i’ve hit this wall more times than i’d like to admit: execs pushing growth bets, engineers waving red flags about tech risk, and customers asking for tiny fixes that actually matter to retention. i’ve been lurkin’ community threads and talking to a few veterans — common advice was to pick 3 clear decision criteria, quantify assumptions (even roughly), and put a one-page decision brief in front of stakeholders before debates start. in my last shop i used customer impact, cost-of-delay, and engineering risk as the trio — it forced concrete trade-offs instead of opinions. curious — what’s one sentence you’d put at the top of a decision brief to make it exec-ready?
yeah, everyone loves frameworks until they don’t. i’ve seen teams spend weeks priortizng while the market moves on. i’d pick one metric that actually hurts the business when it’s ignored (revenue at risk, churn delta), slap numbers on it, and tell people what will happen in 30/60/90 days if we don’t act. make it embarrassingly concrete. people respond to pain, not abstract ‘customer delight’. and for god’s sake, stop calling every feature “strategic” — it’s not.
i tried a rice table but execs just asked for a demo. anyone have a short script or template i can use to explain cost-of-delay in 90s?
In my experience, defensible prioritization combines a shared objective, transparent criteria, and consistent scoring. Start by aligning stakeholders on a single north star metric tied to company outcomes (e.g., revenue growth, active users). Define three decision criteria that map to that metric and can be quantified — for example: projected revenue impact, implementation effort (in engineering sprints), and customer retention impact. Run a light scoring exercise, document key assumptions, and present the top two scenarios with sensitivity to those assumptions. That one-page brief reduces subjective debate and shifts conversations to which assumptions require validation.
i remember a launch where everyone argued for their favorite feature — sales for growth, eng for a rewrite, cs for quick fixes. i wrote a one-pager that listed the top three metrics affected, rough effort in days, and the likely customer-visible outcome. presented it in our 15-minute sync. execs picked the top item because it showed a clear risk to churn. not glamorous, but the act of quantifying the trade-off turned noise into a decision. what i’d change next time: include a tiny sensitivity row (best/worst) to avoid false precision.
When I ran prioritization exercises, we normalized scores across three axes: expected monthly revenue impact (estimated from cohort conversion lift), engineering effort (story points converted to sprint-weeks), and probability of success (based on past delivery history). For a set of 12 candidate features, the top three scored >= 80/100 and accounted for 68% of the modelled revenue uplift under conservative assumptions. Presenting both absolute and normalized scores, plus a one-line assumption for each input, made the ranking defensible and replicable.