Narrow The Ideal Customer

Narrow a for-everyone offer to one specific buyer so the copy stops hedging and starts sounding written for the reader.

Pro involved Offer Improvement Recipes

What it adds

Fixes the broad offer whose language is generic because it tries to speak to every possible buyer at once, resonating with none. The AI builder examines existing customer descriptions, questions, and objections in the source files to find the segment that converts most readily, then rewrites the offer to address that segment alone. The outcome is sharper copy, easier qualification, and higher conversion within a smaller audience.

What your builder is told to do

The actual instructions, in order.

1
Inspect the source files and segment existing buyers by situation, not demographics — what they were trying to do, what they had already tried, and what triggered the purchase.
2
Score each segment on three factors drawn from the files: how readily they buy, how well delivery serves them, and how clearly the offer already fits their situation. Select the highest-scoring segment.
3
Write the selected segment's situation in three lines — where they are, what they have tried, what they want next — using their vocabulary from the enquiry notes rather than internal category names.
4
Separate structural changes from copy changes: structural work is qualification questions, routing for non-fit enquiries, and any scope adjustment specific to this segment; copy work is the headline, the who-this-is-for section, examples, objection answers, and terminology.
5
Rewrite every generality into a segment-specific statement, replacing hedged phrasing with the concrete detail this segment recognises, and add an explicit who-this-is-not-for line.
6
Give excluded segments a respectful route — a different offer, a resource, or a plain statement of what would suit them better — so narrowing does not simply discard demand.
7
Specify responsive behavior: on mobile, the who-this-is-for block sits immediately under the headline on the first screen; on tablet, for and not-for lists render as paired columns; on desktop, both appear alongside the offer summary so fit is judged before price.
8
Define tracking: measure enquiry volume, enquiry-to-sale rate, and the share of enquiries matching the target segment. Rollback rule — widen the definition by one adjacent segment if total qualified sales fall below baseline despite an improved conversion rate.

Edge cases it handles

The things an agent skips when you only say "build a narrow the ideal customer".

Do not turn away buyers already served under prior terms; narrowing applies to new acquisition, not to existing commitments.
Preserve factual claims about method and deliverables; narrowing changes who is addressed, not what is delivered.
If the source files lack segmentation data, report that gap and base the selection on observable delivery fit, marking it as an assumption.
Avoid narrowing on demographic proxies when the files show situation is what predicts fit.
Never phrase exclusion in a way that disparages the segments being routed away.

Definition of done

Your builder is required to check every one of these before reporting the work finished.

Segments are derived from the source files by situation and scored on readiness, delivery fit, and current alignment.
The selected segment's situation is written in three lines using the buyers' own vocabulary.
Both a who-this-is-for and a who-this-is-not-for statement appear on the page.
Before and after copy is recorded for the headline and at least three previously generic sections.
Mobile, tablet, and desktop placements of the fit block are each specified.
Enquiry-quality tracking and a widening rollback trigger are defined before launch.
The builder reports which source files it used and flags any segmentation assumption it could not verify.

Related offer files

Offer Improvement Recipes

Create An Order Bump

Insert a single low-friction add-on at checkout that completes the purchase without pulling attention off the main decision.

Offer Improvement Recipes

Split Into Tiers

Break a single take-it-or-leave-it offer into tiers that capture buyers at different budgets without gutting the core promise.

Offer Improvement Recipes

Repackage As A Bundle

Merge scattered standalone products into one coherent bundle solving a complete problem, priced below the sum of its parts.

How it works

1

Copy the link

Grab the Markdown blueprint URL for this offer type.
2

Give it to your builder

Paste it into Claude Code, Cursor, Codex, or whatever AI builder is already working in your app.
3

It maps, then builds

Your agent reads the blueprint first, then builds the offer into your app around the product, audience, and stack.

Works with your stack

These blueprints are written to adapt. They tell the agent to detect your framework, match your existing design system, and use your source files instead of guessing the offer.

Need it tighter than that? Customize the offer file and tell it exactly which source docs and stack to use.