Franchise Review Reply Approval Workflow Case Study

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What this case study proves today

This proof is limited to workflow structure: drafting, approval, negative-review routing, and status visibility. It does not claim ranking lift, review-score movement, or location-level performance.

What the workflow needs to prove

A franchise operator needs local replies to move quickly while keeping sensitive reviews in human approval. The CMS page documents the workflow without publishing customer details.

Where Payload fits

Payload owns the editorial draft and approval surface. Static export keeps the public marketing build deterministic and reviewable through git.

Before and after workflow

Area	Before	After
Reply drafting	Owners or agencies wrote each response from scratch.	AI drafts start from brand voice, rating, and safety rules before review.
Low-star reviews	Complaints could be delayed or answered without a consistent approval rule.	Low-star and sensitive reviews stay in approval before public posting.
Public proof	The operating model was hard to explain without private examples.	The case study explains the proof boundary and links to public research assets.

Operating model

Local voice

Each location can keep replies warm and specific without making the brand sound like a corporate template.

Central control

Head office can define which review types need approval and which positive reviews can move faster.

Deterministic publish

Published CMS entries export to JSON, then the static generator writes the public page.

Proof assets

Asset	What it proves	Boundary
Review reply benchmark	The reply safety rules are backed by a documented synthetic scenario set.	Synthetic scenarios are workflow evidence, not customer outcome data.
AI search prompt tracker	The site has a repeatable way to measure AI-search mentions and citations.	Tracker slots must be filled from live audits before claiming movement.

What is intentionally not claimed

No claim that rankings improved because of the workflow.
No claim that review score changed because of the workflow.
No private Google review text, reviewer names, or franchisee notes are published.

Next proof to collect

Metric	Why it matters	Publish rule
Median time to first draft	Shows operational speed without claiming search ranking movement.	Publish only aggregate ranges after review.
Low-star approval rate	Shows whether sensitive reviews are being routed through human approval.	Group by rating band, not reviewer detail.

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