The most common mistake startups make before raising venture funding

August 29, 2017

At Matrix, I'm beyond fortunate to meet with hundreds of startups in their earliest stages. The nature of this work can lead to whirlwind days filled meeting with companies innovating in sectors ranging from SaaS to VR to fintech to online marketplaces (sometimes all in the same day- if I ever seem scattered, that's why!). Yesterday a friend asked me what the most common mistake companies make in the early years of their life is. In answering I realized that despite the wide variety of business models, I see the same key issue over and over again in pitches: many startups wait far too long to experiment with scalable, repeatable go-to-market models. 


There are many reasons why startups don't do this, many of which are valid (i.e. the product isn't ready) and others which are much less so (sales are coming easily). Often, it comes down to focus: building a product can be all-encompassing work, and carving out valuable time for something that feels like phase two is often counterintuitive. Unfortunately, this is one use of time/money that has high enough returns that it shouldn't be skimped on at any point in a startup's life without a truly great reason.


To make the point clear, I'll paint an all-too-common picture. A company with a great product is off to a fast start, say $1m ARR using a SaaS model. They are out raising their series A, and the stated purpose of the funding is to accelerate growth. In the pitch, they talk excitedly about how the product sells itself. "We've spent almost nothing on sales and marketing" they'll invariably say before a pregnant pause, leaving it to the investor to imagine how much steeper the relevant curves could look with a little Series A jet fuel poured into the fire. 


The unfortunate issue for everyone involved is that assumption might be horribly wrong, and it could have been de-risked at relatively low cost by devoting a bit of time 6-12 months ago to running tests. Without this testing, the answers to key questions such as:


What does it cost to get a qualified lead? 

What are the most effective lead-gen channels?

How well do paid qualified leads convert?

What is a reasonable quota for a sales rep?

What might LTV:CAC look like? 



will be less-than-educated guesses. 


Consider an alternate scenario, in which the same startup started testing different approaches in a systematic way twelve months ago. The founder tells investors that Facebook advertising is the most efficient channel for lead-gen, costing 30% less than search advertising, and that leads from Facebook convert at similar rates as leads from the company website. Since it is based on reasonably broad targeting, the Facebook lead generation is likely to scale very well and can reasonably be expected to generate a thousand leads per month with $50k/month of spending, enough to keep five sales reps busy at good quotas based on historical close rates. All of this flows into a plain and simple excel model that builds to bookings and revenue targets. The example is oversimplified, but hopefully the concept is clear and it should be obvious that, all else equal, investors will tend to prefer the latter story. 


The difference between the two companies is not an epic amount of time or money, but rather a modest amount of money and a reasonable amount of focus at the right time. (If started too late, these tests can't fully approximate the true sales process and resulting conversion rates.) The resulting difference when it comes time to fundraise, however, can be quite meaningful. More importantly, the benefits of hitting the ground running in a data-driven way after a major fundraise, rather than starting the testing/iteration process with a blank sheet of paper, are potentially immeasurable. 


To be clear, not all companies need to or should come to a Series A fundraise with this work done- at Matrix we do our very best to evaluate each potential partnership on its own merits and there are many cases in which this is either impossible to pull off or makes no sense. I write this only because I hear a great many pitches where proactive steps here could have gone a long way toward getting investors to yes and ultimately helping founders do what we're rooting for them to do: build great, lasting companies that change the world.


Looking for advice how how to tell if your go-to-market testing efforts are on the right track? Check out this post: Five signs a startup's go-to-market is scalable and repeatable. 





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