Quantifying Product Market Fit

by Sean Jackson

A formula for determining Product Marketing Fit (PMF) for early stage startups.

As a startup founder actively raising capital, one of the most common questions I hear when talking with potential investors is product market fit; do you have it or not?

When asked to clarify what they mean by product market fit, most investors provide a heuristic answer, citing some scenario when demand exceeds the supply of time and resources a startup has available.

But not once have I heard an actual metric for it.

As one investor told me when I asked for more detail on how a startup can demonstrate product marketing fit, his response was simple, “I know it when I see it.”

Really? That’s the answer?

In an environment where almost everything in a startup is quantified, the best way to understand product market fit is based on some unquantifiable observational data?

Or put another way, is the best definition for product market fit analogous to the way Supreme Court Justice Potter Stewart defined porn

I think there is a better option.

So on a three hour drive from Austin to Dallas, I considered the question of how to quantify product market fit.

After several iterations, I developed a formula.

Defining The Input Variables

To construct the formula, I considered all the input variables available within a business, based on my personal experience starting and exiting four startups, and narrowed it down to three factors.

Product market fit changes over time. Products can improve or become worse. So can markets. Hence any calculation needs to review product market fit over a defined period.

Customer Acquisition Cost (CaC)
Every business must communicate the existence and benefit of their product/service to potential customers. And the costs of this activity can be quantified as Customer Acquisition Cost or CaC.
Even if you send out one Tweet and get a million people to sign up on your landing page, there is still a quantifiable cost for this effort.
Hence I included this variable since it is a common component of placing a product into a market.

North Star Metric (NSM)
Every early stage company has a core metric that defines, or predicts, product success. 

“A North Star metric is the one measurement that’s most predictive of a company’s long-term success. To qualify as a “North Star,” a metric must do three things: lead to revenue, reflect customer value, and measure progress. If a metric hits those three points, and every department contributes to improving it, the company will grow sustainably—or so the theory goes.”
- Matt Simborg, MixPanel

I love this definition.

But what’s missing is the actual metric to measure for the purpose of a Product Market Fit formula.

So I defined a few of the more common ones that startups use.
  • Total Signups
  • Total Active Users (DAU, WAU, MAU)
  • Total Transactions (daily, weekly, monthly)
  • Gross Profit *see Endnotes
  • Recurring Revenue (MRR, ARR, etc)

The key consideration for inclusion of a North Star Metric in the PMF formula is whether it represents the total of quantifiable activity being measured.
Based on these three input variables, I then created several models where CaC and NSM change over time.

And based on the analysis of these models, I believe I have determined a way to quantify product market fit.

The PMF Formula

Product Market Fit

ΔNSM - ΔCAC>100%
High PMF Growth Potential
ΔNSM & ΔCaC represents the growth of NSM and CaC, as a percentage.
In summary, when a startup’s North Star Metric (NSM) grows greater than the cost to acquire customers (CaC), a company can be determined to have product market fit.

And if the growth in the North Star Metric is twice the growth of the cost to acquire a customer, then the startup has significant potential to further grow into the market.

Let’s try out this formula with a hypothetical startup.

Startup A has $100,000 monthly recurring revenue (MRR) and spent $30,000 to acquire customers. Over time, it grew MRR to $150,000 (50% increase) while keeping acquisition costs to $30,000 (0% increase).
50% NSW
0% CaC
According to the formula, this startup has clearly found product market fit. Even better, it did so without increasing its acquisition cost.

In contrast, Startup B started at the same MRR and customer acquisition; however, it increased MRR to $150,000 while increasing acquisition costs to $90,000.
50% NSW
200% CaC
So while both startups grew, Startup A clearly has found some form of product market fit where Startup B has not.

Now let’s take an extreme example.

Startup C has the same starting point of the previous two examples. However, it increased MRR to $300,000 while spending the same $30,000.
200% NSW
0% CaC
Not only has this startup achieved product market fit, it would also be logical to assume there is more potential for this company to grow into its market by increasing its acquisition costs.
In every scenario I modeled where NSM and CaC increased or decreased overtime, the PMF formula quantified whether a startup had achieved product market fit from an outside perspective.

What about ratios like LTV/CaC?

In finance, ratio analysis is commonly used to evaluate a business, especially  SaaS startups where LTV:CaC is a key benchmark for many investors.

The common wisdom is that any SaaS with a LTV:CaC ratio greater than 3 has found some measure of product market fit.

I disagree.

While it is an important metric, it is a weak signal of PMF.

Let me illustrate.

You have 2 startups with an LTV of $1M and CaC of $300,000. Hence their LTV:CaC ratio is 3.33.

Over time, one startup doubled its LTV and CaC and the second decreased LTV and CaC by 50%.

Both companies have a LTV:CaC ratio of 3.33. However, the second company is decreasing its LTV while the other is increasing.

This is why I would consider LTV:CaC a weak signal, because it does not account for time.

Personally, I like metrics that provide a clear picture of performance without having to combine the result with other metrics to obtain a concise performance model.

Formula Limitations

In reviewing this formula, there are several limitations that should be considered.
Lack of real-world data
As a startup founder, I do not have access to a large dataset of startup financials. I can only use the data I have from the four startups I co-founded.

Hence, this formula has not been tested using a large dataset.

Hopefully, professional early stage investors can use this formula and determine if the formula of PMF holds. More importantly, share their results!

CaC Performance Over-time
This formula assumes that all customer acquisition costs and their results occur in the time period analyzed.

But for startups that have a longer sales cycle, the results from acquisition costs may not be realized for the period being reviewed.

To compensate for this delay, it is reasonable to include projected results in the formula, provided that those projections are based on verifiable assumptions.

For example, a company spends $10,000 on ads every month and has observed that 50% of customers are acquired in the first month, with the remaining customers from that ad spend are acquired 11 months later (i.e. cohort analysis).

In this scenario, both the actual and projected activity should be included in the NSM value, provided that they are clearly separated.
Δ(ActualNDM + ProjectedNSM)
Company Stage Matters
While this model should work for most early stage startups, it will not work for companies in the very early stages of preSeed or where a startup has matured to some level of market dominance.

In the very early stages of a startup, customer acquisition spending can be very inefficient with companies experimenting with different acquisition channels.

Hence there is a lot of wasted money in these embryonic companies as they spend resources to find the right messaging and distribution options while further refining their product/service.

On the opposite side are startups that have established a level of market dominance based on their growth of customers and revenue but now are competing with other market players for market share.

In this scenario, it is reasonable to assume that these companies will have much higher customer acquisition costs with lower growth rates in their North Star Metric.

But rarely do investors ask if these maturing growth startups have product market fit!

In practice, I believe this model is best used for startups in the Seed and Series A stages where founders and investors can measure the performance of the company once a suitable investment has been made in them.

Hence for a Series A investor, using this PMF formula for a Seed Stage company may help answer the product model fit question. For Series B investors, they can use this model to quantify PMF and also gauge the potential PMF as the startup becomes larger.


Securing funding as an early stage startup is very hard. Lots of competition with very few funding sources.

My goal in sharing this formula is to provide a common metric that both startups and investors can use to quickly establish if a company has achieved product market fit, removing the ambiguities that surround this common question.

But I could be wrong and completely off the mark about whether or not this formula works.

That’s where you come in!

Does it work for you? Is it something that helps? Then share this article online.

Or, does it fail to properly quantify product market fit? If so, then let me know. I welcome your criticism.

Regardless, I would appreciate your feedback.

After all, I am a startup founder that would like a clear way to demonstrate product market fit as we speak with investors and not rely on gut intuition.

About The Author

Sean Jackson is the CEO & co-founder of Vitag - a platform for delivering information on-demand. Sean is a serial entrepreneur having started and exited from four startup companies. For more information, please visit his profile on LinkedIn at https://www.linkedin.com/in/seanajackson/


I would like to thank Jason Cohen, Founder of WP Engine, for inciting this conversation on Twitter and providing to me his criticisms and feedback as I iterated through this concept. I am proud to have him on our Cap table!


Gross Profit vs Revenue as a North Star Metric
While revenue is important, gross profit is a better value to measure. For example, a company can sell its product at cost and potentially increase revenue without increasing acquisition costs. While a valid strategy for some businesses in the short-term, it does not lead to long-term company viability.

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