A redirect spreadsheet can contain a row for every old URL and still be unsafe to implement. Coverage is one check; destinations, conflicting rules and unresolved content decisions need their own checks.

In this eight-URL example, five mappings become candidates for approval and three remain unresolved. Keeping those three visible is part of the deliverable.

1. A chain that hides a content decision

The proposed rules send /about to /team, then to the new /about-us. A direct mapping from /about to /about-us removes the intermediate hop. It does not prove that the final page covers the former content. Record both the structural change and the content owner’s approval separately.

2. Two destinations for one source

The old /contact has competing destinations: /contact-us and /support. The new inventory calls the first page Contact and the second Support. Title agreement suggests a candidate, but the reviewer still needs to compare the content. Do not silently pick whichever row happens to occur last in the file.

3. A target missing from the new inventory

The original rule for /blog/welcome points to a page absent from the supplied new inventory. There is a new /articles/welcome page with the same title. Flag that candidate for review. Absence from an inventory is not proof of an HTTP 404; that requires a request to the actual site.

4. A cycle without an approved destination

/loop-one points to /loop-two, and /loop-two points back to /loop-one. Following the rules never reaches a final page. Hold both URLs for a decision. Replacing a loop with an arbitrary homepage redirect would conceal the unresolved question rather than answer it.

5. Retired content without a successor

The old /retired page has no supplied replacement. Ask whether relevant successor content exists or whether removal is intentional. Keep that decision separate from the list of proposed redirects.

A compact review record

Use columns such as old_url, current_target, proposed_target, finding, evidence, decision_owner, and staging_result. Keep structural findings, content approval and live verification distinct. A spreadsheet match is not a successful HTTP test.

Before implementation, preserve the current configuration. On staging, request every old URL and record the response, each Location hop and the final destination. Check final content and intended indexing settings. Compare actual results with the approved map and restore the saved configuration if unexpected behavior appears. CSV is an interchange format; implementation syntax depends on the platform.

Google’s site-move documentation covers the wider migration process, including URL mapping and monitoring. This walkthrough covers one small review step and makes no ranking guarantee.

Get a sample review

Migration Redirect Review offers a free review of ten supplied redirects. Email fieldworkdesk@hey.com with subject Migration sample and ten public old-page URLs plus proposed destinations. Do not include credentials, customer records or token-bearing links. The full review costs USD 300 for up to 500 old URLs and 500 exact-match rules. Agree scope and delivery date before ordering. Production deployment is outside the service scope.

This worked example uses original synthetic data, not customer records or a testimonial. The approach is conservative: normalize only what an explicit rule permits, preserve the source, and report every unresolved decision. This article and demonstration were created with AI assistance.

Start with a policy, not a delete button

The example contains 16 records with six columns: SKU, product name, USD unit price, stock count, updated date, and category. Before changing anything, its rules distinguish display text from business identifiers.

• Product names and categories may have repeated or surrounding whitespace normalized.
• SKU values remain text and are preserved exactly. The identifiers 0001 and 1 are distinct. Surrounding spaces in a SKU are flagged instead of silently trimmed.
• Prices must match the example's documented USD format. Values with an ambiguous decimal convention or more than two decimal places go to review, without rounding.
• Stock may be normalized only when it is a nonnegative integer.
• Dates must already be valid, unambiguous ISO calendar dates.

These are example rules, not universal business rules. Negative stock might represent backorders in another system. A real cleanup must agree that policy with the data owner.

Separate normalization from interpretation

Here are actual decisions from the demonstration:

A price that can be interpreted is not necessarily a price that should be automatically changed.

Exact duplicate rows and duplicate identifiers are different

Two source records for SKU 0003 are identical across the demonstrated fields. The example removes one and records which source row it matched.

The two records for 0010 have different prices and dates. Both remain. The date alone does not establish whether one supersedes the other, or whether an upstream mapping problem created the conflict.

This distinction is why “remove duplicates” needs a precise key and a conflict policy.

Make the result auditable

The run produces 15 retained records, one documented exact duplicate removal, and eight field changes. Eleven retained records still need review. That is a useful output: uncertainty is visible rather than disguised as a finished import.

The deliverables separate the questions a reviewer needs to answer:

• cleaned.csv contains retained records, source-row references, and review flags.
• changes.csv lists the field, original value, replacement, and rule for each change.
• review.csv lists unresolved issues and their reasons.
• removed.csv accounts for the exact duplicate and its matching source row.
• report.md explains the policy, counts, and remaining limitations.

Reconcile source records before trusting the result: 16 source records = 15 retained + 1 removed. Count field changes separately from changed rows. One record can contain more than one field change.

The source file remains untouched. When importing the output into a spreadsheet, explicitly import identifiers as text: CSV itself cannot force the spreadsheet to preserve their type. This example is not certified import-ready while unresolved records remain.

Inspect the files or scope a cleanup

The free before-and-after sample includes the source, output, change log, review list, removed-row record, and report so you can inspect these decisions yourself.

VisitMargin also offers a separately scoped $49 cleanup for one CSV up to 5,000 rows and 15 columns. The sample page explains the offer and contact route. Scope, permitted changes, and timing are agreed before payment; please start with a redacted sample rather than sensitive records.