Ecommerce search has gone from being simply a navigation tool, to also generating revenue and increasing conversion rates, as well as influencing the way customers experience your brand when they use search on an ecommerce site. The intention behind using search is high and the customer knows what they want to buy, but it is also important to note what they use to express this desire for a specific product.

According to various studies conducted in the ecommerce industry, customers who search within the ecommerce website are 2 – 4 times more likely to convert than customers who do not perform a search on the ecommerce site. Despite the revenue-generating potential, many brands do not place enough emphasis on search to help improve conversion rates.

AI Analytics has now emerged as a critical component for ecommerce! The use of AI search analytics tools allows ecommerce retailers to gain an understanding of shopper intent in real-time with the ability to take direct action upon recommendations from the tools.

In this guide, we will explain the various types of AI search analytics tools, why these tools are critical for continued growth and success, key features of the tools, the best tools currently available (in 2025), and how leading ecommerce brands utilize search data to enhance the conversion experience, increase average order value, and make better educated merchandising decisions.

What Are AI Search Analytics Tools?

AI today represents a new way of understanding user activity on a website with the help of AI-powered Search Analytics tools. Search analytics tools are designed to look at more than just the user’s queries entered in the search box; they also look at the behaviour and intent of the user.

The AI-powered search analytics tools examine several areas:

• The user’s search queries
• The user’s click behaviour
• The user’s query refinements
• The user’s zero-result searches
• The filters the user uses
• Any downstream actions (add to cart, purchase, exit)

AI search analytics tools utilise Machine Learning to cluster together similar searches, identify synonyms, spelling errors and identify patterns in data that would not otherwise be viewed by the human eye in a spreadsheet.

For example:

A traditional site report would show;
“Wireless earbuds” has 1200 searches

AI Search Analytics report shows that:

• “wireless earbuds”, “bluetooth earphones”, “ear buds”, and “pods” have the same intent cluster
• Many of the users refining this search are adding “noise cancellation” to their search.
• Searches that include the term “ANC” convert at 38% higher than average.

For e-commerce teams, having real-time intent-based insights from AI Search Analytics will improve their ability to optimise their marketing efforts rather than relying on historical data. AI search analytics tools will turn common user search behaviours into an opportunity for growth.

Why AI Search Analytics Matter for Ecommerce Growth

Search engines show potential buyers some of the highest percentage of visitors. AI-powered Search Analytics Tools tell you exactly what those buyers want (in their own words) and make it happen on your Store.

Removing Zero Result Searches
Zero Result Searches (Silent Revenue Killers)

Example:
The Apparel Brand was seeing hundreds of searches per month for “Linen Co-ord Set” and 0 Search Results. Therefore, AI Search Analytics Identified that this was a Large High Demand Gap.

After Brand added this Product Category and defined Synonyms, they were able to Recoup Lost Demand and achieve a 12% Increase in Search-Driven Revenue within a Month.

Diminishing Conversions
AI Search Analytics identifies the search queries that convert the best and those that cause exits.

For a Beauty Ecommerce seller, the Search Resource revealed that Searches that contained the words “For Sensitive Skin” had High Engagement

Supporting Marketing and Product Decisions Using Customer Insights and Data

There is no longer a separation between Search and Marketing Data.

Example:
An electronics retailer used Insight through Internal Search Analytics to indicate increasing customer interest in USB-C monitors.

Accordingly, the company created separate SEO content and Paid Search Keywords and also accelerated their Inventory Growth, resulting in an increased ROI across marketing channels based upon active demand from their customers.

Essential Features of AI Search Analytics Tools

When assessing AI Search Analytics solutions intended for eCommerce users, you should focus beyond just their reporting abilities.

• Search Query Intelligence
• Identification of search intent and synonyms
• Zero Result Search Tracking
• Attribution of Clicks and Conversions to Search Queries
• Understanding User Behaviour Through Analytics
• Merchandising Analytics
• AI Driven Recommendations
• Ease of Integration with Popular Design Platforms

The best AI Search Analytics Tools will provide detailed information on what to change on your website as well as what focus area(s) to improve next.

Best AI Search Analytics Tools for Ecommerce (2025)

Expertrec Search analytics

Expertrec has been recognized as the best-performing analytics tool for ecommerce using Artificial Intelligence.

Case Example:
The direct-to-consumer home décor brand realized that many of the searches for “wall shelves” would refine to “floating shelves.” Therefore, the company improved its search conversions by 22% by re-ranking “wall shelves” and adding synonyms for “floating shelves” — all without adding new products!

Algolia Search Insights

Algolia provides advanced analytics for tracking search queries, clicks, and user behaviour.

Constructor.io Analytics

Constructor.io provides an easy and effective way to gather analytics about product merchandising and discovery.

Bloomreach Discovery

Bloomreach is an enterprise solution that includes both site search and personalisation capabilities.

Real-World Examples of How Ecommerce Businesses Integrate Expertrec Across Their Various Teams

• Merchandising Teams: Use high intent search data to improve product discoverability
• Marketing Teams: Identify real user’s keywords for SEO and paid campaigns
• UX Teams: Minimize search friction by improving search experience and filtering
• Product Teams: Identify unmet needs and validate/innovate products

Search analytics becomes an intelligence layer that can be used across functions, instead of a reporting tool.

Conclusions

For ecommerce brands that wish to remain competitive and be successful, AI-enhanced search analytics is now a requirement.

A well-designed tool can help ecommerce businesses translate shopper behavior into tangible opportunities.

By investing early in AI-powered search intelligence, ecommerce businesses will be better equipped to provide an excellent shopping experience, increase conversion rates, and make meaningful decisions quickly and effectively that will compound over time.