Why Net Revenue Retention (NRR) is so important for your startup

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When Quibi shut down on the first of December, 2020, nobody was surprised. It had been a long time coming.

The app, which aimed to be a mobile-first, bite-sized streaming platform (a Netflix-meet-Tiktok sort of thing), had big aspirations. It raised $1.75B from investors, signed stars like Kate Hudson and Jennifer Lopez, and elbowed its way into popular media discourse. If you were online for 2019 and 2020, you probably heard about Quibi.

Everything went well until it didn’t, and Quibi announced it’d be shutting down. Let’s not oversimplify the problem: Quibi shut down for many reasons, one of which is that the idea didn’t really resonate with people. The app didn’t find product-market fit.

But Quibi’s failure is also an example of why one metric—Net Revenue Retention (NRR)—is so important. Why?

  • Quibi had high user churn. People didn’t really “get” Quibi. The content format didn’t click, and many customers became not-customers very quickly.
  • Quibi couldn’t convert free trials to paid users. Estimates show that Quibi only converted about 8% of its initial free trial users to paid subscriptions. And many of those 8% probably didn’t stick around for too long.

These two problems mean one thing: Low revenue growth from existing customers. And that’s the exact problem (or opportunity) that the NRR metric tracks.

In short, NRR is so important because it’s a marker of whether your business is positioned for sustainable growth. NRR tells you, as a percentage, whether or not you’re increasing revenue from your current customers.

Below, we’ll cover how to calculate NRR (it’s easier than it seems), what your NRR says about your business, and how startups with usage-based pricing models can use NRR.

How to calculate your NRR in 30 seconds flat

To calculate your NRR, you’ll need the following:

  • Initial MRR (your MRR at the beginning of the period you’re measuring)
  • Churned MRR (how much MRR your startup lost due to customer churn)
  • Expansion MRR (how much MRR you gained from current customers upgrading)
  • Contraction MRR (how much MRR you lost from current customers downgrading)

Once you’ve got these, calculating your MRR is simple:

((Initial MRR - Churned MRR) + (Expansion MRR - Contraction MRR)) / Initial MRR x 100

For a practical example, imagine your startup does $25k per month. Here’s what the formula might look like (with some sample numbers as placeholders):

((25,000 - 3,000) + (4,500 - 1,000)) / 25,000 x 100 = 102%

With these numbers in the formula, your startup’s NRR would be 102%. But is that good? Bad? Other? Let’s cover what different NRR numbers actually mean.

What is a good NRR?

Let’s diagnose your NRR. Keep in mind that what constitutes a “good” NRR depends on your startup’s business model. But, here are some guidelines:

  • Below 100% = This is a bad NRR (in most cases). It means you’re losing revenue from your existing customers. So, while your overall MRR could still be increasing, your current customers are either dropping off or downgrading your product. Long-term, that’s worrying. So, if your NRR is below 100%, it’s a good sign that something about your business model isn’t very sustainable. Relying solely on new customer acquisition to increase MRR is rarely a good idea.
  • Above 100% = Good news—you’re increasing revenue from your existing customers. Anything over 100% is reason to be happy, because it means your customers are enjoying the product so much they’re spending more over time.
  • Above 120% = This is an especially good NRR. If your number is this high, you’re likely adding lots of additional value—with new pricing options or product features—that’s causing your customers to want to spend more, month-after-month.

If you’re a startup with a traditional pricing model, that’s about it. But what about NRR for startups who have usage-based pricing models? Is it any different for them?

Calculating NRR for usage-based startups

The good news? If you have—or want to have—a usage-based pricing model, calculating NRR is, more or less, the exact same formula as the one further up in this article.

There are a few problems, though.

The main issue is that MRR as a metric doesn’t really work for usage-based pricing startups. It’s typically calculated based on fixed subscription revenue. So if you’re billing customers based on how much they use, you’ll never be able to calculate a perfect MRR.

At Octane, we prefer a different metric: MRRU (monthly recurring revenue usage). This smooths out your fluctuating usage data into a predictable metric. It works with some rather tricky math, but in simple terms, it looks at historical usage data for customers and takes a reasonable guess at what usage will be in the future.

If you want to calculate your NRR, you’ll have to use similar, MRRU-like calculations (which you can read about here) to figure out how much MRR churn, expansion, and contraction you received in the past month. From there, you can calculate an estimated NRR.

If you don’t want to do any of that complicated math, you can do some napkin-math and take your best guess at rough averages based on recent usage data. You won’t get a perfect number, but it can be a place to work from.

Bottom line? Calculating NRR isn’t impossible for usage-based startups, but if you want to do it right, you’ll have to do a little bit of math. Otherwise, you can take your best guess.

Want to make your usage-based pricing calculations easy? Try Octane

If you’d rather not spend hours doing math to calculate important metrics for your startup, you might want to chat with us at Octane. We make it easy for startups to implement usage-based billing by eliminating the months-long engineering slog it usually takes to get it going. And we help startups with calculations for important metrics, like MRR & ARR.

Chat with us to 1) implement usage-based pricing in just a few clicks, or 2) make your current usage-based pricing workflows a hell of a lot easier. No engineering resources required.

Book a short, free 1:1 and learn more about Octane.

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