Evidence library

Case Studies

This collection separates public evidence from illustrative examples and methods. Unsupported figures are not promoted as client results.

Evidence policy

A credible case study lets the reader inspect how the result was established.

A client name or a percentage does not supply the missing context. A publishable case needs an authorized identity or an explicit anonymity notice, a baseline, a measurement period, the data source, the work performed, and the limits of the evidence.

This hub applies that standard to every item it links. Where the public record is incomplete, the label says so.

What the labels mean

  • Public evidence: a reader can inspect the source, scope, dates, and reported outcome.
  • Modelled example: the page presents an operating scenario without establishing a real client engagement or verified result.
  • Illustrative method: the page explains a workflow or audit approach without presenting it as verified client proof.

Level 1 | Public evidence

No case is promoted in this level today.

Growing Search publishes performance claims on its technical SEO page, but that public page does not provide the baseline dates, client-level attribution, analytics records, or role allocation needed to verify a complete case. Those figures have therefore been removed from this hub.

Evidence required for this level

The minimum record includes the client or an authorized public identifier, the reporting window, the source platform, a before-and-after comparison, Francisco's or Growing Search's contribution, and any material limitation.

First-party source reviewed

The agency page remains useful as a record of what Growing Search publishes. Its summary numbers are public claims rather than a complete, independently inspectable case.

Review the published source and its limits

Level 2 | Modelled examples

The workflow may be useful, but the story is not client proof.

The three destination pages use case-study framing and contain outcome figures. This review found no evidence that they describe work performed for a real client, and their figures are not verified. They are linked only as illustrative process scenarios.

Illustrative scenario

Manual action recovery

A link review, remediation plan, reconsideration process, and recovery monitoring sequence.

Review the illustrative workflow

Illustrative scenario

Site migration planning

A migration workflow built around URL mapping, launch controls, and post-launch review.

Review the illustrative workflow

Illustrative scenario

Large-site crawl and indexation

A technical sequence covering crawl diagnostics, index controls, and internal discovery.

Review the illustrative workflow

Level 3 | Illustrative methods

These pages are method references, not client proof.

Each page can help frame an audit, an implementation plan, or a measurement question. The destination pages also contain outcome figures that this review could not verify. Use the method, not those numbers, as the reference.

Implementation method

Structured data review

A reference for eligibility checks, markup coverage, validation, and Search Console monitoring.

Review the method

Measurement method

AI Overview visibility

A reference for defining a query set, tracking citations, and separating visibility from attributed traffic.

Review the method

Experiment method

Conversion research and testing

A reference for building hypotheses, recording test conditions, and distinguishing observed lift from a general promise.

Review the method

Industry research

Industry guides answer a different question.

Industry pages explain search demand, technical constraints, and common operating risks. They are research and planning resources, so they are no longer presented as case studies in this collection.

Browse industry guides

Evidence review

Bring the source records into the first conversation.

A useful consultation can start with Search Console exports, analytics annotations, migration records, or a list of decisions that need evidence. The goal is to define what can be measured before assigning an outcome.