13 best ecommerce search engines in 2025 (backed by data—and why most still fail your customers)

Key takeaways from this article

• Understand why most ecommerce search engines still fail customers in 2025
• Learn what defines a best-in-class search experience beyond basic keyword results
• Discover 13 top search platforms and where they fall short on discovery and conversion
• Get insights from Zoovu’s 2025 audit of 50 enterprise brands and real user behavior trends
• See how leading brands like IKEA and Brooks are transforming product discovery
• Walk away with a checklist of must-have features for modern ecommerce search

If your search doesn’t deliver, neither will your sales

That line has never been more relevant. In 2025, your ecommerce site search is no longer a feature, it’s the engine behind your entire shopping experience.

And yet, most ecommerce businesses are still running on outdated, ineffective search functionality that frustrates customers and kills conversions.

According to The 2025 State of Ecommerce Search Report from Zoovu, a staggering 0 out of 50 brands evaluated received an A grade for their onsite search experience. The data paints a bleak picture:

• 85% of search result pages lacked any guided discovery elements
• 76% failed to offer product comparisons
• 58% provided no dynamic filters
• Only 16% of brands earned a B- or better on post-search experience

In short: search is broken. And that’s costing companies revenue, retention, and customer trust.

This blog breaks down what the best ecommerce search engines actually do well, backed by research, and why tools like Zoovu are redefining search as a conversational, AI-powered discovery platform that solves the problems others ignore.

What defines the best ecommerce search engines today?

The ecommerce industry is in the middle of a Search 3.0 revolution.

Where old systems relied on static filters and keyword matches, modern ecommerce search solutions are powered by artificial intelligence, built to interpret user intent, and designed to guide users to relevant products, even when their search query is vague, subjective, or complex.

From Zoovu’s 2025 report and real user behavior analysis, here are the must-have features of a strong ecommerce search engine:

1. AI-powered personalization in real time

Customers expect personalized product recommendations as they search. Engines must process search history, clickstream data, and types of search queries in real time to show results that match actual needs.

2. Conversational discovery, not keyword matching

Traditional search tools fail when customers use subjective or use-case based terms like “bike for commuting” or “affordable couch.” These made up 63% of all zero-result queries in Zoovu’s study.

A real conversational search platform like Zoovu engages customers, asks follow-up questions, and removes the guesswork from product discovery.

3. Advanced search capabilities

From typo correction to voice search and autocomplete suggestions, a modern engine should guide users, not punish them for imperfect input.

4. Personalization and merchandising

AI-driven merchandising combines behavioral targeting with business rules to showcase high-converting, high-margin items dynamically.

5. Post-search optimization

The post-search experience is often ignored. But Zoovu’s report shows that post-search scored 19% lower on average than query or result relevance. The best platforms integrate comparison tools, product finders, and social proof right into the results page.

Which search engines are getting it right in 2025?

Let’s look at 13 top ecommerce search solutions. What they offer, where they shine, and where some still fall short.

1. Zoovu

Best for: Enterprise ecommerce adopting AI-powered, conversational product discovery.

Zoovu isn’t just a search engine, it’s a conversational discovery platform built to replicate the personalized, in-store experience online.

Where most tools wait for the user to guess the right search terms, Zoovu takes the lead, asking the right questions, clarifying intent, and guiding shoppers toward confident decisions.

Zoovu powers product discovery for Microsoft, Canon, and GE Healthcare. Brands that understand the value of personalization at scale.

Key features:

• Conversational AI for guided selling
• Dynamic, real-time product search experiences
• Deep customer insights from every search interaction
• Omnichannel support across regions, languages, and retailers
• Visual configuration, smart bundling, and compatibility matching
• Instant deployment with no-code tools and real-time analytics

Why it’s different:

Zoovu is built for Search 3.0, merging product discovery, guided selling, and AI-powered personalization into one cohesive solution. It doesn’t just handle search requests, it orchestrates the entire shopping experience.

2. Algolia

Best for: Fast, flexible search at the developer level.

But… struggles with discovery, guidance, and shopping behavior.

Algolia has earned a reputation for speed and precision. Its instant search and API-first model make it a favorite among dev teams.

But while it handles exact searches well, Algolia offers little in the way of customer experience optimization. There's no built-in guided selling, no contextual product discovery, and no native way to support subjective queries, a major limitation in 2025.

If your users don't type the “right” thing, they're left to fend for themselves.

3. Constructor.io

Best for: AI merchandising, user behavior scoring, and intelligent ranking.

Constructor uses clickstream data to power search relevance, product boosting, and advanced personalization. It supports large-scale catalogs and offers rich analytics.

It’s one of the better intelligent ecommerce search options, though its onboarding can be intensive for smaller teams.

4. Searchspring

Good for: Visual merchandisers and non-technical marketers.

Searchspring is great for marketing teams who want control over boosting, personalization, and product discovery without needing developers.
Their interface allows you to fine-tune the search process, curate personalized experiences, and launch campaigns based on customer behavior and user satisfaction.

5. Coveo

Good for: B2B and interal search. Less so for retail discovery.

Often too focused on documents, not buyers.

Coveo offers AI-powered site search with content indexing and personalization. But much of its DNA is rooted in enterprise knowledge management, not ecommerce discovery.

It lacks true conversational discovery, and shoppers using vague or subjective search terms will often hit a wall.

If your business sells technical products and needs a great internal search system, Coveo fits. If you're trying to improve conversion rates in DTC ecommerce? You’ll hit limits fast.

6. Bloomreach Discovery

Best for: Legacy DXPs and businesses deep in Bloomreach’s ecosystem.

But… it’s complex, costly, and not built for rapid decision journeys.

Bloomreach offers a powerful blend of search, CMS, and merchandising, but it comes with a high barrier to entry. Implementation is heavy, and while the platform supports AI-driven search, it still relies heavily on data cleanliness and engineering lift.

There’s no conversational interface. And for modern brands prioritizing customer engagement and personalized search, that’s a big miss.

7. Klevu

Best for: Shopify and mid-market brands looking for fast wins.

Klevu brings intelligent search and personalized recommendations to growing ecommerce brands. With NLP, voice capabilities, and solid analytics, it offers great bang for your buck, especially for teams without massive technical resources.

8. Syte

Best for: Visual product discovery in fashion and home goods.

Syte helps shoppers search using photos, not just words. Its AI-powered personalization engine is built for discovery via visual search, and it excels in apparel, lifestyle, and furniture verticals.

9. FactFinder

Best for: European enterprise retailers seeking precise, language-specific search.

FactFinder combines semantic search, recommendations, and smart navigation tools. While its UX is more traditional than Zoovu, it’s a strong engine for international brands handling multiple languages.

10. Doofinder

Best for: Small businesses looking for instant search and smart results.

Doofinder is easy to set up, quick to launch, and offers autocomplete, filters, product tags, and search analytics for storefronts without big dev teams. It's not conversational, but it gets the job done.

11. Unbxd

Best for: Enterprise brands needing enterprise control.

Backed by Netcore, Unbxd offers AI-powered site search, personalization, and product recommendations. It's strong on rules and configuration but lacks the intuitive interface and conversational flow modern shoppers expect.

12. Luigi’s Box

Best for: European SMBs needing plug-and-play intelligent search.

Luigi’s Box provides quick deployment, typo correction, product type search, and analytics, all with minimal setup. It’s gaining traction among brands seeking flexibility without deep complexity.

13. Omega Instant Search

Best for: Shopify merchants seeking lightning-fast, keyword-based search.

Omega’s smart search app for Shopify does what it says: delivers speed and ease. While not feature-rich, it’s ideal for brands that want quick ROI without deep customization.

Methodology: How the top search engines were evaluated

The insights in this blog were built on Zoovu’s analysis of 250 search queries across 50 brands, covering 1,450 unique elements of the search experience. Each search experience was assessed across three critical stages:

1. Query stage

What is it? The initial interaction with the search bar or search box.

What was scored:

• Size and placement of the search bar
• Presence of autocomplete suggestions, predictive queries, and search prompts
• Page load time after query submission

Poor performance indicators:

• Search bar hard to locate
• No interactive features
• Load time greater than 3 seconds

High performance indicators:

• Prominent, interactive search bar
• Predictive search and recommendation tools
• Load time under 1 second

2. Results stage

What is it? The search results page and how well it delivers on intent.

What was scored:

• Search accuracy and relevance of first-page results
• Filter quality and interactivity
• Clarity of product descriptions

Poor performance indicators:

• Irrelevant results (less than 50% relevancy)
• Sparse product details
• Nonexistent or generic filters

High performance indicators:

• 80%+ relevancy
• Dynamic, needs-based filters
• Strong visual and contextual content

3. Product discovery stage

What is it? The platform’s ability to guide the customer toward an informed decision.

What was scored:

• Presence of product finders, personalized recommendations, comparison tools
• Embedded chat or conversational AI
• Highlighting of attributes that match user intent

Poor performance indicators:

• Lack of guidance post-search
• No differentiation between similar results

High performance indicators:

• Seamless guided discovery
• Visual comparability, compatibility indicators, and customer reviews

Scoring Summary: Each stage was scored out of five. The highest overall score recorded was 3.62/5, with most brands falling between C- and C+. No brand earned an A.

Seven key trends from the 2025 ecommerce search audit

1. Brands excel at basic product searches

When users type clear, broad queries like "running shoes" or "couch," most platforms respond well:

• Broad product searches: 87% first-page relevancy
• Specific product searches: 79% relevancy

2. But they fail use case and subjective queries

• Use-case queries ("bike for commuting"): only 48% relevancy
• Subjective queries ("affordable couch"): just 40% relevancy
• These queries accounted for 63% of all zero results

3. Post-search experience is fundamentally flawed

• 85% of search results lacked product finders
• 60% had no chat functionality
• 76% didn’t include product comparison features
• 28% of brands received a D+ or worse for post-search experience

4. Even simple searches are overwhelming

• Broad queries often return 2.43x more results than exist in the product catalog
• Only 16% offered any kind of guided or filtered experience

5. Brands have tools but don’t use them together

• Many PDPs include attributes like "commuter-friendly" or "waterproof"
• But only 29% of brands highlight these in the search results
• 71% rely on product titles alone, which get overlooked

6. Search works, if you type the right words

• 52% of brands failed to return relevant results for synonyms
• 54% failed on misspelled search terms

7. Social proof is underused

• While 90% of PDPs included reviews, only 56.8% of search results featured them
• 62.8% showed product tags like "Top Seller" or "Recommended"

Five brands doing it right: Examples of great search experiences

1. IKEA

IKEA integrates product finders into search results, guiding users to discover the right fit without overwhelming them. Their use of personalized experiences and interactive filters reduces FOBO and improves customer satisfaction.



2. Osprey

Search results highlight intended use cases like "everyday commute" or "outdoor ready," while filters are smartly categorized by trip duration and capacity. A great example of faceted search with context.



3. Brooks

Offers an intuitive search bar with rich predictive search: product images, reviews, and even result counts by category, reducing bounce and increasing conversion rates.



4. Calphalon

Integrates social proof into search and filtering, including review stars and award labels, to guide users through commercial queries customers care about.



5. Ashley Furniture

Implements "search within filters," allowing users to narrow attributes like size or color while showing the number of matching products, a small UX detail that improves user satisfaction.



Final take: Search is broken but fixable

Here’s what the data says:

• Most ecommerce search tools fail at understanding customer intent
• Few deliver personalized shopping experiences in real time
• Even fewer can guide users through complex queries with confidence
• And nearly none support conversational discovery out of the box

Zoovu changes that.

By merging AI-driven search, behavioral intelligence, and a guided discovery platform, Zoovu helps customers stop searching and start deciding, while giving brands the customer data platform-level insights they need to optimize every touchpoint.

Want to see it in action? Book a demo today.