Python · QA Tooling

Python Brand Page
QA Tool

~2,000 brand URLs checked across two retail banners. Found 20 genuinely missing pages in Version 1. Refined into a tool accurate enough that its failures are immediately explainable in Version 2.

~2K
URLs audited
20
Missing pages found
356/356
SC passed in v2
v2
Current version
01 — The Problem

Brand pages can disappear quietly. No one was checking.

A brand page that redirects to a search results page — or simply returns a 404 — looks fine in a spreadsheet. No alert fires. No process existed to systematically verify that brand pages across both banners were actually live and resolving correctly.

The risk: customers land on a search page instead of a curated brand experience. SEO signals are lost. And nobody knows until a buyer or writer happens to click on it.

The tool checks every brand URL, follows all redirects, and flags anything that does not land on the expected brand page destination.

02 — Watch It Run

The script auditing live brand URLs

The console below shows the tool running against the brand list, following redirects, and flagging pages that do not resolve to the correct destination. Status prints to the terminal in real time.

Terminal running the brand URL QA audit
Terminal — Brand page QA audit running live
03 — Output

Results exported and ready for action

The tool writes its findings to a structured output file: confirmed pages, flagged redirects, and missing pages — separated so each finding is immediately actionable without manual sorting.

Structured QA results grouped by status
Output — Structured results by status
04 — Two Versions

Version 1 found the problem. Version 2 proved accuracy.

Version 1 — May 2025
  • 1,008 SC brands checked — 7 missing
  • 1,005 SS brands checked — 13 missing
  • 20 genuinely missing pages found and reported
  • Initial redirect logic — some edge cases flagged incorrectly
Version 2 — April 2026
  • SC: 356 brands checked — 356 passed, 0 missing
  • SS: 18 flags — all confirmed false positives
  • Edge cases (Frye & Co., B-52) now explained immediately
  • Refined redirect detection — passes clean lists cleanly
05 — Results

By the numbers

1,008
SC brands (v1)
7 missing found
1,005
SS brands (v1)
13 missing found
356/356
SC passed (v2)
Zero missing
18
SS flags (v2)
All false positives — explained
06 — Sample Output

What the terminal looks like mid-run

[PASS] shoecarnival.com/nike → 200 OK — brand page confirmed [PASS] shoecarnival.com/adidas → 200 OK — brand page confirmed shoestation.com/frye-and-co → 302 → search results — reviewing [PASS] shoecarnival.com/birkenstock → 200 OK — brand page confirmed [MISS] shoestation.com/brand-x → 404 — page not found, adding to report --- [DONE] SC: 356 checked — 356 passed — 0 missing SS: 1,005 checked — 987 passed — 18 flagged for review

Version 1 found a real problem. Version 2 proved the tool was accurate enough that its own failures were explainable. That is the difference between a script that runs once and a tool that improves.

07 — What's Next

Planned improvements

Special-character brand handling — detect and normalize names like Frye & Co. and B-52 before checking
Scheduled runs — automated weekly audit with results emailed on completion
Delta tracking — surface only new failures since the last run, not the full list each time
Cross-reference against the brand master list to flag brands in the system with no corresponding page
08 — Stack

What it's built on

Python 3 requests Redirect Following CSV / Excel Output Shoe Carnival Brand List Shoe Station Brand List GitHub
Python·QA Tooling·URL Auditing·Redirect Detection·eCommerce Ops·Shoe Carnival·Shoe Station·Automation·Brand Health·Link Checking· Python·QA Tooling·URL Auditing·Redirect Detection·eCommerce Ops·Shoe Carnival·Shoe Station·Automation·Brand Health·Link Checking·