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Skillsoffer-factory

offer-factory

Marketing & growth

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Updated 10-16 02:44

About this skill

Offer Factory

Turn an IT-agency client into 5–10 outbound-testable offer-bets, fast. Each bet is a distinct wager (not one offer reworded), scored, ranked, and expanded into a card you can ship to outbound this week.

Language: reasoning + commentary in Ukrainian; the offers, promises, and cold hooks in English (outbound usually targets the West). Override if the user says otherwise.

What you need

  • Client website URL (required) — the source of services, verticals, ICP signals, and case studies.
  • Optional: fixed ICPs to build under, constraints ("US only", "no enterprise", "fixed-price only"), or a best-client example.

If the user already gave fixed ICPs, build under those instead of enumerating your own (Phase 2 narrows to them).

Process (7 phases)

Phase 1 — Scrape & extract

Fetch the site (Google Drive read_file_content for Docs; WebFetch / Apify rag-web-browser for live web — note WebFetch only sees Google-Docs chrome, so use the Drive connector for Docs). Pull the raw material:

  • service lines, tech stack, capabilities
  • industries / verticals served
  • ICP signals (who they already serve — size, type, geo, stage)
  • case studies with concrete numbers (this is the proof bank)
  • positioning, stated differentiators

Phase 2 — Axis candidates

From the material, list 3–4 candidates on each of three axes:

  • ICP / vertical — who buys
  • Pain / dream outcome — what result they want
  • Mechanism / service — how it's delivered

These three axes are what make bets different bets. (Packaging/pricing is a 4th optional axis — use only if the user asks.)

Phase 3 — Cheap matrix

Generate ~12–15 one-line combos: We help [ICP] achieve [outcome] via [mechanism]. One line each, no detail yet. Cheap to produce, cheap to discard.

Phase 4 — Score & select

Score every combo, keep the top 5–10:

Value  = geomean(Dream, Likelihood, Time, Effort)      # each 1–10; Time/Effort: 10 = fast / low friction
Signal = geomean(MarketSize, TargetingEase, HookClarity)  # each 1–10
BLEND  = 0.6 * Value + 0.4 * Signal
  • Value = Hormozi value equation (dream × likelihood, divided by delay & effort — encoded as a geometric mean so it stays 1–10).
  • Signal = how fast outbound can produce a signal (is the market findable, targetable, and is the hook punchy).
  • Curate the kept set for axis diversity — don't ship 5 variants of the same ICP×pain. Cover different ICPs, pains, and mechanisms.

Phase 5 — Expand winners into Standard cards

For each kept offer (UA reasoning, EN promise + hook):

#  Offer name
   Promise (EN):  see Promise Formulas below — NEVER default to "We help X..."
   Bet:           what makes this a distinct wager (UA)
   ICP:           who + where to find them
   Pain/trigger:  the pain and the moment it's acute
   Mechanism:     what's delivered
   Proof:         REAL case from the site; where missing → [TODO: need case ___]
   Risk-reversal: guarantee / success criteria (esp. high-ticket)
   Cold hook (EN): 1–2 lines + small CTA (+ 1 alt hook for A/B)
   Score:         D L T E → Value | Signal | BLEND

Phase 6 — Test design (built into every card)

Test:  N = 40–60 targeted leads
       metric: positive reply rate (sprint/trial → also booked calls)
       keep ≥ 10% / kill < 5%  (5–10% = keep angle, swap signal or persona)
       window: 2 weeks / cohort

Phase 7 — Output

Ranked table + full cards. Ask before writing to Google Sheets (the user's "Service – use case – offer" 12-column template). Optionally hand off the winners to hypothesis-builder (signals/personas) then sequence-writer (outbound-engine-skills, sequences).

Promise Formulas (replace "We help X achieve Y via Z")

"We help…" is fine for the internal card line, but never as the outbound promise. Use:

  1. Without (Hormozi): [Dream outcome] — without [main sacrifice]
  2. Transformation: From [bad state] to [desired state] in [time]
  3. Imperative: [Verb] [outcome] — [differentiator]
  4. Pain-removal: Stop [pain]. Start [outcome].
  5. Identity/role: Your [role/team] — [what you do instead of the client]
  6. Risk-reversal: Try [thing] for [small commitment]. Keep it only if [proof].

Built-in rules

High-ticket → entry rung (land-and-expand)

For high-ticket offers with a complex/long sales cycle, never test the full contract cold. Split into two cards:

  • Entry rung — paid pilot / diagnostic / scoped sprint, priced in discretionary budget ($5–20K) so it skips CFO approval. THIS is the cold-outbound offer.
  • Core contract — the big engagement, sold on expand after the rung delivers. For high-ticket test KPI = booked discovery / pilot accepted, NOT closed deal (cycle is 60–180 days). Add a risk-reversal (refund-if-criteria-not-met, milestone/phased) and a multi-thread map (who else is in the deal: CFO→ROI, IT→security/architecture, procurement→SLA, ops→timeline). The Signal score naturally penalizes big commitments — that's the math telling you to lead with the rung.

Low-signal rescue

When an offer has high Value but low Signal (a good offer drowning in a commoditized channel, e.g. generic outstaff), don't fix the offer — fix the wedge on the channel:

  1. narrow the ICP / talent niche (e.g. "LLM engineers" not "developers"),
  2. add risk-reversal (paid trial week),
  3. make the CTA concretely small. Re-score after sharpening; a good wedge typically lifts BLEND by ~1 point.

Proof handling

Use only real case studies from the site. Where a card needs proof you don't have, insert an explicit [TODO: need case ___] placeholder — never fabricate metrics. General proof (e.g. "170+ MVPs, 30+ clients") can back any card.

Worked example

Run on an AI-MVP dev agency site with 3 fixed ICPs → 9 variants scored → top 3 cold-test winners:

  • ICP non-tech founder → Paid Discovery & Architecture Sprint (entry rung, BLEND 7.97)
  • ICP AI-startup CTO → Senior LLM Engineer 1-week trial (low-signal rescue of outstaff, 7.95)
  • ICP marketing agency → White-Label AI Feature Sprint (7.70) Pattern observed: all three winners were low-friction entry rungs; the big ongoing commitments (embedded team, pod) ranked lowest — confirming the high-ticket rule above.

Notes

  • 5–10 offers is the target. Fewer = under-exploring the space; more = no focus.
  • This is a backlog of bets, not a campaign. Only expand what the client can resource to test.
  • Keep the cheap matrix (Phase 3) cheap — don't write full cards before scoring.
  • Pairs with offer-ladder: offer-factory = horizontal batch of bets to test cold; offer-ladder = vertical free→low→mid→high portfolio around one core transformation. If the user wants portfolio/monetization structure (not a test batch), route to offer-ladder. The ladder's cold-facing entry rung loops back here for the test card.