From opinion to expected value
Most strategic debates are competing opinions stated with equal conviction. The fix is to make the value logic explicit: what would this decision change, by how much, with what probability? Ranges beat point estimates, because a point estimate hides how uncertain you really are. An expected-value view, even a rough one, turns "I think this is big" into something the leadership team can actually interrogate.
What to quantify
- The size of the prize. Revenue, margin or cost effect if the bet works, as a range.
- The probability it works. Stated explicitly, so it can be challenged.
- The cost of pursuing it. Including the initiatives you would have to stop.
- The cost of delay. What waiting six months actually costs you.
- The leading indicators. The early signals that tell you within a quarter or two whether the bet is landing.
Quantified this way, decisions become testable. The key results you set afterwards are simply the leading indicators you identified here, which is exactly how strategy connects to OKRs.
Quantification without false precision
The trap in valuing strategic options is demanding proof from a domain that cannot supply it: markets can be otherwise, so no model proves a strategy in advance, the Aristotle Distinction. The honest alternative is not giving up on numbers, it is attaching them to conditions: what would have to be true for this option to be worth X, and what is each condition worth knowing?
The methodology connection
The working method is What Would Have to Be True: map the conditions, quantify the sensitivity of the outcome to each, and spend your testing budget on the pivotal doubtful ones, which is also where the crux lives. Data serves as evidence about conditions, never proof of conclusions, the discipline of strategy as logic, not analysis. The number that comes out of this process is a range with named assumptions, which is worth more than a confident point estimate precisely because it tells you what to watch.