If your teams don’t understand and believe in your data, they won’t leap with you.
I’m a huge practitioner of data-driven SaaS management. I believe that almost everything in a SaaS business can and should be measured. But there are two important caveats to measurement that I’ve recently seen overlooked.
First, the data you use for KPIs or OKRs must be understood by your teams. And second, it must have perceived integrity. They have to know what the numbers mean and trust that they’re accurate.
There are some important words in there that need emphasis. First, “understood by your teams” isn’t the same as understood by the founder or the C-level. Of course you know what the data means. That doesn’t mean everyone else does. Ask them to explain it to you. You may be quite surprised by the range of responses.
If your teams can’t articulate what their KPIs mean and how they track back to company success, teach them.
The other important word is “perceived”. It’s important because even if your data is solid, if it’s perceived as shaky or inaccurate, the entire data-driven management process is undermined. Trust is critical to belief. Belief is critical to passion. And passion means the difference between striving to think big and settling for incremental gains.
If there’s an undercurrent implicating your data as anything less than rock solid, you need to confront that and fix it. It’s hard to nurture passion from people who value achievement if they don’t believe that your data is an accurate reflection of their efforts.
Meaningful and Trustworthy SaaS Data Comes with Maturity and Scale
I’ve written before on SaaS scale versus SaaS maturity and how that relates to scalability. Data-driven SaaS management seems to follow right along with the maturity curve. Early on, many a SaaS may make many a decision without the benefit of data. In some cases that makes sense because of scale. For example, there’s nothing worse than trying to be a data-driven beast but having too-small sample sizes. That leads to one of two very bad outcomes. One, you actually wait for the data to become statistically relevant (way too long in SaaS time). Or two, you pretend you’re data driven by making gut calls on pathetic sample sizes. Either way, you lose.
The moral to the scale dilemma is not to kid yourself or your teams. Let anecdotal decisions be just that.
If you have scale in your data, you can at least have confidence in what you’re seeing. But maturity can be a very different thing, and it’s where the sweet spot is. I’ve seen immature SaaS companies (with enormous scale) make similar data-driven mistakes over and over again. Usually, those mistakes start with quantity of metrics over quality of metrics. High quantity makes it harder to understand the numbers and maintain their integrity. It’s the immature first reaction to “make the company data-driven”.
The data-driven SaaS management sweet spot is where scale and restraint intersect in a mature and powerful set of growth-driving numbers that everyone understands and believes.
I have a simple way of looking at the sub-metrics that live below the big hairy audacious numbers. If the revenue growth goal of the SaaS company is 10% month-over-month, then every sub-metric below that needs to improve by at least that same rate. That’s always been an easy way for me to justify audacious goals down to the lowest level. It’s easy for everyone to understand the logic that the topline follows what happens all the way down to their personal OKRs. It stands to reason that you can’t expect BHA growth if you don’t expect BHA improvement everywhere.
Using Meaningful and Accurate Data to Drive Big Hairy Audacious Goals
Passionate people get behind data they believe in. In my experience, that passion is the secret sauce that lets you expect extraordinary results beyond incremental improvement. Is that easy? No. If it was easy, everyone would do it.