Most ecommerce founders have a search bar hiding in plain sight, yet the default experience silently leaks revenue. Shoppers who search are the highest intent users on your site, and when you help them find the right product quickly, conversion jumps. According to Algolia’s 2024 roundup, consumers who use site search are 2.4 times more likely to buy and spend 2.6 times more than non searchers, and up to 30 percent of visitors use site search at all on many stores. The same roundup cites Forrester indicating 43 percent of retail site users go straight to the search bar and highlights that searches that fail drive abandonment. See the compiled data in Algolia’s 2024 e-commerce search statistics.

The opportunity is large because the UX gap is still large. In a 2024 analysis, the Baymard Institute reports 41 percent of sites fail to fully support the eight most common ecommerce search query types. Baymard also finds 34 percent of users look for non product content using site search and 15 percent of sites do not support basic non product queries like return policy, which their 2025 update details in this article. This is why a systematic approach to query understanding, synonyms, merchandising rules, and analytics is one of the fastest ways to lift conversion without adding new ad spend.

What great on-site search must understand

Shoppers do not type neat catalog labels, they describe needs in their own words. Baymard identifies eight common query types that ecommerce search must handle: Exact, Product Type, Feature, Use case, Abbreviation and Symbol, Compatibility, Symptom, and Non product. Their 2024 benchmark shows many sites still fail basic cases like SKU search or use case phrases, see the breakdown in Baymard’s query types research.

Here is the practical takeaway for your search pipeline on Shopify or headless stacks:

• Exact and SKU: ensure product titles, model numbers and SKUs return clean, precise results before anything else. Do not bury the exact match under broader results.

• Product type: queries like women jeans should return the category and prefilter relevant attributes. The Baymard article shows that many sites either do not recognize the category or dump an unfiltered wall of products.

• Features and attributes: color, material, size, brand and price terms should translate into applied filters. Macy’s behavior for blue shirt, highlighted by Baymard, is a useful model.

• Use cases and symptoms: users often type wedding guest dress or sore throat. Build tag dictionaries or rules that map these intents to product sets and guides, then expose transparent filters so shoppers can adjust.

• Non product content: support help pages, returns, order tracking, and store policies in the same box. Baymard’s 2025 update shows 34 percent of users try this pattern and 15 percent of sites still fail to return helpful results, which is a needless reason to lose trust. See their data and examples in this guide.

On Shopify, you do not have to build all of this from scratch. As Shopify explains in its Search and Discovery product page, native features include semantic search, predictive search, typo tolerance, custom filters using Shopify taxonomy, and analytics. In the help docs, Shopify details how semantic understanding expands results using related words and image signals, how to boost products for specific queries, and how to create synonym groups to normalize shopper language.

If you are considering Shopify, you can start a trial and get these capabilities included by default at Shopify.

Synonyms are not optional

Synonyms are the difference between blow dryer and hair dryer returning the same set of relevant SKUs. They also normalize slang, pluralization, hyphenation, and regional vocabulary. Shopify’s synonyms guide outlines best practices and limits, including up to 20 terms per group and a 1,000 term store cap. Shopify treats phrase synonyms as exact order matches, which matters for two word terms like belt bag.

In headless stacks, your search engine’s synonym model determines both flexibility and maintenance workload. Elastic’s documentation explains equivalent vs explicit mappings and how multi word synonyms require the synonym graph filter to avoid token order issues. Typesense supports both one way and multi way synonyms and notes that synonyms do not apply inside phrase queries or filters, see Typesense’s synonym API. The implementation rule of thumb is simple: prefer search time synonyms to avoid reindexing, keep a canonical term for analytics, and review zero result logs weekly to add new real world language.

Two pragmatic tips that consistently produce conversion lift:

• Use your zero party data to seed synonyms. If your quiz tells you customers say sling instead of crossbody, add it. Our playbooks on segmentation can help you capture this language, see StoreAcquire’s zero party data guide and our resource on high ROI email and SMS journeys that leverage this data, here.

• Normalize abbreviations and symbols. Baymard’s research shows 50 percent of sites fail basic unit mapping, which is a preventable failure. Map inch, in, " and ft, foot, ' in your synonym sets and test common numeric ranges.

Merchandising rules that respect relevance

Relevance gets shoppers to the right shelf. Merchandising rules win the final click. The mistake teams make is global boosting that breaks intent. Rules should be scoped, testable, and reversible.

On Shopify, use product boosts for specific terms, not wide glossaries. Shopify’s boost best practices recommend limiting boosted products per query and relying on built in typo tolerance so you do not maintain misspellings. You can also control out of stock positioning for both search results and predictive search, which prevents dead ends by sending unavailable items to the bottom, see the settings.

In headless architectures, query rules give you surgical control. Algolia’s rules let you pin or hide items, boost attributes, filter results for specific contexts, add banners, and time bound promotions as explained in their rules overview. Use cases that consistently increase revenue include:

• Seasonal boosts: lift seasonal attributes when the query suggests gifting, for example boost gift set facets in November queries for candles.

• Profit protection: push higher margin variants for non exact queries. Align this with your AOV and margin strategy, see our guide to ecommerce pricing that protects margin and our case work on bundling frameworks for add on rules.

• Context rules: on mobile, prefer lighter or quick ship results where bounce risk is higher. Algolia’s contexts enable device based rules as shown in the docs.

Finally, never leave a user at a dead end when search fails. Baymard’s 2025 advice on “no results” design is clear: suggest related categories, alternate searches, personalized recommendations, support contact, and popular items to keep users in flow. Their article with examples is here: 5 strategies for no results pages.

Filters and facets that buyers actually use

Filters are not checkboxes, they are an extension of intent. Shopify’s Search and Discovery makes faceted filters easier by using the platform taxonomy and even offers visual filters for logos or swatches, see the product page. In practice you want:

• Category specific filters: different attributes for dresses vs. cookware, automatically applied when the system recognizes a Product Type query.

• Applied filter transparency: show the user what your system detected. Baymard highlights Macy’s setting a color filter for blue shirt as a strong pattern in their query types analysis.

• Inventory aware facets: hide values that would result in zero hits or show counts that reflect stock.

These reduce pogo sticking, which pairs well with checkout polish. If you need a structured list of high impact checkout experiments after you fix search, our checklist lives here: 27 checkout tests for 2025.

Analytics that create a weekly search improvement loop

Search performance compounds when you measure the right things. Shopify surfaces key behavioral reports inside the Search and Discovery app, including searches by query, searches with no results, searches with no clicks, search click rate, and purchase rate. The help center explains where to access these and how to act on them with boosts and synonyms.

Augment platform analytics with GA4 so your growth team can segment performance by channel, device, and campaign. Google’s enhanced measurement automatically logs the view_search_results event when it detects common query parameters like q, s, search, query, or keyword, and it populates the Search term dimension. You can confirm the behavior in Google’s documentation, see GA4 enhanced measurement events. Pair this with custom events for search result clicks and add to cart from search to build a funnel view from query to purchase.

A simple weekly loop that works:

• Review Searches with no results and add synonyms or new products to the index.

• Audit Searches with no clicks and inspect the first two rows for relevance. Add a query rule or adjust ranking.

• Track Purchase rate for search versus site average. Algolia’s data shows search conversion can be 1.8 to 2 times site average and sometimes up to 50 percent higher than average sessions. Treat a flat line as a signal.

If you are investing in headless search, set up separate indices and dashboards for top categories so merchandisers can run these loops without engineering. Algolia’s dashboard makes this non technical via their visual editor, and Elastic and Typesense can be wired to internal tools if you need full control.

Shopify setup that gets you to strong search fast

Shopify’s built in features cover most needs for DTC brands, and the Search and Discovery app adds the controls operations teams want. Start by turning on semantic search if you meet the plan and product count requirements, which Shopify outlines in its semantic understanding guide. Then:

• Define synonym groups for your top 200 queries. Use Shopify’s limits and phrase behavior as a guide, see the synonyms docs.

• Add product boosts for only your highest leverage queries. Keep boosts to a handful of SKUs per term and rely on your base ranking for the rest.

• Configure filters from Shopify taxonomy and add visual filters for speed. The product page and filter docs show how.

• Wire up analytics and set a weekly review ceremony. Shopify’s analytics page for Search and Discovery lists the exact reports to track.

All of this rides on a well structured catalog. Your attributes, tags, and product type hierarchy are the raw material that semantic search, synonyms, filters, and rules use to do their jobs.

Headless search, when you need it

If you run a marketplace, have complex compatibility logic, or require advanced merchandising at scale, headless engines like Algolia, Elastic, and Typesense give you the control you need.

• Algolia: best in class speed and low code merchandising. Their rules system supports pinning, burying, boosting, banners, and timed promotions, as detailed in Rules overview. Use replicas to test ranking strategies and make visual merchandising accessible to non technical teams.

• Elastic: deep configurability with synonym graph filters, analyzers, and query profiles for complex ecommerce logic. The official docs explain search with synonyms and the trade offs between index time and search time synonyms.

• Typesense: open source, easy to host and maintain. Offers one way and multi way synonyms with simple APIs, documented here: Typesense synonyms.

Instrument these engines with GA4 and your data warehouse to model attribution for search and merchandising changes alongside ad channels. If you are building a broader measurement framework, our 2025 attribution guide covers GA4, MMM, and first party data blending, see this piece.

A simple 14 day action plan

• Days 1 to 3: catalog audit, define priority query list, enable semantic search if on Shopify and eligible, wire GA4 search events.

• Days 4 to 7: ship top 50 synonym groups, set category filters, fix top 10 “no results” terms, implement better no results page behaviors following Baymard’s guidance.

• Days 8 to 10: add 10 high intent boosts, configure out of stock ordering, add visual filters where useful, and promote high margin bundles for matching queries using your pricing and bundling strategy insights from StoreAcquire’s playbooks.

• Days 11 to 14: build the weekly analytics loop, hand the dashboard to merch and CX, test two query rules or boosts each week, and document wins in a shared log.

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