Think $1M ARR Means You’ve Achieved Product-Market Fit? Think Again.

May 4, 2023

I’ve had countless conversations with founders about product-market fit. Through the course of these conversations, I run into a lot of misguided advice about growth, efficiency, and the implications of both on a company’s ability to raise a Series A.

Here’s a popular growthism that fell out of fashion during the 2020-21 boon, but seems to be making its way back into the fundraising zeitgeist:

“Series A is the product-market fit round. $1M in ARR is the magical number that equals product-market fit. Therefore, I need to hit $1M ARR to raise my Series A.”

It’s an enticing idea, but it’s shallow thinking at best. Having evaluated hundreds of companies for signs of product-market fit, I can assure you that there’s no specific revenue benchmark that definitively signifies product-market fit.

On the other hand, some say product-market fit is a feeling—a “you’ll know it when you see it” milestone. While I agree with the ethos of that statement, I believe there are concrete signals of product-market fit available beyond gut feel.

Here why $1M in ARR means very little to me as an investor, and the frameworks that I personally use to assess product-market fit at the Series A.

Infographic listing out Kaitlyn Henry's tips on finding product-market fit as a founder.

Revenue growth doesn’t tell the full story

Let’s say you go from $0 to $1M ARR in your first year of sales, which is a line that I’ve seen on more pitch decks than I care to count. It’s no small feat, so that must be a sign that you’ve achieved product-market fit, right?


Hitting $1M ARR can be accomplished under a variety of conditions:

  • Is it one customer paying you $1M, or one million customers paying you $1?
  • How much money did you spend to acquire those customers?
  • How similar or different are those customers from one another?
  • How many of them were design partners, and therefore paying for a product that was effectively custom-built for them?
  • How many were from your personal network?
  • What was the macroeconomic environment at the time? How constrained were budgets?

When you start to ask these questions, it becomes clear that there are better indicators of product-market fit to consider than just revenue. Here are three things that I personally look for when evaluating companies for product-market fit:

1. Your feedback between customers is consistent.

Plenty has been written about a product’s ability to elicit an emotional response from customers as a signal of product-market fit, which I agree with.

However, when I talk to customers and prospective customers of a product, I key off of the consistency of the feedback from one customer to the next just as much as, if not more than, the emotional response.


Ultimately, product-market fit means a group of people have the same problem, and your product solves it.

If there’s inconsistency in how customers and prospects describe the problem, you may lack a clear market opportunity.

If there’s a consistent problem, but inconsistent or unspecific plans to use your product to solve that problem (for example, it requires meaningful customization or human intervention) your product may not actually solve the market’s challenges. The same is true if customers have a lot of variation in what features they find the most valuable.

In both cases, it’s not clear that there’s a path to product-market fit.

There are some very horizontal, flexible products, like Airtable, that buck this trend. But, for most product markets, consistency of feedback between customers can be a great North Star for validating product-market fit.

The takeaway

For founders who are still finding product-market fit, my recommendation is to be ruthless about what patterns you are and aren’t seeing among customers, and let those patterns guide what you build.

For founders who believe they have found product-market fit and are raising a Series A, expect VCs to call your customers and pitch prospective customers to assess product-market fit. Make sure that the way you pitch the problem reflects the consistent patterns you’ve seen in the market and therefore what you expect customers and prospective customers will share.

2. Your ideal customer profile is specific and multi-dimensional.

Product-market fit is inherently tied to an ideal customer profile (ICP). It’s a repeatable value proposition (i.e., product) demonstrated across a defined persona (i.e., market).

Often, founders default to describing their ICP with demographic information, like industry or company size. Sometimes those are fair indicators of whether a customer has the problem that your product solves, particularly with more vertical solutions.

However, I’ve noticed that founders with really strong product-market fit often define their ideal customer profile in much more specific, multi-dimensional terms.

For example, in the early days of Datadog’s growth, the company didn’t see any strong trends in the classic demographic data points. When they dug deeper, they realized that there was a specific technology indicator that was much more important to identifying their ICP: the use of Chef.

At the time, Chef was a popular DevOps automation company, and using it indicated that the company was interested in adopting DevOps-friendly tools more broadly. Companies using Chef converted at much higher rates, much faster, and expanded more once sold.

The takeaway

Try to think outside the box when looking for patterns in why certain customers love your product and others don’t. If you’re fundraising, share those patterns with prospective investors so that they don’t misjudge product-market fit by pitching your product to someone too far outside your ICP.

3. You’ve got a significant number of users in your ideal customer profile.

Sample size matters. Do you have a significant number of customers that you’re deriving your signals on product-market fit from?

One of my biggest issues with VCs using ARR thresholds as investment criteria for a Series A check is that it implies that the composition of that ARR doesn’t matter. I’m ultimately looking for a pattern, and will take a high customer count and low ARR over the inverse any day.

The takeaway

In general, the more potential targets there are in your market and the greater the variability between targets, the more data points you need to draw conclusive patterns. It’s hard to offer exact benchmarks, but here are some loose rules of thumb for the typical customer count numbers I look for at Series A:

  • For PLG companies with tools for individual users, I look for thousands of users, not hundreds. You don’t have to be monetizing, since usage data can also tell a story of product-market fit. However, you’ll have more data points on the strength of your value proposition and resulting market opportunity if customers are paying.
  • For products serving enterprise customers, I look for at least 8 to 12 customers.
  • For products serving mid-market/SMBs, I look for at least 20 customers.

Advice for founders on finding and communicating product-market fit

  1. Start from a repeatable problem. Don’t build a solution before you have a clear understanding of the problem and how the problem manifests across different types of customers. The clearer you are on the problem, the easier it will be to design a solution.
  2. Be open to the commonalities that make users love your product. You might go in with preconceived notions about what problem your product solves, and for whom. Look to usage and customer data to validate where the market pull is actually coming from.
  3. Remember, product-market fit is more than just revenue. Be wary of using hard-and-fast rules or a singular metric as a sign of product-market fit when pitching investors. Use multiple qualitative and quantitative data points to share your story on product-market fit, and share the patterns you see between them.

Some of the companies mentioned in this blog are current and former OpenView portfolio companies. For a full list of our portfolio companies, visit our Portfolio page.

Vice President at OpenView

Kaitlyn is responsible for identifying, evaluating and executing on investment opportunities. She also manages OpenView’s diversity, equity, and inclusion initiatives. Prior to joining OpenView, Kaitlyn worked at Amazon across multiple growth and business development roles. Most recently, she was a senior financial analyst for Amazon’s machine learning group, where she oversaw Amazon’s consumer engagement ML projects worldwide. She has also served as an advisor to early-stage technical founders through her work at the Cal Poly Center for Innovation and Entrepreneurship, an accelerator in Central California.