--- name: fit-criteria-scorecard description: "The weighted decision matrix discipline pointed at prospect accounts: score each one against firmographic fit, technographic fit, trigger strength, and buying-committee access, and let a computed composite score and tier — not a gut feel — say which account actually deserves the next call. Use when: You have a real list of named prospect accounts (from a TAM coverage map, an inbound queue, or an outbound list) and need to decide which ones actually deserve outreach effort next. Not for: You don't have a real ICP canvas yet — build that first; this tool scores accounts against criteria the canvas should already have defined, not invents them here. You're evaluating a single account in depth for a live deal — a deal A3 or a value hypothesis is the right depth for a deal already in motion, not a comparative score against other accounts." --- # Fit-criteria scorecard The weighted decision matrix discipline pointed at prospect accounts: score each one against firmographic fit, technographic fit, trigger strength, and buying-committee access, and let a computed composite score and tier — not a gut feel — say which account actually deserves the next call. Family: Problem solving / root cause analysis · Format: .xlsx · Domain: commercial ## What it is A fit-criteria scorecard is the operational, repeatable counterpart to the ICP canvas's definitional, built-once profile — the same weighted decision matrix discipline, pointed at real prospect accounts instead of a generic decision. Score each account 1 to 5 against firmographic fit, technographic fit, trigger strength, and buying-committee access, weighted by how much each one actually predicts a fast, clean win in your own deal history — not split evenly by default. The composite score and priority/consider/deprioritize tier are computed from those weights and scores, so the account worth calling next is a defensible answer, not a hunch. ## When to use it Use it when: - You have a real list of named prospect accounts (from a TAM coverage map, an inbound queue, or an outbound list) and need to decide which ones actually deserve outreach effort next. - You've already built an ICP canvas and know which signals — a specific trigger, an exec sponsor, a specific tech stack — actually predict a fast, clean win in your own deal history, not just in theory. - You want to say plainly that some tracked accounts aren't worth chasing yet, not flatter every account on the list with a similar-looking score. Not when: - You don't have a real ICP canvas yet — build that first; this tool scores accounts against criteria the canvas should already have defined, not invents them here. - You're evaluating a single account in depth for a live deal — a deal A3 or a value hypothesis is the right depth for a deal already in motion, not a comparative score against other accounts. - Every criterion in your ICP genuinely matters equally — an even split is a real, defensible weighting choice, but check that it's actually true rather than the path of least resistance. ## How to fill it in - **decision** — Which accounts are being prioritized, and against what list or segment? - **criteria** — Pull straight from your ICP canvas's sections — firmographic fit, technographic fit, trigger strength, buying-committee access — weighted by how much each one actually predicts a win in your own deal history, not split evenly by default. - **options** — For each account, score it 1 (weakest) to 5 (strongest) against every criterion — your own honest read of the account, never AI-scored. The composite score and tier are computed, never entered directly. - **narrative** — Drafted from the computed ranking, naming which account deserves the next call and why. ## What good looks like The example below scores three of a TAM coverage map's own tracked mid-market logistics accounts — including that map's own 'dark account' with zero contacts, which scores lowest here rather than being flattered by an even-handed score. Full worked example, on-screen and as a downloadable .xlsx: https://www.katafacts.com/katas/fit-criteria-scorecard ## Common mistakes - **Weighting every criterion evenly by default instead of checking which ones actually predict a win.** — Real deal history usually shows one or two signals (an exec sponsor engaged early, a specific trigger) matter far more than the others — an even split throws that evidence away. - **Scoring firmographic fit generously just because the account is a named target on a list.** — Being on a target list means an account was addressable, not that it's actually a strong fit — conflating the two defeats the reason to score it at all. - **Letting a strong score on one criterion (usually firmographic fit) override a weak trigger or no buying-committee access.** — The exact failure this tool exists to prevent — a composite score should be able to rank a smaller, better-fit account below a larger one with a real trigger and real access, not the other way around. ## What it connects to Upstream: - **Ideal customer profile (ICP) canvas** (`icp-canvas`) — The ICP canvas defines the criteria this scorecard weighs — firmographics, technographics, triggers, and buying-committee roles — as a one-time definitional exercise; this scorecard applies them repeatedly to real accounts. - **Total addressable market (TAM) coverage map** (`tam-coverage-map`) — The TAM coverage map surfaces which accounts are even tracked and how much contact access exists at each; a fit-criteria scorecard is how you decide which of those tracked accounts to invest further outreach in next. Downstream: - **Value hypothesis** (`value-hypothesis`) — Once a fit-criteria scorecard names an account worth pursuing, a value hypothesis is where the real discovery work on that specific account begins. ## Where AI helps Judgement — stays with you: - Deciding the real weight each ICP criterion deserves, based on actual deal history - Deciding an honest fit score for each account against each criterion Analysis — AI helps: - Drafting the narrative from the ranking actually computed, naming which account deserves the next call and why Drudgery — automated: - Computing each account's composite score from its weights and scores, every time one changes - Sorting accounts into priority, consider, and deprioritize tiers - Exporting to xlsx in the house format --- Fit-criteria scorecard is licensed [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Attribution: Katafacts (katafacts.com). ## Using this skill Apply this method as part of whatever broader task, instructions, or deliverable you're already working on -- it's a method to use, not a standalone conversation to start. There is no generation tool for this catalogue yet, so draft the artifact's actual content yourself, following the fields above, the same way a person filling this out by hand would. The canonical guide page and a downloadable worked example (.xlsx) are at https://www.katafacts.com/katas/fit-criteria-scorecard.