Product not received — POD over screenshots
Receiver-aware path: require original POD, match ZIP to billing, then surface signature capture and one bounded ask.
Open exampleCase studies (templates, anonymized)
No forms. No case-content capture. These are templates you can copy and fill manually. Focus: dispute reduction + faster resolution via a receiver-ready Review Rail. Aggregate-only CTA telemetry may record which public lane you open next, but never the case itself.
Download: Case Study Builder (printable HTML)
A one-page template you can edit offline and print to PDF.
3 illustrative mini-stories (examples only)
These are illustrative examples to show how merchants describe ROI without tracking. They are not real customer claims. Replace with your own data only after verification and permission.
Example 1 — Electronics accessories shop (INR + “Empty box”)
Illustrative example — not a real customer claim. Replace with your own data.
- Before: frequent INR claims + missing-accessories disputes; support time fragmented across screenshots and messages.
- Used: packing proof hashes + insert slip + Public Bundle (PDF/PNG/JSON) + local-only Private Appendix for carrier references.
- After (qualitative): faster replies; fewer repeated follow-ups; less staff variance in what to attach.
Example 2 — Fashion store (SNAD + “Used item returned”)
Illustrative example — not a real customer claim. Replace with your own data.
- Before: returns disputes about condition; internal debates about what existed when.
- Used: return intake hashes + policy snapshot hash + Public Bundle for each return incident.
- After (qualitative): fewer escalations; faster internal resolution; clearer documentation trail.
Example 3 — High-value items (Unboxing proof)
Illustrative example — not a real customer claim. Replace with your own data.
- Before: high-value disputes are stressful; inconsistent process across staff.
- Used: high-value preset + deterministic completeness score + playbook response text.
- After (qualitative): standardized packaging; less staff confusion; quicker responses with integrity scope.
Receiver-aware review rail examples (Shopify + Klarna)
These are illustrative examples that show how a receiver-aware standard looks in practice. The point is not to send more files. The point is to satisfy the reviewer’s reason-path with the right evidence joins, in the right order, with fewer ambiguous attachments.
Shopify — subscription not canceled / credit not processed
Illustrative example — not a real customer claim.
- Old mess: cancellation email on one side, activity logs on another.
- Review rail: require signed cancellation policy + cancellation request timestamp + last service usage event.
- Decision effect: if service usage continued after the claimed cancellation, the reviewer sees that consumption clearly instead of reconstructing it manually.
Shopify — product not as described
Illustrative example — not a real customer claim.
- Old mess: customer photo vs studio photo; endless debate about color and lighting.
- Review rail: require received-item photo + label/spec photo + purchase-time specification table from the store.
- Decision effect: the reviewer sees a side-by-side structured comparison instead of unframed images.
Shopify — refund not processed
Illustrative example — not a real customer claim.
- Old mess: merchant says “refund sent”; customer says “nothing received”.
- Review rail: connect warehouse return receipt, refund approval event, and acquiring/reference trace into one packet.
- Decision effect: the reviewer can see whether the funds already left the merchant environment and where the delay likely sits.
Klarna — return not registered
Illustrative example — not a real customer claim.
- Old mess: return tracking in one inbox, warehouse receipt in another, invoice still active.
- Review rail: require return tracking + warehouse receipt + intake timestamp.
- Decision effect: a clean return evidence pack can justify pausing the invoice before a long manual review.
Klarna — unauthorized transaction / friendly fraud
Illustrative example — not a real customer claim.
- Old mess: scattered address records, delivery photo, and weak argument framing.
- Review rail: compare order-side address, delivery address, and delivery proof with signature/photo where available.
- Decision effect: if the addresses line up and the delivery proof is strong, the reviewer sees a cleaner friendly-fraud pattern without identity overclaims.
Klarna — partial order / missing item
Illustrative example — not a real customer claim.
- Old mess: “one item missing” becomes a back-and-forth with no physical logic.
- Review rail: compare outbound parcel weight, packing slip, and missing item weight.
- Decision effect: if the shipped parcel weight matches the full order, the shortage claim becomes weaker on technical grounds.
Case study #1 (template)
Scenario: ______________________
BEFORE: disputes/month __ ; support hours __ ; chargebacks __
AFTER: bundle usage/month __ ; outcomes __
Notes: ______________________
Case study #2 (template)
Scenario: ______________________
BEFORE: disputes/month __ ; support hours __ ; chargebacks __
AFTER: bundle usage/month __ ; outcomes __
Notes: ______________________
Case study #3 (template)
Scenario: ______________________
BEFORE: disputes/month __ ; support hours __ ; chargebacks __
AFTER: bundle usage/month __ ; outcomes __
Notes: ______________________
Case study #4 (template)
Scenario: ______________________
BEFORE: disputes/month __ ; support hours __ ; chargebacks __
AFTER: bundle usage/month __ ; outcomes __
Notes: ______________________
Case study #5 (template)
Scenario: ______________________
BEFORE: disputes/month __ ; support hours __ ; chargebacks __
AFTER: bundle usage/month __ ; outcomes __
Notes: ______________________
Reality Audit
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