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Quiz-Led Offer

Route visitors to the right product by asking what they are trying to do, not who they think they are.

Pro involved Special Offer Structures

What it adds

The AI builder converts your product catalog, use-case documentation, and fit criteria into a branching questionnaire that ends at one recommended option with reasoning attached. It converts because choice paralysis, not price, is what stops multi-option catalogs from selling. The goal is to raise conversion on the correct product and reduce returns caused by mismatched purchases.

What your builder is told to do

8

The actual instructions, in order.
  1. 1

    Read the supplied catalog and use-case files and build a mapping of products to the tasks each is best suited for, plus the conditions where each is a poor choice.

  2. 2

    Write questions about tasks and constraints, never about identity or personality. Ask what the visitor is trying to accomplish and what limits they operate under.

  3. 3

    Implement branching so answers eliminate options rather than scoring them into a blur. Each question should meaningfully reduce the remaining set.

  4. 4

    Keep the flow short enough to finish — target the fewest questions that fully disambiguate the catalog — and show remaining progress at all times.

  5. 5

    Present the recommendation with reasoning: name the answers that led there and name the runner-up with the condition under which it would have won instead.

  6. 6

    Include a no-recommendation outcome for visitors whose needs the catalog does not serve, with a documented alternative path.

  7. 7

    Set breakpoints: on mobile one question per screen with large answer cards and a back control that preserves prior answers; on tablet two questions per screen with a side progress rail; on desktop the full flow in a centered column with an inline results panel and a visible answer summary the visitor can edit.

  8. 8

    Allow the visitor to change any answer and see the recommendation update without restarting.

Edge cases it handles

5

The things an agent skips when you only say "build a quiz-led offer".
  • Two products remain tied at the end; present both with the distinguishing factor rather than picking arbitrarily.
  • Answers indicate a discontinued product is the best fit; route to its documented successor and say so.
  • Return data shows a product is frequently mismatched; add a confirming question before recommending it.
  • The visitor abandons midway; preserve answers for the session and allow resumption.
  • Catalog changes after launch; the mapping must live in one editable structure rather than scattered in branch logic.

Definition of done

7

Your builder is required to check every one of these before reporting the work finished.
  • All questions concern tasks or constraints, not identity or personality.
  • Branching eliminates options based on the supplied product-to-use-case mapping.
  • The recommendation states its reasoning and names a runner-up with its winning condition.
  • A no-recommendation outcome with an alternative path exists.
  • Answers are editable and the recommendation updates without restarting the flow.
  • Mobile, tablet, and desktop each complete the full flow with working back navigation.
  • The builder reports which catalog and use-case files it used and which mappings are assumed.

Related offer files

How it works

  1. 1

    Copy the link

    Grab the Markdown blueprint URL for this offer type.

  2. 2

    Give it to your builder

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

  3. 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.