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Reduce Refund Risk

Cut refunds at their source by fixing the expectation gaps and onboarding drop-offs that cause buyers to ask for money back.

Pro moderate Offer Improvement Recipes

What it adds

Fixes the offer bleeding margin to refunds that are treated as a support problem rather than an offer problem. The AI builder groups existing refund reasons and support threads from the source files into expectation, activation, and fit failures, then repairs each at the point where it originates. The outcome is a lower refund rate achieved by correcting the offer, not by making refunds harder to request.

What your builder is told to do

8

The actual instructions, in order.
  1. 1

    Inspect the source files and sort every refund into one of three buckets: expectation gap (they got something different from what they understood), activation failure (they never reached first value), or fit failure (they were never the right buyer). Record the count per bucket.

  2. 2

    Address the largest bucket first and treat the other two in order, so effort follows evidence rather than intuition.

  3. 3

    For expectation gaps, rewrite the specific page sections that overstate or leave ambiguous what is delivered, and add a plain what-this-is-not statement drawn from the refund reasons themselves.

  4. 4

    For activation failures, define the first meaningful result a buyer should reach, shorten the path to it, and add a check that flags accounts that have not reached it within a set window.

  5. 5

    For fit failures, add qualification before purchase — an explicit who-this-is-for and who-it-is-not-for section using criteria taken from the refund records.

  6. 6

    Separate structural changes from copy changes: structural work is onboarding sequencing, the activation checkpoint, and any qualification gate; copy work is the expectation corrections, the not-for statement, and the plain-language policy restatement.

  7. 7

    Specify responsive behavior: on mobile, the what-you-get and what-this-is-not sections sit adjacent in a single column above the buy control; on tablet, they render as paired columns; on desktop, both appear beside the price so the buyer reads scope and price together.

  8. 8

    Define tracking: monitor refund rate by bucket, time-to-first-value, and the share of buyers reaching the activation checkpoint. Rollback rule — if refunds fall but qualified conversions fall further, relax the qualification gate first and keep the expectation and activation fixes in place.

Edge cases it handles

5

The things an agent skips when you only say "build a reduce refund risk".
  • Do not reduce refunds by making the policy harder to invoke, lengthening the process, or hiding the request path; that trades margin for reputation.
  • Preserve every existing guarantee and refund term exactly as the source files state them; this recipe changes causes, not terms.
  • If refund reasons are unrecorded, state that as the primary finding and implement reason capture before attempting fixes.
  • Where a refund cause traces to delivery quality rather than offer framing, flag it for the owner instead of patching it with copy.
  • Do not add a testimonial, case study, or result claim to counteract refunds; strengthen accuracy, not persuasion.

Definition of done

7

Your builder is required to check every one of these before reporting the work finished.
  • Refunds are classified into expectation, activation, and fit buckets with counts recorded per bucket.
  • The largest bucket has a named fix and every fix traces to a specific refund reason in the source files.
  • A what-this-is-not statement appears on the page, derived from actual refund reasons.
  • Before and after refund rates are captured by bucket, not just in aggregate.
  • Mobile, tablet, and desktop placements of the scope and not-for sections are each specified.
  • The refund policy remains as easy to invoke as before, and a rollback trigger protecting conversion is written down.
  • The builder reports which source files it used and flags any refund cause it could not classify from the evidence.

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.

Build This Offer: Create An Order Bump

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.

Build This Offer: Split Into Tiers

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.