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Predicting success

by Nis Frome
January 28, 2019

Make good decisions and you shall succeed. Measure good decision-making and you shall succeed immeasurably.

The critical limitation when evaluating the performance of teams and individuals is that virtually every definition for success is a lagging indicator: sales teams are judged on quota, marketing teams on leads, product teams on NPS, and so on. Each metric informs managers whether past activities contributed to today’s positive outcomes. But in a fast-changing world we know that what worked yesterday won’t work tomorrow. How can we determine whether ongoing activities will lead to similar, better, or worse results?

In other words, is it possible to reliably predict success?

A leading indicator for success is certainly conceivable. During my onboarding sessions with employees, I ask about how success was defined for them in previous roles, and how they think success should be defined in order to drive high-performing teams and individuals. The responses converge on a widely regarded answer, but one that is seemingly impossible to measure:

Success is the result of quickly and continually making ‘good’ or evidence-based decisions over time.

Some of the most innovative organizations in the world, like Amazon and Bridgewater Associates, obsess over decision-making processes. The problem is that even if we could agree on criteria, it would be challenging to measure decision-making. Documenting and evaluating every decision would turn the workplace into a Dilbertesque science fiction movie.

Fortunately, that isn’t necessary, as a measurable and reliable indicator for evidence-based decision-making is hiding in plain sight. Finding it is as simple as knowing what to look for.

The anatomy of evidence

Teams and individuals that make evidence-based decisions operate systematically and observably different than those that don’t. That’s because evidence is, by definition, a feedback loop with reality, and whenever there’s a gap between hypotheses (e.g. good ideas) and reality (e.g. stakeholder and customer feedback), the result is friction. Teams that generate evidence also generate friction. Teams that don’t generate evidence also don’t generate friction. Measure friction as a proxy for measuring evidence and you can easily identify which teams will perform well on a sustained basis and which won’t.

It’s a simple concept, vividly explained by one of the most successful product leaders in history, Steve Jobs. Now famously known as “The Lost Interview,” Jobs was asked a series of questions in 1995, years after being fired from Apple and shortly before rejoining and revitalizing the company. In one telling segment, he discusses the widespread fallacy that ideas are inherently good or bad, and that creativity and brilliance are the ingredients for success. Rather, continuous testing and iteration, he says, lead to evidence-based decisions which lead to innovation. Friction is a byproduct of that process.

“It’s through the team, through that group of incredibly talented people, bumping up against each other, having arguments, having fights sometimes, making some noise, and working together, they polish each other,” Jobs insightfully understood. “And they polish the ideas.”

Measuring friction

The leading indicator for success is hiding in plain sight because friction is a tangible artifact of ‘good’ decision-making. It’s in your inbox, in your company’s shared folders, and staring at you during each and every meeting. It’s in every file name, including the ones in which this article was drafted.

Generating evidence via testing creates friction, friction forces iteration, and the cycle repeats until a sufficiently evidence-based decision can be made. Teams and individuals that operate this way leave behind a trail of breadcrumbs.

When presented with a file or project, your very first question should be:

“What version number is this?”

As a rule, initial files and projects shouldn’t be developed beyond a brief outline (e.g. few assumptions taken for granted) without feedback and iteration. If there is a version number greater than 1, you should then ask:

“What’s the difference between this version and the one before, and why?”

The answer(s) to each are often informative enough that additional review isn’t necessary. If teams and individuals are deliberately testing assumptions and iterating quickly, trust that they’ll generate evidence, develop perspective, and pick up on nuances in order to get to a ‘good’ decision.

Consider how counterintuitive that would be for most organizations, where presentations and ideas are judged based on creativity, style, and political alignment. “In the most dysfunctional organizations, signaling that work is being done becomes a better strategy for career advancement than actually doing work,” writes Peter Thiel in Zero to One. But the opposite should be true.

Creativity and effort can be wildly overrated. Friction may be unsexy and uncomfortable, but ‘decision gauntlets’ (as I describe them) form the foundation of sustained innovation. Measuring friction is a reliable and surprisingly easy way to evaluate decision-making and ultimately predict success. Optimizing friction early and often is a recipe for building high-performing cultures that win in any environment, now and in the future.

This post was originally published on Medium and has been republished here with permission.

Nis Frome

Nis Frome is the co-founder of Alpha, the platform that enables management teams to make data-driven decisions about users, products, and new markets.