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How AI Search Improves Product Discovery on BigCommerce

Author: Daniel Willson Published On: 24 Sep 2026 AI

Online shoppers expect ecommerce stores to understand what they mean, not simply match the exact words they type. This becomes increasingly important for BigCommerce stores with large catalogs, multiple product attributes, and customers who use descriptive or conversational searches.

Traditional keyword-based search can work well for straightforward queries, but it can struggle with misspellings, synonyms, long-tail phrases, and searches involving several requirements. This is where AI search for BigCommerce can provide a more intelligent approach to product discovery.

Why Product Search Matters for BigCommerce Stores

Search is one of the most direct ways customers communicate purchase intent. A visitor searching for a specific product has already moved beyond general browsing and is actively looking for something that meets a particular need.

However, a search box is only useful when the results are relevant.

Consider a customer searching for:

“comfortable black office chair under $300”

A basic search engine may treat these words primarily as individual keywords. An AI-powered search system can interpret the query as a combination of product type, color, use case, and price preference.

This difference can make product discovery considerably easier.

For a broader explanation of how modern ecommerce search works, see this guide to ecommerce search.

Where Traditional BigCommerce Search Can Struggle

BigCommerce provides native storefront search and product filtering capabilities, but growing stores can encounter limitations as their catalogs become more complex.

For example, shoppers may use:

The challenge is that the language customers use is not always the same language used in product catalogs.

A product might be described internally as a “memory foam work sneaker,” while a shopper searches for “comfortable shoes for standing all day.”

The products may be highly relevant even though the wording is different.

This is one of the key areas where semantic search can improve product discovery.

What AI Search Adds to BigCommerce

AI-powered search can combine traditional keyword matching with technologies designed to understand meaning and shopper intent.

The result is a search experience that can interpret queries rather than simply looking for matching words.

1. Semantic Understanding

Semantic search focuses on the meaning behind a query.

Instead of requiring the shopper's wording to appear directly in a product title or description, the search engine can identify related concepts.

For example:

Search: “winter jacket for hiking”

A semantic search engine can recognize concepts such as outdoor use, warmth, weather protection, and jackets, helping it identify products that may not contain the exact phrase in their titles.

For a deeper explanation, see bCloud AI's guide to AI search for ecommerce.

2. Better Handling of Natural-Language Queries

Modern shoppers increasingly search using complete phrases.

Instead of typing:

“black shoes”

they might search:

“comfortable black shoes for walking around a city all day.”

AI search can break down the intent within the query and use those signals when retrieving products.

This can make the search experience feel more conversational while still maintaining the precision required for ecommerce.

3. Hybrid Search for Precision and Meaning

Pure semantic search is not always enough.

For example, B2B customers frequently search using exact product codes, model numbers, or SKUs. In these situations, exact keyword matching remains extremely important.

Hybrid search combines keyword retrieval with semantic retrieval. This allows a system to preserve precision for exact terms while also understanding descriptive searches.

This approach is particularly useful for BigCommerce stores with large or technically complex catalogs.

4. Automatic Query Understanding

A shopper may enter several requirements in one sentence.

For example:

“blue running shoes under $150 for trail running”

An intelligent search system can identify different elements of that query, including:

Instead of forcing shoppers to select every filter manually, the search engine can use the query itself to improve product retrieval.

Improving Filters and Product Discovery

Filters remain an important part of ecommerce search, particularly for stores with many products.

Customers may want to narrow results by:

However, filters are only useful when the underlying product data is accurate and complete.

If some products are missing attributes, shoppers may accidentally filter out products that should have appeared.

This means improving search should not only involve the search engine. Merchants should also audit product titles, descriptions, attributes, categories, and custom fields.

AI Search and Zero-Result Queries

A “no results found” page can be a major problem for an ecommerce store.

Imagine a shopper searches for a product using a common synonym or makes a minor spelling mistake. If the search engine cannot understand the query, the customer may receive no results even though the store actually carries a suitable product.

AI-powered search can help reduce these dead ends through:

Merchants should also monitor their failed searches. These queries reveal how customers describe products and can expose gaps between catalog terminology and customer language.

The broader principles behind effective onsite search are covered in this guide to ecommerce site search.

Search Ranking Also Matters

Finding matching products is only one part of the problem.

The search engine must also determine which products should appear first.

A modern ecommerce search algorithm can consider different signals when determining product relevance. Depending on the implementation, these can include:

The objective is not simply to return products that technically match the query. It is to present the most useful results in an order that helps shoppers find what they need.

For more information, explore this guide to the ecommerce search algorithm.

BigCommerce Search for B2B Ecommerce

B2B stores often have more demanding search requirements than conventional retail stores.

A business buyer might search by:

For these customers, search accuracy is particularly important because a small difference in a product number can produce completely different results.

AI search can provide semantic capabilities while hybrid retrieval can preserve exact matching for critical identifiers.

This makes the combination of traditional and AI-powered retrieval particularly relevant for complex BigCommerce catalogs.

How to Improve BigCommerce Search Step by Step

Merchants do not necessarily need to replace their entire search experience immediately. A structured optimization process can help identify where the biggest opportunities exist.

Step 1: Analyze Search Queries

Start by reviewing what customers actually search for.

Look for:

This provides a practical picture of where the existing experience needs improvement.

Step 2: Improve Product Data

Review product titles, descriptions, attributes, categories, and custom fields.

Better structured product information gives search technology more useful information to work with.

Step 3: Improve Autocomplete

Autocomplete can help customers reach relevant products faster.

Instead of displaying only text suggestions, ecommerce search can surface relevant products or categories as shoppers type.

This reduces friction and can help guide shoppers toward products they already intend to purchase.

Step 4: Add Semantic Search

If keyword matching is consistently failing on descriptive queries, semantic search can help connect customer intent with relevant products.

This is particularly useful for large catalogs where manually creating synonyms and keywords becomes difficult to maintain.

Step 5: Measure the Results

Search optimization should be measurable.

Important metrics include:

Tracking these metrics before and after changes makes it easier to understand whether an optimization actually improved the customer experience.

When Should a BigCommerce Store Consider AI Search?

AI search becomes particularly relevant when a store experiences one or more of the following challenges:

Large product catalog:
Customers need better ways to navigate thousands of products.

Complex product attributes:
Products have multiple specifications, sizes, compatibility requirements, or configurations.

Descriptive searches:
Customers frequently use conversational or long-tail queries.

High zero-result rates:
Customers often reach empty search-result pages.

B2B search requirements:
Buyers rely on SKUs, part numbers, and technical terminology.

Multiple storefronts:
Different customer groups require different search experiences.

Limited search insights:
Merchants need better analytics to understand search behavior and revenue impact.

For stores evaluating the broader role of AI in product discovery, the AI e-commerce search guide provides additional context.

The Future of BigCommerce Product Discovery

Ecommerce search is moving beyond the traditional search-box model.

Customers increasingly expect online stores to understand context, intent, preferences, and natural language. That means future product discovery will likely combine several experiences, including search, recommendations, conversational interfaces, personalized results, and intelligent filtering.

For BigCommerce merchants, the goal is not simply to make the search box more advanced. It is to make the entire product discovery journey easier.

AI can contribute by helping shoppers describe what they want naturally while giving merchants better tools for relevance, ranking, personalization, and analytics.

Final Thoughts

BigCommerce provides a solid foundation for ecommerce search, but growing catalogs and changing customer expectations can create new challenges.

AI-powered search addresses many of these challenges by combining semantic understanding, hybrid retrieval, query interpretation, intelligent ranking, and improved handling of natural-language searches.

The most effective approach is to begin with actual customer search behavior. Identify where shoppers encounter irrelevant results or dead ends, improve product data and search experiences, and then measure the impact.

For businesses looking to move beyond basic keyword matching, AI search for BigCommerce can turn product discovery into a more intelligent, relevant, and customer-focused experience.

For a detailed look at the limitations of native BigCommerce search and practical improvement strategies, read BigCommerce Search: 7 Proven Fixes for Weak Native Results.

D

Daniel Willson

Published on 24 Sep 2026  |  Category: AI

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