--- name: anti-icp description: "The scoping canvas's one-page, multi-section discipline pointed at who not to pursue — hard disqualifiers that should end a deal outright, softer red flags worth a specific countermeasure instead of an automatic pass, process red flags, and real churn-pattern evidence, not a mirrored ICP canvas with the polarity flipped. Use when: You have real win/loss and churn evidence and want to turn the patterns in it into concrete, actionable disqualifiers and red flags — not a rep's general sense of 'red flags.' Not for: You don't have real churn or loss evidence yet — an ICP evidence log first, then an anti-ICP built from what it actually shows, beats guessing at disqualifiers from scratch. You're tempted to just invert the ICP canvas's own sections — a real anti-ICP is disqualification rules and churn evidence, not a mirrored canvas; forcing that shape misses the hard/soft distinction that makes this genuinely useful." --- # Anti-ideal customer profile (anti-ICP) The scoping canvas's one-page, multi-section discipline pointed at who not to pursue — hard disqualifiers that should end a deal outright, softer red flags worth a specific countermeasure instead of an automatic pass, process red flags, and real churn-pattern evidence, not a mirrored ICP canvas with the polarity flipped. Family: Problem definition · Format: .xlsx · Domain: commercial ## What it is An anti-ICP names who not to pursue, or who to pursue only with a specific countermeasure — the same one-page, multi-section discipline as the ICP canvas, deliberately not a mirrored version of it with the polarity flipped. Not every red flag deserves the same weight: a hard disqualifier (an account under a firm size floor, no one to own the tool post-purchase) should end a deal outright, while a softer red flag (a single-threaded champion, a vague trigger) predicts risk without being disqualifying on its own — proceed, but with a specific next step. A real anti-ICP also carries churn-pattern evidence: not just why a deal didn't close, but why a customer who did buy later left, which is often the clearest signal of all for what to watch for earlier. ## When to use it Use it when: - You have real win/loss and churn evidence and want to turn the patterns in it into concrete, actionable disqualifiers and red flags — not a rep's general sense of 'red flags.' - Reps keep spending real time on deals that predictably won't close or won't stick, and there's no written list of the specific patterns that predicted it before. - You want to separate what should end a deal outright from what should just trigger a specific countermeasure, instead of treating every red flag the same way. Not when: - You don't have real churn or loss evidence yet — an ICP evidence log first, then an anti-ICP built from what it actually shows, beats guessing at disqualifiers from scratch. - You're tempted to just invert the ICP canvas's own sections — a real anti-ICP is disqualification rules and churn evidence, not a mirrored canvas; forcing that shape misses the hard/soft distinction that makes this genuinely useful. - You already have a recent, accurate disqualifier list elsewhere — don't duplicate an existing source of truth. ## How to fill it in - **hard-disqualifiers** — What firmographic or technographic floor should end a deal outright, no matter how it's trending? - **soft-red-flags** — What risk signal predicts trouble but shouldn't kill a deal on its own — and what's the specific next step when one shows up? Not a disqualifier, a trigger for a specific countermeasure. - **process-red-flags** — What timing or procedural pattern, independent of firmographic fit, has predicted a bad outcome before? - **churn-patterns** — Why have real customers actually left — not a guess, the real reasons on file. ## What good looks like The example below is deliberately built from real deal evidence rather than assumption, splitting genuine firm disqualifiers from softer red flags that call for a specific countermeasure — and it shows the same single-threaded-champion failure pattern appearing on both sides of the sale, once as a pre-sale red flag and again as a post-sale churn pattern. Full worked example, on-screen and as a downloadable .xlsx: https://www.katafacts.com/katas/anti-icp ## Common mistakes - **Treating every red flag as an automatic disqualifier, including soft ones like a single-threaded champion.** — Over-disqualifying kills winnable deals that just need a specific countermeasure (multi-threading, a firmer trigger) — flattening hard and soft red flags into one list throws away exactly the distinction that makes this artifact useful. - **Only capturing pre-sale loss reasons and skipping churn evidence entirely.** — Churn evidence is often the clearest signal of all for what to watch for earlier — a customer who bought and later left for a specific, findable reason is more informative than a deal that just didn't close. - **Running the anti-ICP check from memory instead of the actual defined disqualifiers.** — Anti-ICP patterns exist because specific past deals and accounts already failed this way — a rep's general sense of 'red flags' isn't the same as disqualifiers actually derived from real evidence. ## What it connects to Upstream: - **Ideal customer profile (ICP) canvas** (`icp-canvas`) — The ICP canvas defines who to pursue; this defines who not to, or who to pursue only with a specific countermeasure — a deliberately separate list, not this canvas's mirror image. - **Ideal customer profile (ICP) evidence log** (`icp-evidence-log`) — The same real win/loss evidence that sharpens the ICP canvas is exactly what a disqualifier or red flag here should trace back to — not a rep's unstated impression of what goes wrong. Downstream: - **Qualification standard work** (`qualification-standard-work`) — The disqualifier check in qualification standard work is only as good as this list — a stale or missing anti-ICP means that step has nothing real to run against. ## Where AI helps Judgement — stays with you: - Deciding whether a red flag is genuinely a hard disqualifier or a softer one worth a specific countermeasure - Deciding whether a churn pattern is specific enough to act on, not just a vague impression of why an account left Analysis — AI helps: - Drafting each section's bullets from a rough description or real loss/churn notes - Flagging a section that's too thin to mean anything yet, rather than padding it Drudgery — automated: - Formatting the canvas and keeping every section on one page - Exporting to xlsx in the house format --- Anti-ideal customer profile (anti-ICP) 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/anti-icp.