Outbound strategy from intent

Use this skill when you need an outbound targeting strategy from just a company's website — "build our outbound strategy," "who do we target and why now." Given a domain, it returns a structured foundation: ICPs, personas, pains, value propositions, and a ranked shortlist of intent signals to run — each signal tied to a why-now.

SKILL.md
name:
outbound-strategy
description:
Use this skill when you need an outbound targeting strategy from just a company's website — "build our outbound strategy," "who do we target and why now." Given a domain, it returns a structured foundation: ICPs, personas, pains, value propositions, and a ranked shortlist of intent signals to run — each signal tied to a why-now.

Outbound strategy from intent

An agent that turns a single domain into the targeting half of an outbound strategy: who to go after, why they buy, and which intent signals reach them while they are in-market. It stops at foundations plus signals — messaging and campaigns are separate steps.

Input

  • website_url — the company the strategy is for.
  • (optional) market_geo, acv_range, sales_motion (PLG / sales-led / hybrid), icp_constraints.

Procedure

  1. What they sell — category, top use cases, who it is for, pricing and motion clues. Prefer the site, docs, pricing, case studies, reviews.
  2. Who buys — economic buyer vs. daily user; which teams feel the pain; industries named or implied.
  3. Competitors and alternatives — direct rivals, plus the "do nothing / build in-house / spreadsheet / incumbent" alternatives, and the switching trigger.
  4. Segment into ICPs by shared buying reason, not by firmographic label. A segment is real when its members would buy for the same why. Bound each one — company type, industries, employee-size bands, geographies — tightly enough that two people would build the same list from it. Rank by fit × reachability: a perfect-fit segment you cannot find at volume is a worse ICP than a good-fit one you can.
  5. Draw personas, one title to one persona. Assign titles in the person's own language, not the company's org-chart language. A given title belongs to exactly one persona — if "Head of Growth" could sit in two, the personas are wrong; redraw them. Keep economic buyer, daily user, and champion distinct, and state what each cares about (buyer: outcome/ROI; user: friction; champion: looking good internally).
  6. Map value props to pains. Take the pains in the buyer's words, and for each write the value prop that resolves it — the outcome the buyer gets, phrased as a change in their world, not a feature name. A value prop with no pain behind it is a feature: drop it or find its pain. Where a metric, customer, or result backs a claim, cite it; where none exists, mark it unproven rather than dressing it up.
  7. Shortlist signals — 3-5 detectable signals, each with a why-now.

Label anything you cannot verify from public sources as an Assumption and keep it plausible.

Output

  • icps[]{name, buying_reason, company_type, industries, size_bands, geos, evidence, rank}
    • buying_reason — the shared why that makes it one segment
    • evidence — what on the site supports it, or "Assumption"
    • rank — 1-5 (fit × reachability)
  • personas[]{name, titles, role_in_deal, in_icps, cares_about}
    • titles — the variants that map here; no title repeated in another persona
    • role_in_deal — economic buyer / user / champion
    • cares_about — the one thing that moves them
  • pains[]{name, description}
  • value_props[]{name, outcome, resolves_pains, proof}
    • outcome — the change the buyer experiences
    • proof — the metric/customer/result, or "unproven"
  • signals[]{signal, why_now, detect_in, target, rank} (3-5)

What good looks like

  • Every value prop links back to a pain; every persona to an ICP. A strategy whose pieces do not connect is decoration.
  • ICPs are segments, not filters. Firmographics alone describe a list; the buying reason explains why that list converts.
  • No title appears in more than one persona. Overlapping title lists silently double-target and corrupt reply-rate reads — the most common failure in practice. Good output: a targeting engine could route each title to exactly one persona with no ambiguity.
  • Titles are the person's language, not the company's. "RevOps lead" over "Manager, Revenue Systems" if that is what they call themselves.
  • Outcomes, not features. "Cut ramp time in half," not "onboarding module." One cited result outweighs three superlatives.
  • The intents are actually detectable in outbound — not inbound wishes dressed up as signals.
  • Assumptions are labeled, not hidden — a reader can see what is verified vs. inferred.
  • Good output is narrow: a few sharp ICPs and 3-5 signals someone could act on Monday, not twelve of everything.

Rules

  • MUST give every ICP a buying reason; NEVER define one by firmographics alone.
  • MUST keep every title in exactly one persona, and separate economic buyer, user, and champion.
  • MUST use the person's self-description for titles, not internal org labels.
  • MUST map every value prop to a pain and every persona to an ICP.
  • MUST phrase value props as buyer outcomes, not features.
  • MUST label unverified claims as Assumption and unbacked results as unproven; NEVER invent a metric or customer.
  • MUST keep the signal shortlist to 3-5 detectable-in-outbound signals.
  • NEVER pad ICPs or personas for coverage — precision over breadth.