Smart search for an e-commerce store is catalog search that understands the meaning of a query, not just word matches: it fixes typos, takes synonyms and keyboard layout into account, and finds products by use case (“jacket for cold weather”), compatibility (“filter for this model”), and problem (“descaling solution”). Technically, it is usually a hybrid: classic full-text search for exact queries and SKUs plus vector search based on neural text representations for semantic queries. It is worth implementing when shoppers search for products using different words, and a significant share of queries end in no results or irrelevant products.
This article was prepared by AIrassvet based on open sources verified on October 2, 2026. It is intended for owners and executives of e-commerce stores, e-commerce directors, and catalog product managers, including B2B stores with large assortments. The products and companies mentioned are examples; AIrassvet is not affiliated with them, and the section at the end describes the publisher’s services.
Contents
- In brief: standard search vs. smart search
- How much a store loses because of poor search
- Which queries standard search does not understand
- How smart search works: BM25, vector, and hybrid
- Data matters more than the model
- Off-the-shelf service or custom search
- How to implement smart search: step-by-step plan
- Which metrics to track
- Frequently asked questions
- Source materials and verification method
In brief: standard search vs. smart search
| Capability | Standard keyword match search | AI-powered smart search |
|---|---|---|
| Exact product name and SKU | Works if the index is configured properly | Works; hybrid search preserves exact matches |
| Typos and wrong keyboard layout | Often no results | Corrects and surfaces products |
| Synonyms and colloquial names | Only with a manually built dictionary | Understands semantically similar phrasing |
| Query by use case and situation | Does not understand | Finds products by meaning |
| Problem-based query | Does not understand | Connects the problem with products that solve it |
| Compatibility | Only with perfectly filled-out attributes | Better, but still depends on attribute quality |
| No results | A dead end for the shopper | Suggests close alternatives |
How much a store loses because of poor search
Two sets of data show the scale of the problem.
Customer survey. In a study commissioned by Google Cloud from The Harris Poll and published on March 23, 2023, nearly 13,500 adults in 14 countries who used store website search were surveyed. Only one in ten U.S. shoppers said they always found exactly what they were looking for through search (12%) or good alternatives (11%). Seventy-six percent of American shoppers had experienced a store site search failing to find the needed product — on average four times during the survey month. Google Cloud estimates that retailers lose more than $2 trillion a year globally from “abandoned search” (Google Cloud Blog). These are the study sponsor’s figures; the sponsor also sells search solutions, and the data relate to overseas markets, so treat them as a rough sense of scale rather than as an estimate for your store.
Website testing. In an updated study published on April 29, 2026, Baymard Institute, which tests e-commerce usability, says that 56% of sites do not adequately support users’ search needs; the benchmark covers more than 170 websites and apps (Baymard Institute).
The easiest way to estimate your own scale of the problem is through your analytics: the share of search sessions with no results and the share of searches after which the shopper left without clicking a product.
Which queries standard search does not understand
Baymard identifies eight types of search queries and, for each one, reports the share of tested sites that have problems with it (Baymard Institute):
| Query type | Example | Share of sites with problems |
|---|---|---|
| Exact match (name, model) | “Bosch GSR 120-LI” | 12% |
| Product type | “cordless drill” | 20% |
| By feature | “brushless 18V drill” | 39% |
| By use case | “tool for assembling furniture” | 43% |
| Abbreviations and symbols | “18V”, “1/2 inch” | 54% |
| Compatibility | “battery for GSR 120” | 44% |
| Problem-based | “what to use to remove rust” | 37% |
| Not about the product | “shipping,” “returns,” “warranty” | 66% |
The query examples are ours, in Russian; the shares come from Baymard research. It’s clear that most sites handle exact-match queries, while problems start when shoppers describe what they need in their own words. Semantic search is what covers those queries.
How smart search works: BM25, vector search, and hybrid search
In Habr’s December 18, 2025 review of e-commerce search technologies, three architectures are highlighted (Habr):
- full-text search with BM25 ranking (Lucene, Solr, Elasticsearch) works well when the query words and product description match, and it supports filters by price, brand, and category, but it does not capture the meaning of complex wording;
- vector search represents the query and the product as vectors—neural text representations, where close vectors mean similar meanings. It finds products by meaning even when the words do not match;
- hybrid search runs both searches in parallel, then combines and reranks the results.
Why hybrid is needed in almost every case. Vector search understands meaning well, but it can lose to classic search on exact queries: SKUs, model numbers, rare terms. Full-text search, on the other hand, is accurate on exact matches but weak on intent-based queries. Hybrid search takes the strengths of both.
On top of search, you can add two more layers:
- query understanding by a language model: the model extracts filters from the query (“under 5,000 rubles,” “for a 7-year-old”) and turns them into search parameters;
- conversational assistant: the shopper describes the need, the assistant asks clarifying questions, and then suggests products. More on this in the article AI chatbot for a website.
Data matters more than the model
The most common reason search performs poorly is not the algorithm, but the catalog. Smart search will not find “a battery compatible with model X” if compatibility is not recorded anywhere. Before implementation, check:
- attribute completeness: sizes, materials, compatibility, and intended use should be in structured fields, not only in free-form descriptions;
- consistent naming: the same product should not have different names across different product pages;
- synonym and abbreviation dictionary: colloquial and professional names, units of measure, brand transliteration;
- answers to non-product queries: shipping, payment, returns, and warranty should be findable through search, not lead to a dead end.
Language models help here too: based on descriptions and specs, they can fill in missing attributes and normalize names, while a staff member checks the results. Read more in the articles Product pages with AI and Catalog matching with AI.
Off-the-shelf service or custom search
| Option | When it fits | Limitations |
|---|---|---|
| Ready-made SaaS smart search service | A standard online store, fast launch needed, no in-house search team | Vendor-controlled ranking logic, monthly fee, limited customization for your specific needs |
| Custom hybrid search | Large or complex catalog, B2B with SKUs and compatibility rules, special ranking requirements | Requires development, infrastructure, and ongoing support |
| Improving the current search | The main issues are the synonym dictionary, typos, and attributes | Won’t solve intent-based queries |
There are ready-made smart search services on the Russian market: for example, Retail Rocket describes synonym handling, typo tolerance, keyboard layout handling, and personalized results in its search (Retail Rocket). Compare services using your own data: export real search queries and check what each option returns for them.
How to implement smart search: step-by-step plan
- Export search queries for several months from your site analytics: frequency, share of zero-result searches, clicks on results, and add-to-cart events.
- Label the problematic queries by the types in the table above: where results are empty, where products are irrelevant, where the shopper rephrases the query.
- Build a benchmark set of several hundred queries with the correct products for each one. This is the basis for a fair comparison of options.
- Clean up the catalog: attributes, names, synonyms, answers to non-product queries.
- Compare the options on the benchmark set: current search, ready-made service, custom hybrid. Count how many queries put the right product in the top positions.
- Run an A/B test on a portion of traffic and compare business metrics against the control group.
- Set up ongoing monitoring: a weekly report on zero-result searches and new shopper phrasing.
Which metrics to track
| Metric | What it shows |
|---|---|
| Zero-result search rate | How many queries end in a dead end |
| Share of searches with a product click | Whether the shopper finds something relevant |
| First-click position | How high the needed product appears in the results |
| Share of Reformulated Searches | How many times a shopper tries to say the same thing another way |
| Conversion of Search Sessions to Order | Total Contribution of Search to Sales |
| Revenue per Search Session | Money, Not Just Clicks |
A Habr review emphasizes that search metrics should be tied to sales and revenue, not just technical relevance metrics (Habr). That is why the final decision should be made based on an A/B test, not a comparison against a benchmark set.
Search is closely connected to recommendations and personalization: search builds the product set, and recommendations tailor it to the shopper. For more, see the articles Recommendation System for an Online Store and Website Personalization with AI.
Frequently Asked Questions
What is smart search for an online store?
It is catalog search that understands the meaning of the query, not just word matching: it corrects typos, takes synonyms into account, finds products by purpose, problem, and compatibility, and suggests alternatives instead of returning no results. It is usually built as a hybrid of full-text and vector search.
How is semantic search different from regular search?
Regular search looks for a match between the words in the query and the product description. Semantic, or vector, search compares meaning: the query and products are represented as neural network vectors, and phrases with similar meaning find each other even if the words are different.
Why can’t you just replace regular search with vector search?
Vector search may be worse at handling exact queries: SKUs, model numbers, and rare terms. That is why, in practice, a hybrid approach is used, which runs full-text and vector search in parallel and combines the results.
When does an online store need smart search?
When the catalog is large, shoppers describe products in different words, and a noticeable share of search queries end with no results or with users leaving without clicking a product. Start by exporting search queries from analytics to measure the problem.
Will smart search help if the catalog is poorly filled out?
Partially. It will fix typos and synonyms, but it will not find compatibility or purpose if that data is not recorded anywhere. Filling in attributes and normalizing names is a required part of implementation; language models can also help with that work.
How do you measure the impact of smart search?
Compare the share of no-result searches, the share of searches with a product click, the conversion of search sessions to order, and revenue per search session in an A/B test with a control group that sees the old search.
Primary Sources and Verification Method
Verified on October 2, 2026:
- Google Cloud Blog: “New research: Search abandonment continues to vex retailers worldwide”, March 23, 2023 — a The Harris Poll survey of nearly 13,500 adults in 14 countries, 12% and 11%, 76% and an average of four failed searches per month, estimated losses of more than $2 trillion per year.
- Baymard Institute: “Ecommerce Search UX 2026: 8 Search Query Types”, published September 12, 2024, updated April 29, 2026 — eight query types, shares of sites with issues, 56% of sites, and more than 170 sites and apps in the benchmark.
- Habr: “The Present and Future of Search in Ecommerce”, December 18, 2025 — BM25, vector, and hybrid search, and the connection between search metrics and revenue.
- Retail Rocket: “Smart Search for a Website” — an example of the capabilities of an off-the-shelf service, according to the provider's description.
Comparison tables, options, a plan, and metrics, as well as examples of queries in Russian, are an editorial analysis by AIrassvet. Foreign survey data are cited as an indication of the scale of the problem, not as a forecast for a specific store.
How AIrassvet Helps Implement Smart Search
If shoppers cannot find products that are in the catalog, or if the catalog is too complex for off-the-shelf services, search can be built around your data. On the AIrassvet website, which also uses the name “AI Studio of Vladimir Lomtsev,” the following directions are described as applicable to this task:
- Auditing search queries and preparing the catalog: attributes, name normalization, synonym dictionaries.
- AI integration into catalog search: vector databases and a language model for understanding queries.
- An AI assistant for product selection, integrated with the website and catalog.
- Integration, testing, and launch with A/B comparison against the current search.
The first step is to choose one process, describe its current metrics, data sources, constraints, and the criterion by which you will consider the pilot successful.
Bottom Line: Semantic Search Plus a Well-Structured Catalog
Shoppers are increasingly looking for products the way they would ask a salesperson: by purpose, problem, or compatibility. Regular keyword-matching search fails on these queries, and according to Baymard, search problems exist on most tested sites. Smart search solves this with a hybrid of full-text and vector search, but only together with completed attributes and dictionaries.
Start by exporting your search queries, a benchmark set, and an A/B test. Make the decision based on revenue per search session, not on your impression of the demo.