Most fashion retailers evaluate search performance by looking at conversion metrics, bounce rates, and click-through data. They audit the search engine itself — testing ranking algorithms, measuring response times, reviewing synonym lists.

That approach misses the actual problem.

Before you evaluate your search technology, you need to evaluate what you are asking it to work with. Because if your catalog data cannot answer the questions shoppers are actually asking, no amount of search optimization will fix the underlying issue.

The fastest way to waste a search investment is to point
sophisticated technology at catalog data that does not
reflect how shoppers think.

Here is the diagnostic audit. Five questions. Twenty minutes. The answers will tell you exactly where to invest next.

Question 1: Pull Your Top 50 ‘No Results’ Queries — What Pattern Do You See?

Every search platform tracks queries that return zero results. Most retailers never look at the list. Pull yours now. Export the top 50 queries from the past 30 days that returned no matches.

What you are looking for:

  • Are shoppers searching by brand names you do not carry? (This is a merchandising signal, not a search problem.)
  • Are they searching by price ranges? (“Jeans under 50,” “affordable leather bags”)
  • Are they using fit descriptors your catalog does not tag? (“High-waisted midi skirt,” “boyfriend fit blazer”)
  • Are they searching by vibe or aesthetic? (“Grunge style boots,” “minimalist jewelry,” “coastal grandmother cardigan”)

Now ask: do these products exist in your inventory?

What this tells you:

  • If yes → Your catalog has the products but not the language. Shoppers and your data are speaking two different vocabularies.
  • If no → Your merchandising team now has concrete evidence of demand gaps.
  • If the queries are mostly misspellings or brand names → Your search layer may actually be the issue.

Industry benchmark: Algolia reports that zero-result rates above 3-5% indicate a data or taxonomy problem, not a search engine limitation. If 12-21% of your searches return no results — the industry average — you have a catalog intelligence gap.

Question 2: Can Your Filters Handle Multi-Attribute Queries?

Go to your site. Try to filter products using combinations of attributes that shoppers actually care about:

  • Show me: cropped length + linen fabric + summer weight
  • Show me: platform sole + neutral tones + walkable heel height
  • Show me: relaxed fit + workwear appropriate + machine washable

Most sites cannot do this. You can filter by category, then maybe fabric, then maybe length — but only if those exact attributes exist as separate, structured fields. And even then, the combinations rarely work as expected.

What this tells you:

  • Filters fail on combinations → Your attributes are too shallow or inconsistently applied. Each product needs richer, multi-layered tagging.
  • Filters work but return strange groupings → Your attribute values are inconsistent. “Linen blend” and “linen mix” are treated as different materials.
  • Filters work smoothly → Your catalog has structured depth. The problem may be in query interpretation or ranking.

Research from Nosto found that 56% of fashion retailers cannot dynamically filter results based on product-specific attributes (like “the cut of jeans”). Yet 30% of consumers cite this as a top frustration. If your filters cannot handle nuanced combinations, your data structure is the blocker — not your search UX.

Question 3: Search for Your Best-Sellers Using Customer Language — Do They Appear?

Take your top 20 best-selling products from last quarter. For each one, write down how a customer would describe it if they saw it on Instagram but did not know your brand’s product name.

Examples:

  • Your product: “Heritage Denim Trucker Jacket”
  • Customer searches: “cropped jean jacket,” “vintage style denim,” “90s trucker”
  • Your product: “Cashmere Blend Relaxed Pullover”
  • Customer searches: “oversized soft sweater,” “cozy knit,” “effortless layering piece”

Now search your own site using the customer language. How many of your best-sellers appear in the top 10 results?

What this tells you:

  • 0-30% appear → Your product titles and descriptions are written for internal teams, not shoppers. The vocabulary mismatch is severe.
  • 30-60% appear → Partial discoverability. Some products are tagged with shopper language; most are not. Inconsistent enrichment.
  • 60%+ appear → Strong alignment between catalog language and customer language. If search still underperforms, look at ranking or merchandising rules.

Question 4: Audit 100 Random SKUs for Attribute Completeness

Pull 100 products at random from your catalog. For each one, check whether the following attributes are populated:

  • Fit/silhouette (not just size)
  • Color family (not just hex codes or brand-specific names)
  • Fabric weight or seasonality
  • Occasion or use case
  • Style or aesthetic descriptor


Count how many products have all five attribute types filled in.


What this tells you:

  • <20% complete → Your catalog is structured for inventory management, not discovery. Massive enrichment gap.
  • 20-50% complete → Partial coverage, likely driven by manual tagging or vendor-supplied data. New SKUs degrade quality over time.
  • 50-80% complete → Strong foundational data, but gaps remain in contextual attributes (occasion, aesthetic, mood).
  • >80% complete → Enterprise-grade catalog intelligence. If search still fails, the issue is likely in query understanding or personalization.


Lily AI’s research shows that rich, multi-layered product attribution can reduce zero-result searches by 60-85% and lift search-driven revenue by 10-25%. But those gains require structured, consistent attribute coverage — not selective tagging of hero products.

Question 5: How Many Shoppers Refine Their Search After the First Try?

Check your analytics. For users who perform a search, what percentage submit a second query within the same session?

Industry benchmark from Algolia: 20% of searchers refine their queries. If your number is significantly higher, it means the first set of results did not match intent.

What high refinement rates tell you:

  • Industry benchmark from Algolia: 20% of searchers refine their queries. If your number is significantly higher, it means the first set of results did not match intent.
  • Shoppers saw no results and tried again → Zero-result rate is too high. Data coverage problem.
  • Shoppers rephrased the same query → Your search does not understand synonyms or natural language variations. Query interpretation issue.


Now layer this with exit rate after search. Luigi’s Box reports that 30-42% of users exit after a failed search. If your refinement rate is high and your post-search exit rate is also high, shoppers are trying to make your search work — and giving up when it does not.

If shoppers are refining searches repeatedly and still leaving, they are telling you the catalog cannot answer their questions — no matter how they phrase them.

What These Answers Mean for Your Next Investment

If your audit revealed gaps in three or more of these areas, you do not have a search technology problem. You have a catalog intelligence problem.

The investment that will move your metrics is not in the search layer. It is upstream:

  • Structured, fashion-native attributes that reflect how shoppers think
  • Consistent attribute application across 100% of your catalog, not just hero SKUs
  • Semantic depth — occasion, mood, aesthetic, trend — not just physical descriptors
  • Language alignment between brand vocabulary and customer vocabulary
  • Intelligence that scales as your catalog grows, without manual re-tagging

The retailers solving discovery at scale are not replacing search engines every two years. They are building the catalog intelligence that makes any search engine work.

What These Answers Mean for Your Next Investment

You can keep investing in better search technology and watch it underperform on weak data. Or you can fix the catalog intelligence layer that every search system depends on — and unlock the discovery, conversion, and revenue your products deserve.

The question is not whether your search vendor is good enough. The question is whether your catalog data is ready for the search experience your shoppers expect.

Your catalog. Our intelligence.
Better discovery from day one.

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