Site search issues - are filters the solution or is it even doable when Google often fails with those
I'm struggling with a product search solution for a fairly messy catalog for products with a lot of technical specifications. Expected queries like 30 l box or R 32 compatible or IDs written as 32-3256-67 etc. I've tried the standard keyword search stack. The…
I'm struggling with a product search solution for a fairly messy catalog for products with a lot of technical specifications. Expected queries like 30 l box or R 32 compatible or IDs written as 32-3256-67 etc. I've tried the standard keyword search stack. The results were abysmal. I noticed even Google isn't that reliable for these kinds of queries. One way would surely be to clean up the catalog and introduce proper fields for all that and apply filters. But that's a lot of manual work. Do I have to just live with that?
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I use metafields with a lot of success with the Discovery app for large catalogs.
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I wouldn’t just live with it, but I also wouldn’t start by cleaning the entire catalog manually. For technical catalogs, filters should become the source of truth, but the search layer still needs to understand messy human input. I’d start with the top 20-50 failed searches and normalize only the fields that matter most: capacity, compatibility, model numbers, units, SKU/part IDs, etc. Then map messy queries like “30l”, “30 l”, “30 liter” or “R32 compatible” back to those fields. The mistake is treating filters and search as separate problems. Filters give you structured truth. Search should translate messy queries into that truth.
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