--- name: icp-evidence-log description: "The voice-of-the-customer (VOC) evidence log's theme-clustering discipline pointed at win/loss call notes and discovery excerpts instead of product feedback — turning a scattered set of deal-pattern observations into the vital-few signals that should actually shape the ideal customer profile (ICP) canvas and anti-ICP. Use when: You have real win/loss call notes or discovery-call excerpts and want to test whether the ICP canvas's assumptions actually hold up against what happened in real deals. You suspect a pattern in why deals stall or close fast (a trigger, a buying-committee shape, a segment signal) but haven't clustered the evidence to see if it's real or anecdotal. Not for: You don't have real call notes or deal records to draw from yet — this clusters evidence that already exists, it doesn't generate deal history from nothing. You only have two or three deals total — not enough material for a real pattern to emerge from coincidence." --- # Ideal customer profile (ICP) evidence log The voice-of-the-customer (VOC) evidence log's theme-clustering discipline pointed at win/loss call notes and discovery excerpts instead of product feedback — turning a scattered set of deal-pattern observations into the vital-few signals that should actually shape the ideal customer profile (ICP) canvas and anti-ICP. Family: Voice of the customer · Format: .xlsx · Domain: commercial ## What it is An ICP evidence log is the same clustering discipline as the VOC evidence log — a theme drafted per entry, freely reassignable, with the tally recomputing live — pointed at win/loss call notes, discovery-call excerpts, and competitor mentions instead of product-feedback quotes. The lean connection for ICP is voice of the customer applied to "specify value — lean's step one": an ICP canvas built from assumption instead of real deal evidence is exactly the impression-not-data mistake this catalogue's own discipline exists to catch everywhere else. ## When to use it Use it when: - You have real win/loss call notes or discovery-call excerpts and want to test whether the ICP canvas's assumptions actually hold up against what happened in real deals. - You suspect a pattern in why deals stall or close fast (a trigger, a buying-committee shape, a segment signal) but haven't clustered the evidence to see if it's real or anecdotal. - You want the vital-few fit signals named with a defensible tally, not just a sales leader's gut sense of 'our best deals all look like X.' Not when: - You don't have real call notes or deal records to draw from yet — this clusters evidence that already exists, it doesn't generate deal history from nothing. - You only have two or three deals total — not enough material for a real pattern to emerge from coincidence. - You already know the answer and just want it confirmed — genuine synthesis means letting the entries suggest the clusters, not the other way around. ## How to fill it in - **scope** — Which deals or accounts does this evidence cover, and what prompted pulling it together — sharpening the ICP canvas, testing anti-ICP disqualifiers, something else? - **entries** — Enter each win/loss note, discovery excerpt, or competitor mention you've already gathered, with its source, source type, and date. - **themeSummary** — Drafted from your notes, then genuinely yours to edit — reassign any entry's theme and the clustering updates immediately. - **narrative** — Drafted from the top themes — what the clustering actually suggests about who the ICP is. - **nextSteps** — What will the team do with this synthesis — update the ICP canvas, add a disqualifier, change a qualification step — ranked by impact and effort, owned and dated? ## What good looks like The example below clusters nine win/loss and discovery-call entries into four themes: a positive trigger signal (already-outgrown spreadsheet workarounds), a strong predictor of a clean close (an exec sponsor engaged early) paired with its inverse failure pattern (a stalled single-threaded champion), and two smaller but real anti-ICP signals — a company-size floor and an unplanned enterprise security-review gap. Full worked example, on-screen and as a downloadable .xlsx: https://www.katafacts.com/katas/icp-evidence-log ## Common mistakes - **Clustering win notes only, leaving losses out of the evidence set.** — A pattern that only explains why deals win says nothing about why similar-looking deals lose — the anti-ICP disqualifiers this evidence should feed depend on the loss entries just as much as the wins. - **Treating a single memorable deal as a pattern.** — Same vital-few discipline as every other artifact in this catalogue — one vivid story isn't a cluster; it needs enough independent entries to mean something beyond one account's specifics. - **Pulling quotes from memory instead of the actual call notes or customer relationship management (CRM) system's history.** — A pattern built from what a sales leader remembers inherits every bias in that memory — the same reason every other evidence-gathering artifact in this catalogue insists on real notes, not recall. ## What it connects to Downstream: - **Ideal customer profile (ICP) canvas** (`icp-canvas`) — A theme with real weight behind it — a trigger, a buying-committee pattern — is exactly what should update the ICP canvas's definition, not sit unused in a log. - **Anti-ideal customer profile (anti-ICP)** (`anti-icp`) — A loss-side theme (a size floor, a process gap) is a disqualifier candidate — the anti-ICP list should trace back to evidence like this, not a sales leader's unstated assumptions. - **Deal A3 — win-rate root cause analysis** (`deal-a3`) — A theme that concentrates in lost pipeline value, not just deal count, is exactly the kind of finding a deal A3's Pareto would chase to root cause. ## Where AI helps Judgement — stays with you: - Deciding whether a drafted cluster genuinely reflects a real deal pattern, or moving an entry to a different theme - Deciding which theme is real fit signal versus coincidence worth watching, not yet acting on Analysis — AI helps: - Drafting a first-pass theme label per entry from its actual content - Drafting the narrative from the top themes - Flagging when a theme is thin (one or two entries) rather than a defensible pattern Drudgery — automated: - Computing theme counts and percentages, recomputed live on every reassignment - Identifying the vital-few top themes - Exporting to xlsx in the house format --- Ideal customer profile (ICP) evidence log 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/icp-evidence-log.