Demystifying Product/Market Fit

by Nick Mikhailovsky, CEO and Co-Founder of NTRLab.

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Marc Andreessen said, “…the life of any startup can be divided into two parts – before product/market fit and after product/market fit.” It’s a kind of magic thing, without which a startup stands little chance of success. That said, how do you define it beyond “you know it when you see it?”

The idea of Product/Market Fit (PMF) probably started with Steve Blank’s description in his book  “Four Steps to the Epiphany,” although he didn’t call it that. Later, the term itself was used in “Startup Marketing” by Sean Ellis. He created Startup’s Pyramid and  described its life cycles. Since then, every early stage startup’s goal is to reach PMF (and thus become a later stage startup).


Marc Andreessen wrote a series of posts in his Pmarca blog, “The Pmarca Guide to Startups.” In part 4 he writes that PMF is the only thing that matters.

At the same time, his way of defining PMF is akin to the proof of a theorem that says: “We omit the proof as obvious”:

You can always feel when product/market fit isn’t happening. The customers aren’t quite getting value out of the product, word of mouth isn’t spreading, usage isn’t growing that fast, press reviews are kind of “blah,” the sales cycle takes too long, and lots of deals never close.

And you can always feel product/market fit when it is happening. The customers are buying the product just as fast as you can make it or usage is growing just as fast as you can add more servers. Money from customers is piling up in your company’s bank account. You’re hiring sales and customer support staff as fast as you can. Reporters are calling because they’ve heard about your hot new thing and they want to talk to you about it. You start getting entrepreneur of the year awards from Harvard Business School. Investment bankers are staking out your house. You could eat free for a year at Buck’s.

Tren Griffin wrote a long article called 12 Things about PMF, but still does not provide any specific measurable way of determining whether or not PMF happened. First he quotes Andreessen, then Eric Ries. “The term product/market fit describes ‘the moment when a startup finally finds a widespread set of customers that the product resonates with.’”  

Furthermore, he writes, it is impossible to quantify PMF: “The “satisfy the market” part of the Andreessen definition is where the PMF concept necessarily starts to get qualitative. Various math tests have been devised in an attempt to quantify PMF, but they are proxies for something that is fundamentally like Justice Stewart’s famous definition of pornography: “I know it when I see it.” Even if there is a best practices test for whether PMF exists that does not mean that creating PMF can be reduced to a formula.”

I respectfully disagree.

Our experience working with dozens of early stage startups,  primarily on the software development side, says that PMF, for a significant number of start-ups, is achieved when at least one user acquisition channel meets the following condition:

CAC < Expected LTV,                                            (1)


CAC – Customer Acquisition Cost (money spent on acquiring 1 paying customer),

Expected LTV – mathematical expectation of full earnings from the client (after deduction of COGS – Cost of Goods Sold)


The left side of the inequality relates  to the market. We can symbolically represent a user acquisition channel as

[user acquisition channel] = [client segment] * [traffic source] * [conversion instruments]      (2)

Client segment is a group of (potential) clients for which you have a specific unique value proposition.

Traffic source is a marketing channel, such as adwords, cold calls, word of mouth, etc.

Conversion instruments are things like landing pages, presentations, call scripts and other tools to close a sale/conversion.

The customer acquisition cost depends on all three parts. One can optimize them, iterating in an embedded way:

for i in [client segment]

                for j in [traffic source]

                                for k in [conversion instruments]


The right side of the inequality relates  to the product. It is influenced both by the margin we have on a single transaction, and the number of such transactions we have with the customer until he leaves for good.  You can express it this way:   


where Pk  is the probability of k-th purchase/billing of the customer

PRICE is the price of the  k-th purchase/billing of the customer

COGSk  is the cost of goods sold at k-th purchase/billing of the customer

Neither side of the equation (1) includes fixed costs. This is because if the above condition for PMF is achieved, we can raise funds, spend money on marketing, acquire a lot of users, and *eventually* our earnings from *A LOT OF* users *may become* more than our fixed costs and we will make a profit.

Achieving a product-market fit does not mean that we are already profitable. Quite to the contrary, user growth typically requires a lot of investment, and the cash flow will be negative during that process. Moreover, depending on the nature of the product, the user acquisition cost can grow with time/number of users, thus limiting potential growth.

Suppose we have a single-product online store: we buy an item at a price of X and sell it at a (larger) price of Y.  Suppose also that our customers only buy once. If


we make a unit profit on this sale.

Of course, we have fixed costs that are not related to a specific sale, but, as the sales volume grows the fixed costs will be offset by the volume of our sales:

(Y-X-CAC)*N > F

where N is the number of sales for a specific time period, F are the fixed costs for the same period.

Taking it a step further, let’s assume now that the customer makes the next purchase with probability 0.5. Summing the series, we obtain

Expected LTV = 2(у-х).

Then we can acquire customers more expensively than у-х (but cheaper than 2(у-х)), and eventually make a profit on these customers. However, in order to keep the startup running until we get this profit, we would need more operating capital.

Definitely, the CAC for each user acquisition channel will be different, and that should be  accounted for properly.

The situation is more complicated in “true” bilateral markets, such as AirBnB, where CAC applies to attracting both buyers and sellers. There are, however, many simplified bilateral markets where one of the sides can be taken for granted initially. A good example of such a simplified bilateral market is any digital media business where you can initially rely on adwords and other readymade advertising products for (lousy) monetization. It is sometimes possible to further simplify the situation by converting a bilateral market into a unilateral one by, for example, monetizing a media audience through offline events like conferences.

There have also been products with PMF so strong that CAC was nearly 0. Slack is a famous example of a PMF so powerful that it scaled without the need to spend on advertising at all.

It is more difficult to apply the formula in the case of enterprise applications. In that case, the cost of the initial sale for a startup is often very high until there are enough cases to show customers and the unit economy only becomes positive if the startup is paid for implementation services. In cases like this, our formula may not be the best way to determine product-market fit.

As startups start to scale, other factors come into play, such as the cost of customer retention, which can significantly affect the financial dynamics. This is especially true for SaaS startups, but that is another story for a different post.

Now you are equipped with most of the tools to determine numerically if your startup has a product-market fit — or not.

Contact Nick via email and connect with us on Linkedin!

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