Ecommerce Site Search Checklist: Autocomplete, Typos, Synonyms, Facets

Ecommerce Site Search Checklist: Why Data Quality Beats Algorithm Tweaks

An ecommerce site search checklist should start with a hard truth: most on-site search failures trace back to data and UX gaps, not to shortcomings in the search engine itself. Shoppers who use site search convert at rates roughly 50 percent higher than those who browse without it, and they can account for nearly half of total revenue. Yet research from the Baymard Institute consistently shows that over 60 percent of ecommerce sites still perform below acceptable search standards. The gap between search potential and search reality is almost always a data and execution problem.

This checklist walks through six areas where retail and ecommerce teams can make measurable improvements: autocomplete, typo handling, synonym management, faceted navigation, no-results recovery, and the ongoing operational rhythm that keeps search quality from decaying. If your product data is messy before it reaches the search index, even the best algorithm cannot save the experience. That is why product data quality is the foundation everything else rests on.

Autocomplete: Guide Shoppers Before They Finish Typing

Autocomplete is where most search journeys begin, and it is where the biggest quick wins hide. A well-tuned autocomplete panel can resolve queries in fewer keystrokes, expose product categories a shopper might not have navigated to, and steer ambiguous intent toward high-relevance result sets.

Start by pulling 30 to 90 days of search query logs and clustering them into intent types:

• SKU or model lookups — exact queries like bosch serie 6 dishwasher
• Attribute-driven queries — descriptive terms like waterproof hiking jacket mens
• Problem-solution queries — need-based phrases like chair for lower back pain
• Vague or incomplete queries — broad terms like charger or cable

Then configure autocomplete to accelerate those intents rather than just echoing product names:

1. Blend suggestion types. Show query completions, category suggestions, and individual product tiles in one dropdown. Mixed suggestions reduce reformulations and help shoppers discover relevant categories they might not have browsed.
2. Front-load key attributes. Include brand, compatibility, and size in suggestion labels so the shopper can evaluate relevance before clicking.
3. Keep response latency under 150 ms. Mobile users perceive anything above 200 ms as sluggish. Compress suggestion payloads and cache popular prefixes aggressively.
4. Measure suggestion quality, not just CTR. A suggestion click followed by an immediate bounce means the suggestion promised the wrong result set. Track post-click engagement to distinguish helpful suggestions from misleading ones.

If your product titles are inconsistent — mixing formats, abbreviations, or units — autocomplete quality degrades fast. Standardizing titles with a repeatable template is one of the highest-impact fixes you can make. See our guide on product title templates by category for a framework.

Typo Tolerance and Synonym Rules Without Precision Loss

Typo handling and synonym management sit at opposite ends of a precision spectrum. Under-configured typo tolerance blocks legitimate queries. Over-configured synonyms pollute result sets with irrelevant products. Both leak revenue silently.

Typo tolerance best practices:

• Use progressive edit distance based on query length. A two-character query like tv should allow zero edits. A ten-character query can tolerate one or two character substitutions.
• Require the first one or two characters to match exactly. This prevents wild mismatches on short prefixes and keeps performance manageable on large catalogs.
• Log queries that trigger typo corrections but lead to low conversion, then review them weekly. Some corrections misfire in ways that automated rules cannot catch.

Synonym management best practices:

Separate synonym rules into three categories:

• Bidirectional equivalents: sofa <-> couch, sneakers <-> trainers
• One-way expansions: ps5 -> playstation 5, mbp -> macbook pro
• Contextual merchandising terms: category-specific mappings that only apply within certain product families

Build synonym candidates from real data — zero-result queries, reformulated queries, and abandoned search sessions. Maintain a deny list for dangerous expansions, especially brand name collisions. Version all synonym rule changes so rollbacks are fast. For a deeper dive into taxonomy and search alignment, check our post on product taxonomy for ecommerce SEO and search.

Faceted Navigation That Narrows Without Dead-Ending

Facets should accelerate decision-making, not trap shoppers in empty result states. The most common failure is letting users stack filters that produce zero results and then offering no graceful recovery.

Use this facet design checklist:

• Prioritize high-discrimination facets at the top: brand, price range, size, availability, compatibility.
• Dynamically hide empty values after each filter interaction. Never show a facet value that leads to zero products.
• Display product counts next to each value so shoppers can predict the impact of a click before committing to it.
• Keep active filters visible with one-click removal. Buried active filters create confusion about why the result set is small.
• Order values by shopper behavior, not alphabetically. Sort by popularity, conversion rate, or margin — whichever aligns with your merchandising goals.

Avoid a single global facet template for the entire catalog. Laptops need RAM and screen size. Skincare needs skin type and ingredient filters. Treat facet configuration as a taxonomy exercise, not a UI task. Our guide on faceted navigation best practices covers the technical and UX details in depth.

Test mobile facets separately. Long filter lists inside nested accordions often bury high-intent options below the fold, especially on devices where sticky headers consume significant viewport space.

No-Results Recovery: Turn Dead Ends Into Second Chances

A no-results page is not an error state — it is a recovery opportunity. With 68 percent of shoppers abandoning sites after a poor search experience, a well-designed no-results flow can recapture meaningful revenue.

A strong no-results template includes:

• Corrected or relaxed query suggestions based on fuzzy matching and common misspellings
• Related category links that match the probable intent behind the query
• Trending or popular products in the same product family
• Recently viewed items for returning visitors
• A clear call-to-action fallback — browse top categories, contact support, or use a guided product finder

Diagnose the root cause for every high-volume no-results query:

1. Misspelling not caught by typo tolerance settings
2. Missing synonym for a colloquial term, abbreviation, or regional phrasing
3. Attribute present in free-text descriptions but absent from structured fields — the search index cannot match what it cannot see
4. Filter combination that collapses to zero results

That third point is critical and often overlooked. If a product description mentions "stainless steel" but the material attribute field is empty, a filtered or faceted search for stainless steel will miss it entirely. Structured attribute enrichment solves this at the source. For a dedicated playbook on eliminating dead-end queries, see the no-results playbook.

Semantic Search and AI: The 2026 Layer on Top of the Basics

Even with perfect typo handling and synonyms, keyword-based search has limits. It cannot understand that "shoes for a wedding" and "formal dress shoes" share the same intent. Semantic search — powered by vector embeddings and natural language processing — closes this gap by matching meaning rather than literal terms.

In 2026, the adoption curve for semantic and hybrid search (keyword plus vector retrieval) has accelerated significantly. Modern search platforms now routinely support:

• Intent-aware query rewriting that translates a conversational query into structured search parameters
• Vector similarity matching that surfaces conceptually related products even when no keyword overlap exists
• Multimodal search combining text, image, and voice inputs in a single experience
• Hyper-personalization that adjusts rankings in real time based on browsing context, device, location, and purchase history

However, semantic search still depends on clean, structured product data as input. A vector embedding of a poorly written, attribute-sparse product listing will be just as vague as the listing itself. Tools like Lasso ensure that your catalog data is standardized and enriched before it reaches any search engine — keyword or semantic — so every retrieval method has high-quality inputs to work with.

For more on how product discovery is evolving, see our overview of product discovery trends in 2026.

Monthly Search Tuning Sprint: Keep Quality From Decaying

Search quality degrades without ownership. New products launch with incomplete attributes, synonym needs shift as trends change, and facet configurations drift as the catalog evolves. The most effective operating model is a monthly sprint with clear responsibilities and a repeatable QA protocol.

Suggested sprint workflow:

1. Pull analytics: Top 50 queries, top 20 zero-result queries, top reformulation chains, and search exit rate by category.
2. Audit attribute completeness for the product families behind those top queries. Missing or inconsistent values are the single largest cause of poor relevance.
3. Update autocomplete, synonyms, and facet ordering based on the data.
4. QA test 20 to 30 critical search journeys across desktop and mobile.
5. Deploy and measure — compare two-week before-and-after metrics.

Key metrics to track:

• Search conversion rate (target: 2x or higher versus browse-only sessions)
• Search exit rate (percentage of search sessions ending without any product interaction)
• Zero-results rate (aim below 5 percent)
• Revenue per search session
• Time to first product click

Lasso fits into this rhythm as the data infrastructure layer. Instead of spending sprint time manually fixing product titles, normalizing attribute values, or filling empty fields, Lasso automates enrichment and validation so your team can focus on tuning search logic and merchandising rules. If you are building this process from scratch, explore pricing to see how it fits your catalog size, then book a walkthrough.