Feature prioritization with evidence, not invented scores
Most prioritization frameworks ask you to put a number on a feeling. You assign a 1-to-5 to reach, effort, and confidence, multiply, and call the spreadsheet a decision. Feature prioritization with evidence flips that: Klyr ranks Feature Bets from what your customers actually said, weighing how often a problem comes up, how badly it hurts, how confident the signal is, and what the counter-evidence says, with every claim linked to a source quote.
Why RICE and ICE scores are guesswork in a trench coat
RICE and ICE feel rigorous because they produce a number. But trace any score back and you find a person picking digits that justify what the team already wanted to build. Reach is a guess. Impact is a guess multiplied by optimism. Confidence is the most honest field because at least it admits the rest is made up. The math is real; the inputs are vibes.
The deeper problem is that the score has no memory and no receipts. Six weeks later nobody can reconstruct why a bet scored a 7 instead of a 3, which customer said it mattered, or whether anyone pushed back. The framework laundered an opinion into a ranking, then threw away the evidence that would have let you check it.
None of this means frameworks are useless. It means a number with no source behind it is not a decision, it is a guess with extra steps.
- ·Reach and impact are estimated before you have data, then frozen as if they were measured
- ·Scores hide who disagreed and why, so the same debate reopens every planning cycle
- ·There is no link from the number back to a customer, an interview, or a quote
- ·Two PMs scoring the same feature land 4 points apart and both feel confident
How Klyr ranks Feature Bets from real evidence
Klyr starts where prioritization should start: the raw material. Upload interviews, support tickets, sales call notes, and churn surveys, and Klyr synthesizes them into themes where every claim links to a verified source quote. Themes that cannot clear two real citations auto-drop, so a one-off complaint never gets dressed up as a trend.
From those cited themes, Klyr assembles ranked Feature Bets. Instead of a multiplied guess, each bet is scored on signal you can inspect: frequency (how many distinct sources raised it), severity (how much pain the quotes describe), confidence (how strong and consistent the evidence is), and counter-evidence (who said the opposite, so you see the downside before you commit). You can open any factor and read the exact lines it came from.
The result is a ranking you can defend in a room full of skeptical stakeholders, because the answer to 'says who?' is one click away, not a shrug. See the full evidence-to-bet flow on a real example at the proof page.
Evidence-based prioritization the framework versions skip: counter-evidence
RICE has no field for 'and here is who hated this idea.' That omission is how teams ship confident mistakes. A feature looks like a slam dunk because everyone counted the customers who asked for it and nobody counted the customers it would slow down, confuse, or churn.
Klyr treats counter-evidence as a first-class input to the ranking. When the same corpus contains quotes that argue against a bet, those are surfaced next to the supporting ones and pull the score down honestly. You are not just seeing the case for building; you are seeing the case against, sourced and side by side.
This is the difference between evidence-based prioritization and motivated scoring. Real evidence includes the parts that are inconvenient. A ranking that only counts the wins is just your own enthusiasm with a chart on top.
From a ranked bet to something your team can actually build
A prioritized list is where most tools stop and where the work actually stalls. Klyr carries the winning bet forward into a Build Pack: a PRD with acceptance criteria and a coding-agent prompt ready for Cursor or Claude Code, every requirement still traceable to the evidence that justified it.
Then the loop closes. Ship the bet, record the outcome, and it feeds back into Product Memory, so the next prioritization round starts from what you learned last time instead of a blank spreadsheet. Your coding agent forgets the moment the session ends. Klyr is the memory it loses: the persistent graph from evidence to outcome that makes each decision compound.
That graph, not the citations, is the point. Cited synthesis is table stakes now. The durable advantage is a prioritization record that remembers why you built things and whether it worked. Compare the approach against NotebookLM and other tools.
When a simple framework is genuinely the right call
Evidence-based prioritization is not always worth the overhead, and pretending otherwise would be dishonest. If you have five features and a clear strategic mandate, a back-of-napkin ICE pass in ten minutes is the correct tool. Forcing a full evidence synthesis through a trivial decision is its own kind of waste.
Lightweight frameworks also work fine when the stakes are low and reversible. A copy tweak, a settings toggle, an experiment you can roll back in an afternoon: just ship it and watch the metric. You do not need a cited Feature Bet to justify a button color.
Klyr earns its place when prioritization is contested, expensive, or hard to reverse: a roadmap several engineers will spend a quarter on, a bet a stakeholder will challenge, a decision you will be asked to explain in six months. When 'says who?' is a question someone will actually ask, you want evidence, not a number you invented under deadline.
- ·Reach for ICE: few options, low stakes, easily reversible, clear mandate already
- ·Reach for Klyr: contested roadmaps, expensive build, stakeholders who push back, decisions you must defend later
- ·The honest rule: match the rigor to the cost of being wrong
Start prioritizing from evidence today
You can begin on the free plan with no card. Upload a handful of interviews or notes, watch the themes form with citations attached, and see your first ranked Feature Bets emerge from frequency, severity, confidence, and counter-evidence instead of numbers you picked to win an argument.
If you want to see the full loop before uploading anything of your own, walk through the proof page or read how Feature Bets work in the docs. Flat self-serve pricing starts at zero and the details are on pricing.
How is this different from RICE or ICE scoring?
RICE and ICE multiply numbers you estimate by hand, then discard the reasoning. Klyr ranks Feature Bets from actual evidence: how often a problem appears across sources (frequency), how much pain the quotes describe (severity), how strong and consistent the signal is (confidence), and what the counter-evidence says. Every factor links back to a verified source quote, so the ranking is something you can inspect and defend rather than a guess dressed up as math.
What counts as evidence, and where does it come from?
Anything where customers tell you what they need: interview transcripts, support tickets, sales call notes, churn surveys, community posts. You upload it and Klyr synthesizes themes with citations, dropping any theme that cannot clear two real source quotes. Feature Bets are then ranked from those cited themes, so the prioritization rests on what people actually said, not on what the room assumed.
Does evidence-based prioritization replace frameworks entirely?
No, and we will not pretend it does. For a handful of low-stakes, reversible decisions, a ten-minute ICE pass is the right tool and a full evidence synthesis is overkill. Klyr earns its place on contested, expensive, or hard-to-reverse bets: the roadmaps several engineers will spend a quarter on, the decisions a stakeholder will challenge, the calls you will be asked to explain months later. Match the rigor to the cost of being wrong.