Shoppers who use a brand’s site search behave differently. They spend more—2.6x more than non-searchers. They convert more. They drive 44% of ecommerce revenue.

And yet, when 50 major retail brands were examined, not one achieved an A grade for onsite search. Irrelevant results, dead-end queries, and the inability to understand real-world language leave revenue on the table.

The truth is simple: poor site search is a silent revenue killer. And many retailers have yet to embrace the solutions that could fix it..

If you know search matters but need a clear, actionable path to choosing, implementing, and measuring the right ecommerce search solution, this guide is for you.

Here’s what you’ll discover:

• The essential elements of a world-class search solution

• The top ecommerce search tools of 2025

• How to know when you’re ready to invest in search

• A framework for evaluating vendors and selecting the right fit

• How to measure impact, value and ROI

Why ecommerce search tools matter more than ever in 2025

Ecommerce search is a primary driver of both user experience and business performance. The stakes are higher than ever – and so are customer expectations. The search experience an ecommerce brand offers on its website – from the search bar itself to the underlying search technology – has a significant impact on an ecommerce brand’s revenue.

• When search works, it’s a high-intent funnel: shoppers who use a brand’s search bar are often ready to buy, typically spending 2.6x more than non-searchers and contributing to higher conversion rates.

• Desktop conversion rates are 2x higher for retailers with advanced search vs. basic search capabilities.

• In 2022, the Baymard Institute published an overview of types of ecommerce site search queries and reported that “42% of all sites perform below an acceptable ecommerce search UX performance…. To make matters worse, 8% of sites have a downright ‘broken’ ecommerce search UX…”.

Learn how Under Armour increased conversion rates by 35% using AI-powered ecommerce search from Algolia.

Search needs to support omnichannel journeys

Consistency is key – over 70% of shoppers today are considered omnichannel, meaning their entire shopping journey is taking place across multiple touchpoints and mediums like web, app, store, and support.

Ecommerce search tools today help brands deliver consistent search experiences across different channels, while making sure search is fast, accurate, and personalized.

Your customers expect hyper-personalization

More than ever, what we hear from our customers is that they want to implement hyper-personalization based on visitor behavior, preferences, and real-time context.. AI provides a way to do this, but where do you start? 

It begins with the 1st party data on your website, and that data lives in your site search. Search is the fulcrum for several reasons: 

• Your entire product catalog is contained and displayed by your on-site search engine via high quality search results and catalog and collections pages. The same merchandising rules that drive site search results are leveraged throughout your site.

• As users engage with content — clicks, conversions, signups, purchases, add-to-cart, etc. — this data is leveraged by search engines to improve results even more and deliver recommendations, and this data is also used in AI personalization engines.

• LLM chatbots also use the search data in conversational shopping and recommendations. Data from chat can also influence search results.

What to look for in ecommerce search solutions

Keep in mind that the best ecommerce search solution ultimately depends on your business’s technical resources, complexity, and priorities. But in 2025, best-in-class solutions should generally deliver on several key fronts. Here’s what to look for:

Search relevance and AI capabilities

Search relevance is the foundation of a high-converting experience. Inaccurate or generic results drive shoppers away. Leading platforms now use advanced AI and machine learning technologies to boost relevance, including things like:

• Natural language processing (NLP): Understands intent behind queries, even with synonyms, misspellings, or conversational language.

• Semantic search and vector technology: Goes beyond keywords to surface products based on meaning and context. Vector search enables matches even if the query doesn’t use exact product terms.

• Hyper-personalization: Combines real-time, contextual, and behavioral data to tailor results for each shopper — factoring in browsing history, location, device, and more.

• Learning-to-rank algorithms: Using AI to improve the ranking of results based on user interactions and feedback, all while optimizing for conversions over time.

• Agent-based search: AI agents stitch together LLMs, vector databases, and custom orchestration code to deliver top results as shopping chat with AI bots. 

Performance and scalability

Speed is non-negotiable. Amazon and Google famously found that even 100 millisecond lags in page load times can cost millions of dollars of lost revenue. Shoppers expect instant results, especially on mobile devices. Performance also impacts SEO and overall satisfaction.

• Millisecond response times: Your search must deliver results in under 100ms, even on mobile and during peak traffic.

• Global CDN and infrastructure: A distributed network ensures fast, reliable performance for international customers.

• Traffic spike handling: The platform must scale automatically for events like Black Friday, product drops, or flash sales.

• Real-time indexing: New products, price changes, and inventory updates should be reflected in search results within seconds.

Merchandising and business control

Non-technical teams need the ability to shape the search experience. This increases agility and allows for rapid response to market trends.

• Visual merchandising tools: No-code interfaces, such as our own Merchandising Studio, let merchandisers adjust product ordering, create campaigns, and set rules without IT support.

• Rule-based promotions and boosting: Ability to promote high-margin items, prioritize inventory, or highlight seasonal collections.

• A/B testing: Test different ranking strategies, filters, or layouts to optimize for conversion.

• Dynamic filtering and faceted search: Allow customers to refine results by attributes like size, color, material, availability, and more.

Analytics and insights

You can’t optimize what you can’t measure. Actionable analytics are essential for continuous improvement.

• Real-time dashboards: Monitor search performance, top queries, and conversion funnels as they happen.

• Zero-result tracking: Identify where customers are searching but not finding products, then adjust content or inventory accordingly.

• Query analysis: Understand how shoppers describe products and use those insights to improve data and marketing.

• Conversion tracking: Measure the impact of search on sales and average order value.

• Customer behavior insights: Uncover patterns and preferences that inform site design and merchandising.

Integration and technical requirements

Most ecommerce teams operate in complex, multi-platform environments. Seamless integration is crucial.

• Pre-built connectors: Ready integrations for popular commerce platforms like Shopify, Magento, BigCommerce, Salesforce Commerce Cloud, and more.

• Composable commerce support: Ability to plug into a modular, API-driven tech stack.

• API flexibility: Well-documented APIs for custom use cases and headless implementations.

• Headless commerce compatibility: Decoupled architecture for fast, flexible front ends. Many companies are adopting a MACH framework — microservices, API-first, Cloud-native, and Headless — to help them build scalable, forward-thinking systems.

Top ecommerce search solutions for 2025

The search vendor landscape is crowded. Here’s an up-to-date, objective comparison of the leading platforms.

Feature	Algolia	Elasticsearch	Searchspring	Klevu	Constructor.	Bloomreach	Coveo
AI/ML Capabilities	Excellent	Customizable	Good	Advanced	Excellent	Excellent	Excellent
Speed/Performance	Very High	High	High	High	High	High	High
Merchandising Tools	Yes	Limited	Yes	Yes	Yes	Yes	Yes
Developer Experience	Excellent	Extensive	Moderate	Good	Good	Good	Good
Ease of Setup	High	Low	High	High	Moderate	Moderate	Moderate
Ease of Use	High	Low	High	High	Moderate	Moderate to High	Moderate
Time to Value	Short	Long	Short	Short	Moderate	Moderate	Moderate to Long
Pricing Model	Usage-based + enterprise	Resource and usage-based	Usage-based	Tiered	Enterprise	Enterprise	Enterprise
Markets served	SMB and enterprise	Enterprise	SMB	SMBs	Enterprise	Enterprise	Enterprise

Algolia

Overview: Algolia is a market-leading, AI-powered search and discovery platform known for its speed, reliability, and scalability. Algolia offers robust analytics, business-friendly merchandising controls, and extensive integrations with platforms like Shopify, Magento, and Salesforce Commerce Cloud — making it a top choice for brands seeking scalable, customizable search experiences.

Pros:

• AI-powered NeuralSearch engine combines keyword and vector search for highly-relevant search results

• Powerful merchandising tools: visual rule builders, promotions, and dynamic personalization without deep technical skills

• Rich search analytics to optimize product discovery and shopper journeys

• Integrations with leading ecommerce platforms (Shopify, Magento, Salesforce Commerce Cloud, BigCommerce, etc.)

• Generous free tier for customers to build proof of concept capabilities

Cons:

• B2B support lags behind B2C capabilities such as account-based search

• Deep customizations or unique use cases may require developer resources

• Learning curve for advanced features if migrating from basic or built-in search

Best For: Algolia leads the pack in the latest Gartner Magic Quadrant on Search and Product Discovery for its broad array of options and capabilities supporting enterprise-scale solutions, AI capabilities, and trust by customers globally. 

Pricing:

• Free tier: up to ~10,000 search requests/month and 100,000 records

• Paid tiers: ~$0.50 per additional 1,000 queries and ~$0.40 per 1,000 records/month

• Custom pricing for premium and enterprise tiers with add-ons (NeuralSearch, Analytics, merchandising tools)

Elasticsearch

Overview: Elasticsearch is a flexible, open-source search and analytics engine (built on Apache Lucene), often used by technical teams for ecommerce search implementations needing full control over indexing, relevance, and architecture

Pros:

• Supports full-text search, filtering, analytics, and near real-time indexing

• Flexible query API with full control over ranking, relevance, aggregations, and search logic

• Large, active global community and ecosystem of tools/plugins (Logstash, Kibana, Beats, etc.), strong developer resources

Cons:

• Significant technical complexity; requires specialized engineering for setup, tuning, scaling, and maintenance

• No built-in ecommerce features like merchandising UI, personalization dashboard, or native analytics

• Total cost of ownership can grow quickly: includes infrastructure, engineering hours, hosting, and maintenance

Best For: technical teams or organizations with in-house engineering resources who need full control, customization, and scalability for complex or unique ecommerce search projects.

Pricing:

• Open-source version is free to use

• Elastic Cloud (Elasticsearch as a managed service) starts from approximately $16/month and scales with resources, which include hosting, features, and support layers

Searchspring

Overview: Searchspring is a unified ecommerce platform for AI‑driven search, merchandising, personalization, and analytics. It supports leading platforms like Shopify, Magento, BigCommerce, and Adobe Commerce, enabling retailers to enhance product discovery and boost conversion with minimal coding

Pros:

• Intuitive interface and strong support for non-technical users (no-code merchandising, drag‑and‑drop rules)

• Integrates with major ecommerce platforms and offers unified analytics and personalization

Cons:

• Advanced configurations and custom templating may require technical or developer involvement

• Starting cost (~$599/month) can be high for small or lean merchants

Best For: Best for SMB merchandisers and ecommerce retailers who want intuitive, no-code tools to manage search, recommendations, and visual merchandising without heavy technical involvement.

Pricing:

• Starts at ~$599/month for the Basic usage-based plan

• No free tier but demos are available

• Enterprise pricing available via custom quote

Constructor

Overview: Constructor is an AI-powered search and product discovery solution for ecommerce brands focused on customer experience and personalization. It uses machine learning and clickstream data for real-time personalization, and offers a unified platform for search and recommendations.

Pros:

• Purpose-built, enterprise-grade AI-powered search for ecommerce

• Unified commerce discovery modules exist under one platform

• Strong analytics and predictive insights tailored to ecommerce workflows

Cons:

• No public pricing; recommended for enterprise deployments only

• Onboarding and proof-of-concept process may take several weeks and require stakeholder involvement

Best For: Retailers and enterprise brands with complex catalogs who prioritize advanced AI personalization and unified discovery experiences.

Pricing: Pricing is custom and enterprise-focused; includes a free proof‑of‑concept “ROI assessment” phase (typically a 4-week proof schedule) before purchase

Klevu

Overview: Klevu is an ecommerce search and navigation platform built for mid-market brands on platforms like BigCommerce, Shopify, and Magento. It provides fast setup, AI-enhanced semantic search, self-service merchandising, and multilingual support. .

Pros:

• Fast implementation and launch, plus strong usability: non-technical users can manage merchandising and search rules easily

• Self-service tools for merchandisers, without needing developer resources

Cons:

• Less robust analytics and recommendations compared to other tools like Algolia, Coveo, and Bloomreach

• May not scale well or support large catalogs or complex B2B requirements

Best For: small to mid-sized ecommerce brands—especially international stores—looking for fast setup, multilingual support, and easy-to-use AI search with flexible pricing.

Pricing: Entry-level AI-powered plans start around €449–€500/month (for search + recommendations modules)

Bloomreach Discovery

Overview: Bloomreach Discovery is part of the Bloomreach Commerce Experience Platform, combining AI-native site search with recommendations, merchandising, content, and headless CMS—all aimed at accelerating commerce personalization and content‑commerce convergence

Pros:

• AI-powered search out of the box with minimal manual tuning

• Built-in merchandising, recommendation, and content discovery features in one platform

Cons:

• Implementation can be complex and may require technical support or ongoing professional services

• Total cost of ownership can quickly rise with modules

• Limited customization and reporting capabilities and features

Best For: large and enterprise organizations looking for a unified ecommerce platform that combines search, merchandising, content management, and personalization for both B2C and B2B.

Pricing:

• Custom pricing based on modules, customer base, catalog complexity, and event volume

• Reports suggest setup fees start at ~$4,000; annual contracts with usage fees that scale with volume

Coveo

Overview: Coveo is a cloud-based, AI-driven relevance and personalization platform serving enterprise use cases across ecommerce, customer service, and content. It offers unified indexing, deep learning insights, and broad connector ecosystem for multichannel personalization across touchpoints

Pros:

• Strong personalization capabilities via Coveo Relevance Cloud; adapts based on interactions and context

• Extensive set of connectors (site search, CRM, product catalogs, support data) and flexible configuration

• Rich analytics and relevance metrics for tuning search and content impact

Cons:

• Deployment complexity and high technical overhead can slow time-to-value

• Pricing and platform sophistication may be overkill (and too expensive) for smaller brands or teams without engineering support

Best For: enterprises and B2B brands with multi-channel or complex digital ecosystems who need advanced AI capabilities,integrations, and personalized experiences across multiple touchpoints.

Pricing:

• Offers two commercial tiers: Pro and Enterprise

• Pricing based on query volume and modules; Pro includes basic features (200K queries, up to 3 connectors), Enterprise offers unlimited connectors, recommendations, and omnichannel extensions

• Free trial and usage-based volume discounts available

Signs your ecommerce site is ready for advanced search tools

Before investing in a new search solution, assess if and where your business is hitting the limits of legacy search. Here are the most common signals.

Performance indicators

• Low Search-to-Conversion Rates: If your search-driven conversion rate is below 5–10% (or significantly lags your site average), there’s likely an issue with result relevance or experience.

• High Bounce Rates from Search Pages: When users land on a search results page and quickly exit, that’s a clear sign search isn’t delivering what they want.

• Zero-Result Queries: Traditional search tools struggle with subjective, long-tail, or use-case driven searches. Phrases like “bike for commuting” or “affordable couch” are common, but many retailers’ search engines can’t interpret them. In Zoovu’s 2025 study, these made up 63% of all zero-results queries.

• Customer Complaints: If support tickets or reviews mention trouble finding products—even when they’re in stock—you’re seeing real business impact.

Business growth signals

• Large or Growing Catalog: If your catalog exceeds 1,000 SKUs, manual curation and static search rules no longer scale.

• International or Multisite Expansion: Selling into new markets introduces language, currency, and regional preference challenges that require flexible, multilingual search.

• Complex Product Attributes: Multiple variants, bundles, or technical specs require search engines that can parse and rank based on nuanced product data.

• Advanced Merchandising Needs: If you need to promote, pin, or demote products dynamically—or want to run A/B tests and campaigns driven by search—basic tools won’t suffice.

Technical readiness

• Platform Limitations: Your current ecommerce platform’s built-in search may be basic or inflexible, especially for custom business rules or merchandising.

• Integration Requirements: You need to connect search with PIM, CRM, analytics, or other parts of your stack.

• Team Capacity: Search projects require collaboration across product, engineering, and merchandising. If you have the resources to own implementation and optimization, you’re ready to extract value from advanced tools.

AI-readiness

• Data Foundation: AI thrives on high-quality, structured data. Event data is crucial to driving ROI for your AI initiatives. Is your current data collection and data cleanliness sufficient to effectively fuel AI-powered search and personalization?

• Talent and Skills: It’s incredibly expensive to build AI from the ground up, so you’ll most likely want to identify the right partners and vendors to help you leverage AI with your tech stack. 

• Use Case Identification: Everyone is clamoring to bring AI into their organizations, but how do you do that effectively? It starts by pinpointing specific business challenges that AI in search can solve, such as reducing providing personalized recommendations at checkout or enabling conversational commerce.

If you’re seeing these signals, it’s time to evaluate an advanced search platform. The business upside—higher conversion, lower bounce, and better customer retention—can be immediate and significant.

Evaluation criteria for selecting ecommerce search tools

50% of retailers report revenue gains directly attributable to their site search tool – that number jumps to 93% for advanced and AI-powered tools.

Use the below criteria, combined with the vendor analysis and components list earlier, to figure out which search solution fits your brand and needs best.

Business-needs assessment

Every business has different requirements to guide how it will implement search.

• Catalog Size and Complexity: Larger catalogs need more sophisticated indexing, ranking, and filtering capabilities.

• Business Model: B2B, B2C, and marketplace models have different requirements for pricing, segmentation, and search logic.

• Traffic Volume and Growth: Make sure the platform can handle your current and projected demand.

• International and Multilingual Needs: Support for multiple languages, currencies, and region-specific catalogs is essential for global brands.

• Budget Constraints and ROI: Understand both initial and ongoing costs, as well as expected revenue uplift.

• Scalability and Total Cost of Ownership: Consider not just licensing, but also infrastructure, maintenance, and staffing.

Technical considerations

To achieve optimal outcomes, here are some technical considerations to discuss with your solutions providers:

Performance benchmarks

• Query Latency: Any solution you implement needs to be fast. Search results should feel instant (under 100 ms) and AI answers should be fast, too. Your search provider should support this. 

• Indexing Speed: How fast can new products, pricing, or attributes be updated and searchable?

• Uptime SLAs: Enterprise platforms should guarantee 99.9%+ uptime, backed by service-level agreements.

Integration requirements

• Platform Compatibility: Must work with your ecommerce platform, PIM, and other core systems.

• Data Pipeline Needs: Ability to ingest, transform, and synchronize catalog, inventory, and behavioral data.

• Frontend Flexibility: Support for custom UI, headless commerce, or PWA frameworks.

Implementation and infrastructure

• Deployment Model: Cloud-native SaaS, PaaS, or hybrid options. Hosting your own open source solution may sound great (open source is free, right?), however there are hidden costs to hosting and maintenance. SaaS is all-inclusive and may be a better option. 

• Implementation Requirements: Level of technical effort required for go-live. Do you want to build it or buy it? 

• Deployment Timeline: How long from kickoff to launch, and what resources are needed?

Vendor evaluation checklist

• Proof of Concept (POC) Process: Do you need to run a limited pilot to validate performance?

• Support and Documentation: Quality of technical docs, onboarding, and customer success resources.

• Community and Ecosystem: Availability of integrations, plugins, and a user community.

• Pricing Transparency: Clear, predictable pricing—preferably usage-based or with volume discounts.

A solid evaluation process, grounded in your specific business and technical needs, is the best way to avoid costly mistakes and quickly realize value.

Measuring ROI and success

Investing in a search platform only makes sense if it drives measurable business outcomes. Here’s how to quantify and track success.

Key performance indicators

Search quality metrics:

• Click-Through Rate (CTR) from Search: Measures how often users click a result after searching—a direct proxy for relevance.

• Zero-Results Rate: Tracks the percentage of searches that return no results. A drop here signals improved coverage and intent understanding.

• Search Refinement Rate: How often users adjust or refine their queries. High rates indicate poor initial relevance.

Business impact metrics:

• Search-Driven Conversion Rate: Percentage of search users who complete a purchase. This is the primary ROI metric.

• Average Order Value (AOV) from Search: Tracks if searchers spend more than non-searchers.

• Revenue per Search Visitor: Directly ties search performance to topline business impact.

ROI calculation framework

A typical ROI calculation for ecommerce search investment:

ROI = (Incremental Profit – Investment) / Investment x 100

For example, if your new search tool drives a 20% lift in search-driven conversion, and that equates to $500K in new gross profit against a $100K annual investment, your ROI is 400%.

See Forrester’s Total Economic Impact report for search. 

When to expect results

Generally, most ecommerce brands can expect to see conversion improvements within the first month after implementing a search solution, and clear ROI within 3-6 months.

• Months 1 - 2: lifts in conversion rates and engagement metrics

• Months 3 - 6: significant and measurable improvements – including higher conversion rates, AOV, and revenue per visitor

• Month 7 and beyond: continued optimizations, A/B testing, tuning, and merchandising help scale

Success stories

PetSmart, the leading omnichannel pet retailer in North America, saw better outcomes after migrating to Algolia from a legacy search tool:

• Revenue per session growth from search

• Conversion rates increased by 5-7%

• 2% increase in cart additions on their mobile app

These are not isolated results. A great ecommerce site search solution paired with analytics-driven optimizations can drive significant gains for ecommerce brands.

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After partnering with Algolia, Nuts.com increased conversions and empowered their merchandising teams:

• Customers that search had a 3x higher conversion rate than non-searchers

• Average order value per session for searchers increased 15%

“...since adopting Algolia as our search platform, we’ve seen that customers searching on our website tend to convert three times more than those who don’t.”

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Men’s luxury retailer Harry Rosen saw a 360% increase in conversion rate and 68% increase in transactions by implementing personalized search results to continuously improve results. 

• Using A/B Testing they discovered the best conditions to give the best search results to improve click-through-rates or conversions.

• These changes improved how expert advisors and the store’s customers have searched the site.

Better ecommerce search = better business outcomes

Ecommerce search isn't just a feature—it's a critical driver of conversions, revenue, and customer satisfaction. Yet most brands continue to underperform, missing significant revenue opportunities with outdated or inadequate search solutions.

Advanced search directly translates to higher conversion rates, increased average order values, and improved customer lifetime value.

The stakes are particularly high for mid-market and enterprise retailers, where even small improvements in search can lead to conversion lifts that generate millions in additional revenue.

What's next?

• Assess your current search experience against industry benchmarks and the readiness signals outlined above.

• Define your requirements using the evaluation frameworks in this guide.

• Shortlist vendors that align with your technical, business, and operational needs—including Algolia, which leads the market in speed, AI capabilities, and business-friendly controls.

• Demo the platform or plan a proof of concept to get an idea of real-world impact with your own catalog and users.

Ready to elevate your ecommerce search performance and see measurable ROI?

Get started with Algolia's AI-powered search solutions—connect with our team today to see what's possible for your business.