Startup Metrics: A Comprehensive KPI Cheatsheet for Founders

Experienced entrepreneurs know that building successful products is not about damn luck or revolutionary ideas; it’s more about data-driven decisions and careful analysis of the market. There are plenty of books and posts written on this topic, but it still takes days of research to create some sort of a mindmap and understand startup metrics and their common use cases. 

This post is a quick cheatsheet for entrepreneurs to determine what metrics may be useful to their business.

Let’s assume that we are building a social network, and our business model is ad-based. The longer the average session duration in our app, the better, right? It means that our ads will get more impressions from our app users. 

But what if we are building not a social network but a search engine? Is it a positive change when the average session duration grows? Or it means that our UX is getting worse, and users have to spend more time looking for the requested information? 

And lastly, what does the average session duration mean for a SaaS application like GitHub or Slack?

Sometimes, a metric has opposite meanings for different business models.

In order to provide you with any recommendations, we need to find similarities in startup businesses and group them up somehow. I believe that the two main factors that define what metrics are more important for your business are:

All business efforts are always focused on the only goal: maximizing profits. A business model defines revenue sources and all the financial metrics that depend on them.

In the beginning, every startup faces an atmosphere of extreme uncertainty. As it progresses over time, it validates business hypotheses one by one and picks more and more precise metrics to rely on.

To read further about metrics, make sure you know about different business models and startup stages, and can define yours.

Note: In terms of this post, we don’t discuss the business models that assume uneven revenue distribution across customers. If your business provides a service to one or two big clients that generate 80% of your revenue, data analysis won’t be as helpful as for the business models above.

It’s important to understand that you can track both absolute values of a metric, as well as its change compared to the previous period of time when it makes sense. When you need to see how a particular metric grows over time, you can use Month-over-Month (MoM), Quarter-over-Quarter (QoQ), or Year-over-Year (YoY) metrics. 

The most common use of this type of metric is Revenue Growth, which is featured in the table below, but you can apply it to any other metric when it makes sense. To give you an example, let’s compare Total Revenue vs. Monthly Revenue vs. Month-over-Month Revenue Growth of a company below.

Here is the graph of its Total Revenue:

Looks good, right?

Seems like the company is growing. But wait a minute, this is the Total Revenue Graph, which adds up new revenue on top of what they already earned. To be honest, the Total Revenue graph doesn’t make a lot of sense and is used pretty often to impress prospective investors. Let’s review the Monthly Revenue graph of the same company:

Monthly Revenue vs. Month

Seems like it’s not that good. What experienced entrepreneurs and investors pay more attention to is the Month-over-Month (MoM) Revenue Growth graph, and for this company, it looks like below:

Revenue Growth vs. Month

This MoM Revenue Growth graph clearly shows that the company stopped growing. Moreover, it started shrinking. This may indicate that its business model doesn’t scale well.

Tracking a whole bunch of different metrics is difficult, especially when each one of them has its own priority, and your team is growing quickly. That’s why many businesses try to measure their success/growth using just one metric and call it North Star Metric (NSM). Here are some notes about using NSM based on our experience:

Having NSM in place makes the decision-making process much easier. Ex: If a new feature doesn’t drive NSM growth, it means that your team should stop working on that. It’s pretty difficult to define a single metric that would capture your business goal. That’s why you need to readdress your NSM once in a while, especially when shifting your business priorities. NSM can be a complex metric (ex., custom formula), and almost always, it’s different from business to business. Sometimes companies pick a basic metric as their NSM so their team can understand it easily.

Here are some good NSM examples:

If you’d like to learn more, have a look at an example of how Thoughtbot calculates its NSM. 

This is the type of metric that indicates where your business currently stands. Usually, financial metrics are not actionable but show your business's overall success/potential.

These are the type of metrics that indicate how fast your business grows. With the help of these metrics, you can project your financial KPIs.

It’s important to note that all the acquisition metrics are usually tracked against different acquisition channels (blended, organic, paid marketing, etc.) to determine the most effective ones.

If acquisition metrics show how fast you grow, then engagement metrics indicate the quality of your growth and the value you bring to your customers. Sooner or later, every business starts to focus on these metrics, especially in a competitive market.

These metrics can be a good start for measuring your startup success, growth, and value. Choose your metrics wisely and reconsider them every time you change the strategy or move to the next stage. While you grow and gain more experience, you will create the whole economy for your product with built-in custom metrics. 

When performing a user engagement analysis, every business is trying to define their “sticky criteria” – an action or a sequence of actions that, with high probability, will convert a user into a long-time customer. Having a defined sticky criterion, you can focus on your marketing efforts and rebuild the UX of your product to inspire your users to follow that lead.

In our next post, we’ll describe how Cohort Analysis works and how it helps define your sticky criteria and make user engagement forecasts.