictor of who would churn.

This is a good way to discover which types of use are the best indicators of possible upsell or potential churn. (As the author mentioned, HubSpot did this and called it their CHI score or Customer Happiness Index. It evolved to be a very good predictor for churn.)

Well-explained! It is very useful to understand and monitor your KPIs.
Here are 'Leads by lifecycle stages': It helps businesses get insight into their customers’ purchase cycle by understanding where they are in the buying process. It also provides valuable insight into the customer journey, helping companies gain a deeper understanding of where potential customers are in the buying process and what actions they take at each step along the way.

Read more: https://blog.saasmantra.com/5-metrics-for-saas-business/

Activation rate is arguably the most important SaaS metric of them all. This especially rings true in a product-led growth model, where the in-app user experience becomes a driving force for improvement.

The activation rate is the percentage of users who complete their first trial or purchase. It is a key measure of both sales and user engagement, enabling you to identify areas for improvement before your customers even have their first trial.

This post is aimed at helping SaaS executives understand which variables really matter, and how to measure them and act on the results.

He calculates the common life of a client as 1 divided with the aid of using the client churn price. Ie. when you have a 20% annual client churn price, your common client lifetime is five years (1 divided with the aid of using 0.2 = five)...I do not see the common sense right here though, can a person explain? Does this now no longer anticipate a strong churn price each year

LTV – the Lifetime Value of a typical customer
CAC – the Cost to Acquire a typical Customer

From reading/watching founder interviews, it all seems to come down to this.

If your CAC is low, sure, you can afford a low LTV. You'll be net-positive.

But if your CAC is high, either price high or make sure people stick.

Curious to hear your thoughts on this.

I can attest to the power of negative churn. While it depends on your product, upselling and cross-selling existing customers can be way easier that finding new ones. And usage-based pricing can have the same effect (though it can also go the other way, so be careful!).

I also liked what he said about predicting churn by tracking engagement. But you have to be really careful with that, because tracking can quickly cross ethical lines.

He calculates the average lifetime of a customer as 1 divided by the customer churn rate. Ie. if you have a 20% annual customer churn rate, your average customer lifetime is 5 years (1 divided by 0.2 = 5)...I don't see the logic here though, can someone explain? Does this not assume a stable churn rate every year?

Getting paid in advance is something that keeps coming up in conversation - is this standard practice for most tech freelancers? How do you justify wanting to be paid in advance to your clients? Besides cash flow, I see the benefit to you of customers being more committed to the service (thereby potentially lowering churn rate), but is it not a big ask when they don't know the quality of your work?