Prioritize on Confidence and Time-to-Results

Two axes teams forget

George Bonaci, VP of Growth at Ramp, describes a correction to the way most teams prioritize growth experiments. The standard grid ranks work on impact and effort, the two obvious axes. Bonaci adds two more that he says are "usually lost when people are prioritizing": confidence, meaning how sure you are that something will work, and time-to-results, meaning how fast you will know.¹ The addition turns prioritization from a return-on-investment estimate into an information-value calculation.

From those axes he draws two decision rules. The first is straightforward: if you are highly confident something will work, do not agonize over it, just ship it. The second is the one he says teams neglect. If you are unsure a thing will work but you will learn the answer quickly, do it anyway, because the experiment is cheap information regardless of outcome. Speed-to-signal, on this account, makes a low-confidence bet worth running. The failure Bonaci is pointing at is teams killing low-confidence ideas on impact and effort grounds without noticing that a fast, cheap experiment buys knowledge either way.

Why time-to-results is the hidden lever

Bonaci treats time-to-results as the axis that does the real work, because it can be engineered. For long-horizon bets such as content or brand, which can take twelve to eighteen months to resolve, his move is to find leading indicators or to scope the experiment down so an early read arrives sooner. Compressing time-to-results is how a team keeps velocity high even on inherently slow bets: rather than waiting on the full result, it engineers an earlier signal. This connects the framework to the practice of scoping a problem down until it produces contact with reality quickly, and to treating growth as a portfolio in which fast, cheap experiments de-risk the roadmap while a slower slice stays funded for the long-horizon plays.

Bonaci is describing a ranking layer that sits beneath the broader treatment of growth as science rather than a copied playbook. Impact and effort alone, in his account, bias a team toward big, confident, slow bets and away from the cheap-information bets that actually reduce risk. Adding confidence and time-to-results is what surfaces the neglected quadrant, the unsure-but-fast experiment.

Where it strains

The aim of the practice at Ramp is to make prioritization a legible calculation rather than a matter of taste. Bonaci is candid that the framework has soft spots. Confidence is a subjective prior, and without calibration it can simply rationalize doing what a team already wanted to do, the attachment problem that dogs any experimentation culture. Fast-to-learn cheap experiments can also crowd out the slow high-impact bets if a team over-optimizes for quick signal, which is why he keeps the portfolio framing in place to protect the long-horizon slice.

The framework points at adjacent growth practices. Once a high-confidence winner is found, the move is to take it hard, described in saturate the winning channel. And the caution that a fast aggregate read can mislead if it hides a losing sub-population runs parallel to segment-level analysis, the discipline of checking experiment results by segment before trusting the headline.¹