When talking about metrics, investors are often asked: “What’s a good number to target?”
This is understandable: investors often rush to look at a company’s data – numbers that describe a startup’s performance – and reject it on the basis that one or more of these metrics don’t quite hit a particular target. Understandably, this leads businesses to stretch their numbers in an effort to meet a certain criteria. This causes a vicious cycle that shouldn’t exist in the first place.
Instead, analysis should start with understanding the particular dynamics of a company and avoid examining metrics in isolation. This is because there is more to building a great company than simply hitting particular metrics, and startups deserve investors who can look beyond the metrics.
This is one reason why specialist VCs who have experience in the nuances of a particular company type, or operator VCs with intuitive understanding of the operational levers in a business, are essential to any firm’s makeup.
How metrics can be manipulated
By its very nature, software-as-a-service (SaaS) is an incredibly data-driven corner of VC. Metrics can be incredibly powerful, enabling investors to compare build up benchmarks or one company against another, even at comparatively early stages of their development. But the ease of doing so means there’s a lot of information hiding beneath the surface.
Glorifying metrics above all else can lead to a few problems. When an investor states “this is the metric that matters”, companies are likely to optimise towards it. Focus isn’t bad, but myopic advice based on general knowledge that doesn’t fully appreciate an individual company can undermine a company’s early efforts. In a similar vein, pronouncements that ‘this number is good,’ or ‘this number is bad’ can lead to similar behaviour where a company will start optimising to hit a particular number. This is a typical human tendency: we tend to improve what we measure.
For example, a startup might change the price of a product so that revenue goes up, to engineer greater revenue and hit the desired KPIs. A problem comes, however, when you create a ceiling by selling to a customer that cannot afford to pay more, and negatively impact the business over time. The particular metrics you were chasing might look good, but the overall picture, less so.
A more subtle problem can arise in selecting – and sticking to – precise definitions for metrics. Certain SaaS metrics are neither standardised nor consistently calculated. So especially in early stage companies, before consistent accounting principles are in place, it’s easy to fall into the trap of picking the definition that makes the company look superficially superior.
As an example, take the CAC (customer acquisition cost) payback period. What should be part of the CAC? Brand spend? Management team members who lead revenue functions? A growth squad that improves the customer conversion cycle? And should you offset CAC to the length of the sales cycle?
The outcome of the calculation is totally skewed by the definition picked. Making it low may provide short-term relief: passing hurdles in a fundraising process or avoiding questions at the board meeting. But in the long-term, investors do companies a disservice by attempting to build a predictable growth engine and reach scale by incentivising companies to jump through hoops to hit the ‘good number’. Consequently, the metrics must be placed into context.
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The solution: taking an operator approach to data
As an ex-operator, I first want to get under the surface of how a company acquires its customers: is it an inbound or outbound motion, what marketing channels are used, who does what in the revenue teams, what’s the sales cycle length like, etc. Day-to-day metrics the teams already use are the key diagnostic for further growth potential: is the sales cycle increasing or decreasing? Can we charge higher prices to new customers? These day-to-day metrics are also the ones that the operational leaders of a company can directly influence. And these typically combine to offer a truer view of how the CAC payback period could change.
Only day-to-day metrics will tell you what is fundamental, and what has the ability to change. A financial plan can look sensible on paper, but reaching those growth targets and metric improvements depends on what’s underneath the surface, which is why context is necessary alongside numbers.
Furthermore, in a pre-product-market-fit business, calculating SaaS metrics such as LTV:CAC can be close to meaningless (I confess to skipping in decks!). What is the worth of the LTV of a customer, if your company has only existed for a year and has only a handful of them? What’s CAC if a company has less than twenty employees and everybody works collaboratively to land a sale, making it close to impossible to allocate effort across individuals? The operational day-to-day metrics are all you have in this case.
VCs can get a long way looking at metrics, but it is when they put on the operator glasses and look beyond the numbers that they can unlock the full picture of a company’s potential.