It’s been almost a decade since Marc Andreessen introduced the concept of “Product Market Fit.” Since then, PMF has become ubiquitous in startup vernacular. Seed stage entrepreneurs say they’re working toward product-market fit. Later stage entrepreneurs claim they have it. Entrepreneurs in a pivot claim they lost it but now they’re getting it back. The concept is so universally used that it’s become tautological. Companies with strong revenue growth and cost-effective customer acquisition, are deemed to have Product-Market Fit (PMF) which is then used to explain their success.

In Andreessen’s original formulation, PMF was binary. There were BPMF and APMF: “Before Product Market Fit” and “After Product-Market Fit.” In that model, companies iterate until they find PMF, then scale. Graphed, it would look something like the following (imagining sales as the Y-Axis):

While this might fit a Facebook or a Google, most B2B markets are more complex than this. Business-to-business markets are usually a hodgepodge of multiple types of customers, product use cases, purchasing processes, and geographic eccentricities. In these markets, getting to initial PMF is not that big a breakthrough – it’s usually just the start of a long process. As the company scales in these markets, it has to continually re-create new product market fits as it reaches saturation with the customers and use cases it can reach with existing products, channels, and well-served geographies.

So, rather than the “no PMF – PMF! – Scaling!” model, it’s a better idea to expect a model that looks more like the graph below: a sequence of new channels, products, and geographies that together add up to a smoothly scaling revenue line. A company that successfully navigates this process is achieving more than just PMF: it’s achieving Go To Market Scale.

While the simple model of Product Market Fit can help focus teams that are trying to get someone (anyone!) to buy their first product, I think it leads teams to think “job’s done” when they hit on the first working combination of product/channel/customer. After initial PMF, many teams can ramp quickly to their first $3M (or $10M or $40M) of sales and then get blindsided when growth slows. And it’s understandable why this happens: it’s hard to admit that things need to change, particularly when the team went through multiple painful iterations to get to their initial PMF. But too often, teams react by doubling down on the current go-to-market mix through increased sales hiring or bigger marketing expenditure even as the incremental sales head or the new marketing spend returns increasingly disappointing results. The next step is usually to fire the sales or marketing head for poor execution. But it’s usually not the execution that’s at issue, it’s the go-to market (GTM) mix.

It’s not a sign of failure when a particular GTM mix starts to yield diminishing returns. It’s normal for channels, products and use cases to saturate. It’s only a failure if the team doesn’t detect it and react in time to keep sales ramping.

Achieving Go To Market Scaling

Go To Market Scaling works by executing a smooth sequence of individual product market fits, each time establishing:

• Value Fit: The product delivers significant net value for a use case
• Purchaser Fit: There is an identified organizational purchaser
• Distribution Fit: There is a cost-effective distribution channel

Most teams focus on the first two bullet points: making sure that the product is delivering sufficient net value for well-identified use cases. This is where tools like Net Promoter Scores and “MustHaveIt” scores help. Scoring high on these measures means that products are delivering value. But if it’s tough to get customers to be references or if your product reviews are less than enthusiastic, then the company doesn’t have Value-fit (at least for these customers and use cases).

But, the third bullet point above is equally as important and often ignored: how do you know what channels are likely to be a good distribution fit and how do you know when you have good fit? The latter question is usually easy to answer: when you have distribution fit it’s suddenly easy to recruit new high-quality channel partners or top performing sales reps from other companies. Almost everyone makes quota. Highly qualified prospects complain publicly that they can’t get a call back from your sales force because you don’t have enough sales capacity to handle demand. Alternatively, in a self-service SaaS model, your word of mouth driven direct traffic or your referral/viral loop suddenly starts kicking in. Everyone wants in.

Once it happens, you can see that you have fit – but it’s a harder problem to figure out what distribution is going to be the right fit before you go to market. Part of it is understanding your unit of value. But it’s equally as important to understand your product’s selling costs, first unit implementation cost and how your products’ value scales with units.
The value that a product can deliver when it’s an Enterprise standard vs. a team standard vs. single user is almost always qualitatively different. 

If the cost of adopting the very first unit is high (perhaps because of a security review or data integration requirements), then those costs put a minimum bar on the number of units you have to sell at one time – which in turn limits your choice of distribution.

If you understand your product’s value scaling and minimum adoption cost, you’ll be on the way to picking distribution with good fit.  For example, products with high implementation costs usually require a direct sales force equipped with systems engineers and professional services.