Introduction

The different stages of growth

The growth journey rarely unfolds in a straight line. Markets shift, products evolve, and teams adapt. Yet despite that inherent variability, software companies tend to move through three recognisable phases of maturity. According to former HubSpot CRO, Mark Roberge, these are: Product-market fit (PMF), go-to-market fit (GTMF) and growth/moat. Though AI has in many cases sped up the transition through these stages and tweaked the underlying characteristics, the overall nature of the stages remains intact. Below, we outline the key components of each. 

These phases – and their non-linearity – is nicely shown in the below diagram. Before you get to the GTMF stage – it’s important to be confident you’ve achieved PMF. The next section covers how you can build that conviction. 

How do you know you’ve hit the elusive Product-Market Fit milestone?​ Looking at indicative metrics.

Product-Market Fit (PMF) is a term named by Andy Rachleff at Benchmark, who said, “When you first start out, the only thing that matters is finding a cohort of customers who truly value what you offer.” PMF is fundamentally about understanding whether you have created something that customers love and need.  

Whether customers truly need something is related to the value they are getting from your product. Looking only at client wins or revenue growth isn’t sufficient to figure this out, you want to see clients consistently using the product and not churning when given the opportunity to churn. For a software company, excellent retention metrics are compelling evidence that customers love what you’ve built. They have bought into what you have created and have made your product an indispensable part of their operations. 

A major issue with tracking retention is that it can take a long time to collect the detailed metrics you would be looking for – in the enterprise software world this can even take years. Companies with high ambitions and looking to figure out if they have reached PMF don’t have time to wait for these detailed metrics to develop – especially in a time where everything is accelerated by AI. The best software businesses create models to track Leading Indicators of Retention (LIR) incorporating key engagement metrics that you believe are correlated with eventual client retention. Eventually, this can be back-tested and refined against actual retention to close the loop on your models and ensure continuous improvement. ​

In addition to both predictive and actual retention metrics, there are several traditional PMF metrics from the consumer world which can also be helpful indicators, such as Net Promoter Scores (NPS) and user sentiment. 

Our advice to founders is: don’t wait for the evidence to fall into your lap. Hypothesise about the right indicators for your business, and come up with a list of three to five indicators that might predict propensity to renew, expand, or churn. Sit down with your customer success team and product team and try to gather these indicators for your ten to twenty most recent customers that were up for renewal. If you have insufficient data, make approximations or skip the indicator for now. Can these be improved by the customer success team? By targeting different leads? By qualifying differently in the pipeline?  Measure, test and iterate. 

As soon as you’ve built conviction that you’ve nailed PMF, it’s time for the next stage.

Premature scaling off shaky foundations is a common and painful scaleup mistake

Winning early customers and finding PMF is a critical milestone for startups. But simply finding PMF won’t automatically unlock consistent, reliable growth for your company. Probably the single most common mistake we see businesses making is trying to invest heavily in scaling while still on shaky foundations.

Attempting to scale too quickly and immediately jumping from a shaky PMF to the ‘Growth’ phase despite uncertainties around their PMF can have tough consequences and lead to: poor commercial decisions, missed opportunities, wasted money, unnecessary dilution, and human costs associated with burnout and high employee attrition. In other words, it is not recommended to fast forward and rush through the work of finding your go-to-market fit. 

“The number one problem I’ve seen for startups, is they don’t actually have Product-Market Fit, when they think they do.”

ALEX SCHULTZ | CMO, META

Five questions to ask yourself to assess if you’re ready to move on to focus on GTMF

Discovering whether you have reliably achieved PMF is harder than ever in the world of AI. This is for two key reasons – firstly the underlying technology is changing so rapidly that without constant technical improvements, a post-PMF startup can very quickly become technically obsolete. And secondly, most enterprise buyers are currently experimenting with a very wide range of AI tools, which is unlikely to be representative of their long term buying patterns, which makes revenue traction and demand signals much noisier. These reasons have led some commentators to suggest that achieving PMF is no longer static (Link to Tom Tunguz Article). 

Below are five specific practical questions to ask yourself to determine whether you have confidently made it through the PMF stage. 

1. Is customer adoption and usage strong and consistent across defined user groups?

2. Do we have clear data showing healthy emerging retention rates (logo retention, NRR, POC / pilot conversion etc)

3. Do we have any evidence of evangelical customer and viral referrals?

4. Does usage of the product spread in a low friction way across different users / teams in new customers?
5. Are customers showing early evidence of stickiness – e.g. asking for additional integrations with other systems or workflows? 

After reaching Product-Market Fit, finding Go-to-Market Fit is critical to successful scaling

Go-to-market (GTM) generally refers to everything around how a product is positioned, packaged, marketed and sold. It works as an amplifier to the underlying product advantage. While it’s possible to achieve PMF without a well-functioning GTM strategy, once you are in the scaling phase it’s critical to iterate towards go-to-market fit to effectively scale from early promise into mainstream success. Put simply, it is the product advantage, plus the efficacy of the go-to-market strategy that generates significant and durable growth.

Companies face a challenging trade-off: trying to expand as rapidly as possible and capture the market opportunity, whilst ensuring they don’t pour investment into a broken or incomplete GTM machine. Channelling rapid investment into an irreparably leaky funnel will consistently lead to much worse outcomes than preemptively building and iterating a well-functioning engine.​

Many people are familiar with working with a Minimum Viable Product (MVP) when launching a product. In the go-to-market case, it is helpful to start with your Minimum Viable GTM strategy, then test and iterate your way forward. This journey should not begin once at scale; it should represent continuous focused experimentation, iteration and improvement. After investing in this process of finding and ultimately reaching go-to-market fit, then you can be confident that you are ready to scale by doubling down on the things that work, while simultaneously experimenting with your next GTM tactics. You have put in the foundational effort.

In today’s AI-powered environment, this journey is both accelerated and heightened in complexity. AI-driven tools let you automate lead scoring, personalise outreach, and optimise campaigns with real-time data, amplifying both speed and accuracy of your GTM work. However, deploying these technologies on an unproven or weak GTM foundation will magnify inefficiencies at scale. To fully utilise and tap into the powers of AI, founders should pair rapid experimentation with robust validation, ensuring the GTM engine is resilient before leveraging AI to drive expansion and unlock mainstream growth.

As you might expect, the time spent navigating the GTMF stage will vary from company to company. There have been some breakout successes who have navigated this phase extremely rapidly, but most companies take some time to achieve GTMF. Companies solving complex problems or selling into large enterprise customers tend to take a bit longer to find their sweet spot. We recommend working towards GTMF in the same way you might do early product testing: create a culture of ownership, experimentation, measurement and iteration when optimising your GTM strategy. This brings us onto measurement, which we’ll cover in the next section.

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