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.
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.
AI-powered search uses emerging technologies like:
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.”
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.
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.
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:
By leaning on our machine learning and real-time personalization capabilities, home24 transformed search from a frustration point into a revenue driver.
While search bars may seem simple, their placement, functionality, and inherent ability to interpret shopper intent directly impact conversions.
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:
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.
AI-powered systems interpret context, preferences, and behavioral signals to return personalized, high-converting results.
Key AI technologies for intent-based search include:
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.”
Traditional search struggles with imperfect or complicated search queries. Here’s how AI-driven solutions address the most common issues:
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.
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.
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.
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.
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 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 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:
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 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:
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.”
Instead of relying solely on keyword matching to interpret queries and rank results, advanced search platforms use technologies like:
Interested in learning more about the emerging AI technologies we’ve been highlighting throughout the blog post? Learn more here.
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:
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.
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.
Merchandisers should regularly track the following search performance metrics:
Many of these metrics are trackable within a search analytics dashboard so merchandisers can monitor trends and refine their approach in real time.
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:
Ecommerce businesses should do more than react to customer behavior. Proactive monitoring and analysis supports actively shaping and enhancing the shopping experience.
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:
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:
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.
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.
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.
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.
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.
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.
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.
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.