LeadHaste
IntermediateNo variables to fill — paste & go

Localize a winning sequence for a new vertical

Ports a proven sequence into a new vertical without the find-and-replace trap: it extracts the structural mechanics that made your winner work, maps how pain, vocabulary, and proof shift in the new industry, rewrites every touch on the same skeleton, and flags the proof gaps and assumptions to validate before you scale sends.

The prompt
You are a vertical-expansion strategist who has helped outbound teams take a sequence that works in one industry into new ones — and watched the naive approach fail every time. Find-and-replace localization ('swap SaaS for healthcare') keeps the surface and loses the substance: the pains shift, the vocabulary shifts, the proof that lands shifts, the buyer's skepticism shifts. Real localization re-derives each touch from the new vertical's reality while protecting the mechanics that made the original work.

Localize my winning sequence. Method:

1. First, extract the sequence's mechanics — what each touch does structurally (problem opener, proof touch, angle shift, close) and why it likely works. Show me this skeleton before writing anything new.
2. Then map the translation: for each touch, what changes in the new vertical — the pain expression, the vocabulary, the proof type, the objection lurking underneath — and what stays.
3. Rewrite every touch for the new vertical. Same skeleton, new flesh. Word counts within 10% of the originals.
4. Flag what I'm missing: where my proof is weak for this vertical (no case studies yet, wrong logos), which claims need adjusting for regulated industries, and the one assumption about this vertical I should validate with 5 discovery calls before scaling sends.

Rules: keep the original's CTAs and cadence unless the new vertical's buying culture demands otherwise — and if it does, say why. Never bolt industry jargon onto generic copy as decoration; if a line would survive with the industry word deleted, it isn't localized. If my new vertical knowledge (question 4) is thin, interrogate me harder before writing.

Before you write anything, interview me. Ask me these questions ONE AT A TIME, waiting for my answer each time:
1. Paste the winning sequence, with its performance numbers and the vertical it wins in.
2. What's the new vertical, and which role are you targeting there?
3. What do you sell, and does the offer change at all for this vertical?
4. What do you actually know about this vertical's version of the problem — from calls, customers, or research?
5. What proof do you have that's relevant to this vertical, even partially?

Once you have my answers, deliver the skeleton, the translation map, the rewritten sequence, and the gaps. If the mechanics of my winner depend on something this vertical doesn't have, tell me that before I burn a fresh list finding out.

How to use it

  1. 1

    Copy the prompt into Claude, ChatGPT, or any LLM.

  2. 2

    Paste the winning sequence with its real numbers — the mechanics extraction depends on knowing what actually worked.

  3. 3

    Give question 4 everything you have on the new vertical: call notes, customer quotes, research; thin input here produces decorated copy, not localization.

  4. 4

    Review the skeleton and translation map before accepting the rewrites — that's where wrong assumptions surface cheapest.

  5. 5

    Run the flagged validation calls before scaling past a test segment of 100 to 200 prospects.

Best practices

  • Localize from your winner, not your newest sequence — mechanics proven by data are the asset being ported.

  • The 'delete the industry word' test is the quality bar: every localized line should collapse without its vertical-specific substance.

  • Borrow proof carefully across verticals: 'we did this for SaaS' can work in manufacturing if framed as a transferable mechanism, but the prompt will flag when it won't.

  • Send the localized sequence to a small test segment first and compare against the original vertical's baseline, not against zero.

Example: what this looks like in practice

A RevOps lead at a 40-person accounts-payable automation company has a sequence pulling 4.2% replies with e-commerce CFOs and wants to enter manufacturing. She pastes the winner and admits her manufacturing knowledge is three discovery calls deep. The skeleton reveals her touch 1 works via a cash-flow-timing pain and touch 2 via a fast-implementation proof point. The translation map shifts the pain to three-way-match failures and supplier-relationship risk, keeps the cadence, and flags two gaps: her e-commerce logos carry little weight with plant-economics buyers, and implementation speed matters less than ERP compatibility. The rewritten sequence tests at 2.8% on 150 manufacturing prospects — below her e-commerce baseline but instantly her best cold number in the new vertical.

Best fit

Roles
Sales LeaderRevOpsFounder / CEOSDR / BDR
Company size
Startup (1–10)SMB (11–50)Mid-market (51–500)
Audience
B2B
Industries
Any industry
Works with
Any LLM
Difficulty
Intermediate

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Prompt FAQ

Frequently asked questions

Don't swap the industry nouns — re-derive the substance. Extract what each touch does mechanically (which pain it opens, what proof it uses, how it closes), then rebuild each element from the new vertical's reality: how those buyers express the problem, what evidence they trust, what objection sits underneath. Keep the structure and cadence that made the original win; replace the flesh, not the skeleton.