Semrush or Ahrefs for E-commerce Category Page SEO

Start by pulling a full crawl of your site and counting URLs by pattern. If filtered pages like color and size combinations outnumber real category and product pages by several times over, your crawl budget is going to filter combinations instead of the pages you want ranked. Ahrefs' Site Audit crawls at the scale most stores need and groups pages by URL pattern, so this count takes minutes instead of a manual export.

Once you have the count, decide which filter combinations deserve to be indexed (a genuinely popular one, like "red running shoes", might) and noindex or canonicalize the rest. This single change often does more for organic visibility than a quarter of new blog content, and it costs nothing beyond the engineering time to implement it, which makes it worth doing before any research-tool subscription.

Follow these steps to audit filter URLs:

1. Pull a full crawl of your site and count the URLs by pattern, using a site audit tool that groups pages by URL structure.
2. Compare filtered pages, such as color and size combinations, against your real category and product pages.
3. If filtered pages outnumber real pages several times over, recognize that crawl budget is going to filter combinations instead of ranked pages.
4. Decide which filter combinations deserve to be indexed, keeping only genuinely popular ones like a common color and product pairing.
5. Noindex or canonicalize the remaining filter combinations, and do this before paying for any new research tool.

Category pages carry the real ecommerce revenue, but they're also the pages most likely to get buried under filter and search-result URLs if your rank tracking isn't scoped correctly. Set up tracking that separates category-page rankings from product-page rankings from blog rankings, since each moves differently, and a drop in one shouldn't get lost in an average across all three.

Both tools track rankings at scale, but Ahrefs' rank tracker ties more directly into its crawl data, so you can see in one place whether a ranking drop coincides with a crawl or indexing issue on that page.

A store running Google Shopping campaigns needs to know what competitors are bidding on and what their product titles and pricing look like, and Semrush's PLA research is built for exactly that: it shows which queries trigger a competitor's shopping ads and what their listings say. That's useful even for the organic side, since a query a competitor is bidding heavily on is usually a query worth an organic collection page too.

Ahrefs doesn't compete here. If your team runs Shopping and organic out of the same login, that's a real point in Semrush's favor, independent of either tool's core keyword research. A store that treats the two channels as entirely separate disciplines often misses this: the same competitor research that informs a Shopping bid strategy should also inform which category pages get written next, and a combined workspace makes that connection obvious instead of requiring a manual cross-check between two logins.

Migrating platforms, whether from a legacy cart to Shopify Plus or between two versions of the same platform, breaks more redirects than teams expect, especially for wholesale, store-locator or discontinued-collection pages that don't get the same QA attention as the main catalog. Ahrefs' backlink and broken-link reports can surface pages that used to earn links and traffic but now return a 404 or redirect to the homepage, which is a useful way to recover lost equity after a migration.

Run this check within a week of any platform change, not months later once the damage compounds.

Retail inventory costs move with broader consumer prices¹. When those costs climb, the margin case for organic traffic over paid traffic gets stronger, since organic clicks don't carry an incremental cost per acquisition the way Shopping ads do. That's a reason to prioritize fixing crawl waste and reclaiming lost pages before adding a new tool, since both are essentially free traffic recovery.

The median landing page converts around 6.6% of visitors². A category page pulling meaningfully below that is worth auditing for load speed, filter clutter or a mismatched value proposition before you assume the keyword itself is weak.

Picture a mid-size apparel-adjacent store with 400 real products but 6,000 indexable URLs once you count every color, size and sort-order combination. A crawl audit shows Googlebot spending most of its visits on those parameter pages instead of the 60 category pages that actually carry revenue. The fix runs in three steps: canonicalize every filter combination back to its base category page, noindex any combination that gets no independent search demand of its own, and resubmit an updated sitemap that lists only the pages meant to rank.

Stores that run this fix typically see indexed-page count drop sharply within a few weeks while their real category pages start climbing, since the crawler finally has room to revisit them regularly instead of getting lost in filter combinations that were never going to rank for anything.