The question is not, “Does the company have product/market fit or not?”. The more appropriate question is, “What’s the strength of the company’s product/market fit?”

This editorial is a continuation of a piece I wrote last year titled, Product/Market Fit as a Service.

In the first few weeks of the stay at home orders across the country, I spoke with a number of co-founders and executives who decided to make a pivot to solve very unique problems during the pandemic. Each proposed solution sounded very viable and each leader had big visions of what solving the problem would mean for their business and for humanity. 

My conversations were a microcosm of what much of the startup world was experiencing. A survey conducted in April 2020 by NfX found 36% of startups pivoted their product due to COVID-19 and that 62% of those pivots were moderate to significant.

I checked in with each of them around mid-June (about 3 months later) and each one of their pivots turned out to be a failure. Failure because they scaled sales & marketing investments before product/market fit (PMF) was strong enough.

They got excited about the first customer wins, the size of the market, inbound inquiries, and other key performance indicators so they doubled-down. They thought they had product/market fit, and that might have been true. . .to an extent.

So this got me thinking. . .

Product/market fit is not binary. It’s a spectrum. It’s a continuum.

There is a wide range of content on the topic of PMF from preeminent thought leaders such as Steve Blank, Paul Graham, and Sean Ellis who talk about “finding product/market fit”, but few describe PMF as a spectrum or how to strengthen it.

Slides 16–20 by Pete Kazanjy is a good start, but strengthening PMF should not just be a focus for early-stage companies, but also for more mature companies because PMF is a continuum. In the pursuit of continuous improvement, there’s always an opportunity to strengthen PMF.

This is an important distinction because the way a company operates needs to revolve around the strength of PMF (how and when to adapt product roadmap, who and when to hire, where and how to spend marketing investments, etc). “Finding” early PMF is the responsibility of the CEO/co-founder, but strengthening it needs to be a shared responsibility across functional leaders of the company.

What if we could measure the strength of a company’s PMF from a 1–10? If we could, what would we do once we have an answer? How could internal and external stakeholders take action on these insights?

Let’s first think about what a typical Enterprise SaaS company that scores a 10 on PMF would look like:

1. An executive buyer takes a meeting the next day after just one cold email
2. Six-figure deals with marquee brands are closing in less than three months
3. The buyer makes a purchase regardless of the price

Now, let’s think about what a typical Enterprise SaaS company that scores a 1 on PMF would look like:

1. 3% close rate because of steep competition and losing on price
2. 100% churn rate
3. High CAC

Creating the scoring system

It’s about creating a matrix with various weighted criteria that calculate a final score.

Example of possible metrics to calculate a score:

• Number of annual agreements paid up-front
• Percentage of AEs exceeding quota
• Close rate
• Average length of the sales cycle to close $100k+ deal
• Price elasticity
• CAC
• NPS

This Linkedin post by a VC describes metrics that should dictate the strength of a company’s PMF.

You could validate the PMF score by inputting data from dozens of startups that agree to share their metrics anonymously. The crowdsourcing of data will also create benchmarks for each metric. For instance, the industry average for close rate for an Enterprise SaaS company that sells MarTech to retailers.

So let’s say through a diagnostics process, we determine a company scores a 7 on the PMF scale and the conclusion states that the company can reach a 10 if they reduce CAC by 20% and improve close rate by 10%. These insights help the executive team determine what to prioritize. The executive team takes these actions because through benchmarking, the score proves that companies that score a 9 or 10 outperform their peers by X and have a Y% success rate of a successful exit. With more data and constant validation, confidence in the insights goes up.

Benchmarking metrics across other startups will be the key to pulling this off, but also presents the biggest challenge for a few reasons:

1. Requires a trusted third-party to do the benchmarking
2. Requires the willingness of founders to share their data (even anonymously)
3. Requires a large enough sample size of other companies in the relevant comparison set for rich benchmarking data

Additional thoughts about how the score could be used. . .

How do companies with stronger PMF outperform their peers with weaker PMF?

Could the score be used as a signal to key potential hires and investors?

Could execs or founders use the score to make better decisions around hiring or other investments?

Do VCs already use a matrix like this that’s more sophisticated? It’s unclear if even large, well-renowned institutions like Y Combinator use benchmarking data this granular.