Beyond Keywords: How Modern Onsite Search Optimizes the Ecommerce Experience

A well-optimized ecommerce onsite search experience is a powerful revenue driver in ecommerce, so much so that customers who search drive 44% of ecommerce revenue. Yet, many retailers struggle to deliver results that meet their expectations. Research shows that 41% of ecommerce sites have search usability issues, leading to missed revenue opportunities and frustrated shoppers.

This guide covers the key components of high-performing search, common challenges merchandisers face, and how AI-driven solutions can streamline workflows and increase revenue. You’ll also learn how to assess your current search experience and identify areas for improvement.

What is Ecommerce Onsite Search? 

An onsite search bar (theoretically) allows users to describe what they’re looking for with their own words, speeding up the time it takes them to discover (and hopefully purchase!) products. 

Early onsite search relied on basic text matching, which often led to irrelevant results, poor shopper experiences, and lost revenue opportunities. Now, well-designed search engines go beyond matching keywords. It interprets customer intent and understands context on a 1:1 basis, all with the goal to deliver attractive results.

How AI is transforming ecommerce onsite search

AI-powered search uses emerging technologies like:

• Transformers: These deep-learning models process natural language more effectively than traditional keyword matching so search engines can interpret queries in context
  
  
• Large Language Models (LLMs): By analyzing vast amounts of shopping behavior data, LLMs recognize patterns in customer intent, refining search accuracy and filtering out irrelevant results​
  
  
• Generative AI (GenAI): GenAI enhances real-time personalization (and can cater to a variety of intents) so shoppers receive results that align with their preferences, past behaviors, and session context​

These innovations allow ecommerce businesses to move beyond relevance and provide dynamic search experiences.

Take Sephora, for instance, which uses Constructor’s ecommerce search and autosuggest, among other solutions. In the image below, the user searched for “winter foundation.” The search engine picked up on context and returned results focused on hydration.

Hydrating foundations are returned for a shopper who’s looking for “foundation winter.” 

Why Strong Onsite Search Matters

Shoppers who use onsite search convert at higher rates than those who only browse, making search one of the most valuable tools for ecommerce revenue growth. Unfortunately, many retailers still struggle with search usability.

The cost of poor search experiences

A subpar search function leads to high bounce rates, as frustrated shoppers leave your site when they can’t find what they need; abandoned searches when customers give up rather than refining their queries; and ultimately lost revenue.

How AI-powered search drivers conversions for home24

For home24, a leading European home furnishings retailer, search was a major challenge. Customers struggled to find the right products, and the manual work required to improve search results (e.g. manually implementing redirects to category pages) became unmanageable​.

When they finally switched out of “maintenance mode” and to Constructor’s search solution, they saw:

• Double-digit growth in search conversion rates

• Fewer zero-results searches, thanks to AI-generated synonyms and typo tolerance
  
  
• Improved revenue, as personalized search recommendations increased engagement

By leaning on our machine learning and real-time personalization capabilities, home24 transformed search from a frustration point into a revenue driver​.

The Anatomy of Effective Onsite Search 

While search bars may seem simple, their placement, functionality, and inherent ability to interpret shopper intent directly impact conversions.

Search bar visibility and design

A well-placed search bar encourages shoppers to use it, leading to higher engagement and conversions. The most effective search bars follow these key user experience (UX) principles:

• Consistent placement: Keep the search bar in a familiar, easy-to-locate spot (usually at the top of the page)
  
  
• Mobile optimization: Ensure the search bar is prominent and accessible on small screens
  
  
• Clear icons and auto-focus: A magnifying glass icon signals functionality, while auto-focusing on the search field saves time for users
  
  
• Autocomplete suggestions: Guide shoppers toward relevant results before they even finish typing

Let’s return to Sephora. Sephora integrates personalized autocomplete into its search, instantly suggesting other products based on shopper history and trends. This reduces friction and increases conversions by surfacing high-intent items early in the journey. 

Sephora’s search bar is visually easy to find, intuitive to use, and offers relevant product suggestions for searchers at first type.

Understanding user intent

AI-powered systems interpret context, preferences, and behavioral signals to return personalized, high-converting results.

Key AI technologies for intent-based search include: 

• Natural Language Processing (NLP): Understands conversational queries like "comfortable running shoes under $100"
  
  
• Clickstream Analysis: Tracks what users click, ignore, and purchase to refine future results
  
  
• Real-Time vs. Historical Data: Balances a user’s current session behavior with past shopping history to improve recommendations​

For instance, if a shopper searches for "puppy shampoo for sensitive skin" on an American pet retailer’s site, they should prioritize gentle, veterinarian-recommended options based on user behavior, rather than just showing generic shampoos. 

This American pet retailer uses AI-native search to pick up on 1:1 user behavior, delivering the most attractive results for the query at hand: “puppy shampoo for sensitive skin.” 

Handling common search challenges

Traditional search struggles with imperfect or complicated search queries. Here’s how AI-driven solutions address the most common issues:

1. Typo tolerance and misspellings 

Humans make mistakes, and retail shoppers frequently make spelling errors. A smart search engine should still guide them to attractive results.

For example, the American pet retailer’s search engine automatically corrects misspellings and guides users towards the products they actually want.

Even when searching for a “pet leesh,” this search engine returns attractive results. 

2. Synonym recognition 

Different shoppers use different words for the same product ("sofa" vs. "couch," "sneakers" vs. "running shoes"). AI-powered search recognizes these queries and makes sure they return consistent, relevant results.

3. Long-tail queries 

Long, specific searches often reveal high purchase intent, yet traditional search engines struggle with them. 

AI helps by breaking queries into meaningful parts ("women’s waterproof hiking boots size 8"), matching phrases to relevant product attributes, and learning from real-world clickstream behavior.

While most conversions come from top-performing queries, supporting long-tail searches enhances the full shopping experience​, making it a good idea to optimize for both.

4. Non-product searches 

Shoppers often search for information not related to products, such as "return policy" or "order tracking.” AI-powered search distinguishes between transactional and informational queries, helping users find the right answers without leaving the site.

A German footwear brand enhanced search by integrating FAQ content into results, which helped to reduce customer service inquiries and improve UX.

5. Zero results management 

Shoppers who use search functions are 2.5x more likely to convert — but only if your website ranks products at all. 

A "no results" page shouldn’t be a dead end. Instead, it should suggest alternative products, offer corrected searches, or provide search tips to help refine queries (e.g., "Try different keywords or remove filters").

Not sure which “no results” strategy to land on? A/B test “no results” tactics and pages to track effectiveness. 

When you integrate AI-driven recommendations and typo handling, your brand can turn zero-results pages into conversion opportunities — even when the exact product searched for isn’t available.

Beyond Basic Ecommerce Search: Advanced Features That Drive Revenue 

Beyond simple keyword matching, today’s most effective search solutions enhance user experience, improve product findability, and drive conversions through personalization, automation, and advanced data insights.

Autocomplete and predictive suggestions 

Autocomplete reduces friction in the shopping journey by anticipating a user’s intent before they finish typing. It speeds up the search process, minimizes errors, and nudges shoppers toward high-converting results.

Here are some best practices for effective autocomplete:

• Balance trending and personalized suggestions. AI-powered search should prioritize both popular searches and results tailored to the individual user’s behavior
  
  
• Incorporate visual suggestions. Show product images in the dropdown to enhance engagement
  
  
• Track analytics on suggestion clicks. This is to determine which autocomplete options drive the most conversions

We saw an example of useful autocomplete above in Sephora: real-time autocomplete suggestions based on user interactions to help shoppers discover the right beauty products quickly.

Faceted search and filtering options 

Faceted search helps shoppers to efficiently refine search results with filters like price, category, brand, and product attributes. When implemented effectively, it can significantly improve engagement and conversions.

Dynamic facets vs. static filters:

Dynamic facets differ from static filters in that they adapt based on the product catalog and the context of a user's query. For example, a facet like "inseam length" would only appear in search results when the user is browsing for pants. In contrast, static filters remain consistent across all search experiences, regardless of the query.

For a more seamless filtering system, consider these best practices:

• Collapse non-essential facets by default for a cleaner UI, especially on mobile
  
  
• Use ecommerce AI to optimize facet order based on customer preferences and past engagement
  
  
• Ensure accuracy in product categorization since mislabeled SKUs can cause filtering issues

Birkenstock includes pre-built facet groups and options into their search results. In this case, it pre-selects the black option for the search “black shoes.”

Advanced AI technologies in ecommerce search 

Instead of relying solely on keyword matching to interpret queries and rank results, advanced search platforms use technologies like:

• NLP, which enables the search system to understand complex queries phrased in natural language, such as “I need a lightweight jacket for summer hiking”
  
  
• Transformers and LLMs, which improve search intent detection and enable context-aware ranking of results
  
  
• GenAI, which powers hyperpersonalized search experiences, ensuring that product results align with individual shopper behavior and preferences on a never-before-seen level

Interested in learning more about the emerging AI technologies we’ve been highlighting throughout the blog post? Learn more here.

Hyperpersonalization of search results 

True AI-native search helps you find the right products for the right customer at the right time. Personalizing search results to a user's behavior and preferences helps increase engagement and conversions by surfacing products that match their interests.

Here are some best practices for balancing personalization with merchandising:

• Allow AI to personalize rankings while giving your merchandising team control over strategic priorities
  
  
• Use behavioral data to refine results. Past searches, clicks, and purchases can dynamically influence ranking
  
  
• Monitor performance metrics such as conversion rates and click-through rates (CTRs) in your product search and discovery dashboard to fine-tune personalization models

Let’s see this in action on the American pet retailer’s website. When a shopper that’s primarily searched for cat litter, cat food, and cat toys searches for “food,” cat products surface first, even with such a vague query.

This retailer’s AI-driven search personalizes results by prioritizing pet products based on user behavior, pet breed preferences, and past purchases. So, the first three results under this user’s search for “food” are cat food options, despite dogs being the most popular pet in the United States, because they had previously searched for various cat supplies. 

Measuring and Optimizing Search Performance

Merchandisers can assess how well search is driving conversions and where adjustments are needed by tracking performance metrics. From there, they can fine-tune the search experience and improve product discoverability.

Search metrics every merchandiser should track

Merchandisers should regularly track the following search performance metrics:

• Search usage rate: Measures how often users engage with search, offering insight into how critical search is to the shopping journey
  
  
• CTR on search results: Indicates whether search results are relevant and compelling enough to drive engagement
  
  
• Conversion rate from search: Tracks how often searches lead to purchases, making it one of the most direct indicators of search effectiveness
  
  
• Zero results rate: Measures how often searches return no results, signaling gaps in product data, synonyms, or typo tolerance
  
  
• Search exit rate: Shows how often users abandon the site after searching, which can highlight relevance issues or frustration points

Many of these metrics are trackable within a search analytics dashboard so merchandisers can monitor trends and refine their approach in real time.

Analytics and continuous improvement

A search engine that performs well today may need adjustments tomorrow due to seasonal trends, shifting customer behavior, or updates to product catalogs. Regular analysis helps merchandisers stay ahead of these changes and uncover new opportunities for improvement.

Strategies for continuous search optimization include:

• A/B testing: Compare different search configurations or ranking strategies to reveal what drives better engagement and conversions
  
  
• Using search analytics to inform product and content strategy: Identify top-performing queries and common search terms to stock inventory more effectively and align marketing efforts with customer demand
  
  
• Collaborating cross functionally: Teams across merchandising, IT, and marketing can work together to align search strategy with business objectives
  
  
• Proactively addressing search friction points: High zero-results rates or low CTRs may indicate missing synonyms, outdated filters, or opportunities to refine autocomplete suggestions

Ecommerce businesses should do more than react to customer behavior. Proactive monitoring and analysis supports actively shaping and enhancing the shopping experience.

Challenges Businesses Face When Optimizing Ecommerce Onsite Search

Many ecommerce teams recognize the importance of onsite search, but struggle to optimize it effectively. Common challenges can lead to missed revenue opportunities, poor user experiences, and unnecessary manual work.

Here are some of the most common barriers to effective search optimization: 

• Overrelying on keyword matching: Traditional search engines often fail to understand user intent, returning irrelevant results when queries don’t perfectly match product data
  
  
• Experiencing manual curation that doesn’t scale: Relying on manual rule-setting creates bottlenecks as product catalogs grow. AI-powered search eliminates the need for constant intervention
  
  
• Treating all searches equally: Not all search queries should be ranked the same way. AI-driven personalization ensures that high-intent shoppers see the most attractive results first
  
  
• Suffering from team constraints and limited bandwidth: Merchandising teams often lack the time and resources to manually refine search rules, making ecommerce automation essential
  
  
• Not leveraging search data for broader insights: Search data reveals trends in customer behavior, demand shifts, and inventory needs, yet many businesses fail to integrate these insights into merchandising strategies
  
  
• Ignoring mobile search experience: Search should be optimized for small screens, touch interactions, and voice input, yet many retailers still rely on desktop-first designs
  
  
• Struggling to align across teams: Conflicting priorities between IT and merchandising can stall search improvements. Clear ROI justification and alignment on shared business goals help drive the adoption of AI-powered solutions.

Where Constructor Fits In 

Constructor offers an AI-first approach to onsite search and product discovery, designed to optimize ecommerce performance by aligning with key business metrics.​ Unlike traditional search solutions that require heavy IT involvement, Constructor provides transparent analytics that merchandisers can access without relying on technical teams.

Our suite of tools uses AI and clickstream technology to continuously learn and adapt from every user interaction, offering increasingly attractive product recommendations and search results over time.​

Here are a few real-world results from our clients:

• Bonobos: Achieved a 9% increase in search revenue, a 6% lift in browse conversions, and a 92% increase in recommendations conversions
  
  
• Fisheries Supply: Experienced a 15% increase in revenue per user ​
  
  
• Target Australia: Reported an $13 million revenue increase​

Take the Next Step Toward Smarter Search

A well-optimized search improves the shopping experience by delivering more attractive results, increases conversions and revenue gains, and reduces manual effort.

To see how your current search solution measures up, request a complimentary search audit. Take the first step toward ensuring your ecommerce onsite search experience works for both your customers and your business.

Frequently Asked Questions 

• How does onsite search impact user experience and sales?

A well-optimized onsite search experience reduces friction, improves product discovery, and drives conversions. Shoppers who use search often have high intent to purchase, and when search results are pertinent, they are more likely to complete a transaction. Poor search experiences, on the other hand, lead to site exits, frustration, and lost revenue opportunities.

• How does mobile search differ from desktop search, and what specific optimizations should we make?

Mobile search requires a streamlined, touch-friendly experience since users have smaller screens and often search on the go. Best practices include larger touch targets, prominent search positioning, voice search capabilities, and highly relevant autocomplete to reduce typing. Results pages should load quickly and display key product details without excessive scrolling. Constructor’s AI-powered search adapts to mobile contexts, ensuring seamless functionality across devices.

• How can we integrate our existing product taxonomy and attributes with a new search system?

Most enterprise search solutions allow flexible integration with existing product taxonomies through APIs, feed imports, and database connections. Constructor’s Attribute Enrichment enhances product attributes automatically, improving search accuracy without requiring manual restructuring. A phased approach — starting with your existing taxonomy and refining it based on search analytics — ensures a smooth transition.

• How do we balance personalization with merchandising priorities in search results?

Balancing AI-driven personalization with business goals requires clear merchandising rules that guide product ranking while allowing AI to optimize within set parameters. Constructor’s searchandising features let merchandisers promote specific products, apply time-bound rules, and tailor results for different customer segments. This ensures high-margin items or strategic promotions get visibility, while personalization fine-tunes the rest of the experience.

• What data security and privacy considerations should we be aware of when implementing a new search solution?

A search provider should offer GDPR and CCPA compliance, secure data transmission, and anonymized user identifiers to protect sensitive information. Constructor follows strict security protocols, conducts regular audits, and provides transparent data handling practices to balance personalization with privacy. Businesses should also evaluate data residency policies and encryption standards to ensure compliance with internal security requirements.

• What role does image search play in ecommerce search?

Image search is an emerging technology that enhances product discovery by allowing shoppers to upload an image and find visually similar items in a retailer’s catalog. This is particularly valuable in industries like fashion, home decor, and accessories, where customers may struggle to describe products in words. With Constructor’s AI-powered Image Search, retailers can bridge the gap between offline inspiration and online shopping, improving engagement and conversion rates.

• What is the future of ecommerce onsite search?

The future of onsite search is being shaped by AI, automation, and multimodal discovery. Emerging advancements include conversational commerce powered by AI shopping assistants, visual search capabilities that let shoppers search by image rather than text, and voice search optimization that makes hands-free shopping more intuitive. Additionally, AI-driven personalization is enabling search results to continuously adapt in real time based on user behavior.

As search technology evolves, AI will play an even greater role in understanding shopper intent, automating manual tasks, and delivering highly relevant, revenue-driving results.