9 Internal Site Search Best Practices for Enterprise Ecommerce

For ecommerce stores, internal site search is a valuable tool that plays a crucial role in online shopping by helping customers quickly find products, improving user experience, and increasing conversions. While searchers account for only 24% of online shoppers, they drive 44% of ecommerce revenue.

But here’s the challenge: shopper expectations for search continue to rise. Exposure to conversational AI has fundamentally changed how people expect to interact with search interfaces. They want to express complex needs in natural language, receive contextually aware (not just relevant) suggestions, and get immediate results. A search experience that once felt acceptable now feels frustratingly rigid.

Retailers who roll up their sleeves and implement the new age of internal site search best practices will be the ones who turn their search experience into a critical brand touchpoint in an era where product catalogs are increasingly similar.

If you’re an ecommerce leader who needs to turn your site into a competitive advantage into the new year, this guide will help. Discover how critically analyzing your current search experience can help you accelerate buyer journeys, enhance merchandising efficiency, reduce customer support load, and position yourself for future technologies such as AI agent compatibility.

The Foundation: Understanding Search Intent

Search intent refers to the underlying goal or purpose a user has when entering a query into your internal site search. Understanding search intent is essential for delivering results that truly satisfy the shopper’s needs, not just matching keywords.

Beyond keywords: Why relevance isn't enough

For decades, ecommerce search has operated on a simple premise: match keywords in a query to product attributes in your catalog. Type “blue jeans,” see products labeled “blue jeans.” This keyword-based approach made sense when the primary challenge was simply finding products that matched search terms. However, to truly enhance user experience and address user intent, it is crucial for internal site search to provide relevant suggestions that go beyond simple keyword matches and answer the question that actually matters:

What does this shopper want to buy at this specific moment in time?

Defining attractiveness and its relationship to relevance

This is where we go beyond relevance and to the concept of attractiveness. While relevance measures how well a result matches keywords in a search query, attractiveness measures something far more valuable: how likely a product is to convert for a specific shopper. In other words, attractiveness builds on relevance by considering not just if a product matches the query, but if it is appealing enough to drive a purchase.

Research shows that more attractive results generate click-through rates that are nearly twice as high as less attractive results, even when all results are technically “relevant” to the search query.

The distinction matters because it transforms search from a matching exercise into a conversion engine. For every 1-point increase in attractiveness score, click-through rates improve by 3.8%. This predictable relationship provides a reliable lever for improving search performance — and ultimately, revenue.

How modern search understands intent

Modern search engines use multiple layers of intelligence to understand what shoppers actually want.

Semantic understanding

Semantic understanding is the ability of a search engine to interpret the meaning behind a query, rather than just matching keywords. Natural language processing and transformer models — the “t” in ChatGPT — enable search to understand semantic meaning. For example, when someone searches for “comfortable shoes for standing all day,” the engine understands this describes a need (all-day comfort) that could map to running sneakers, cushioned flats, or orthopedic insoles, depending on the individual shopper. An effective internal search engine interprets user queries to deliver more personalized, relevant results, enhancing the overall user experience.

Clickstream data

Full, verified clickstream data adds another dimension of understanding. By analyzing what shoppers actually click on and purchase after certain queries, search engines learn the true intent behind searches. If most people who search “peppers” on a grocery site click on bell peppers rather than black pepper, the engine learns to prioritize bell peppers — even though both technically match the keyword.

For a new visitor, Pick n Pay returns bell peppers alongside other bell pepper-inspired and related food items for the query “peppers.” This is due to conversions derived from collective clickstream data, which determines product rankings regardless of a user’s behavior history (or lack thereof).

Contextual signals

Context awareness also brings real-time intelligence to search. Modern search considers a variety of factors, including:

• Location: showing winter coats to shoppers in cold climates
• Device: optimizing results for mobile vs. desktop
• Time of day: highlighting quick-ship options for evening shoppers
• Shopping history: surfacing preferred brands and styles
• Current session behavior: understanding what they’ve viewed or added to cart

These contextual signals help search engines surface products that fit not just the query, but the shopper’s current situation and preferences.

AI agents and Agentic AI

AI agents, such as the AI Shopping Agent (ASA), further extend this understanding. Rather than forcing shoppers to break down complex needs into simple keyword searches, ASA can handle conversational queries like, “What hiking backpack should I buy for a week-long hike?” or “Show me Euro summer-inspired outfits for my trip to Italy.” By understanding the full context of what shoppers need — not just what they type — ASA transforms vague inspiration into structured product exploration.

Agentic AI refers to artificial intelligence systems that can act autonomously on behalf of users, engaging in multi-step reasoning and decision-making to guide shoppers through complex discovery journeys. Agentic AI builds on conversational search by not only understanding natural language but also taking proactive steps to clarify intent, ask follow-up questions, and curate personalized recommendations.

Balancing rankings with business goals

Here’s a truth that may surprise retailers: search optimization isn’t just about suggesting the right products at the right time. It’s about showing the right products at the right time that drive your specific business KPIs.

We discussed relevance earlier, the metric that traditional search engines optimize for (essentially, how well results match the search query).

But ecommerce isn’t academic research. It’s a revenue-driven business with specific goals. You might need to optimize for conversion rate, revenue, profit margin, or inventory velocity. These business metrics often require different product rankings than those suggested by pure relevance. Optimizing internal site search can directly increase search revenue by aligning product rankings with business goals, ensuring that the most profitable or strategic products are surfaced to users.

Consider a premium fashion retailer. From a relevance perspective, showing sale items prominently makes sense. After all, they match the search and convert well. But the merchandising team might choose to bury those sale items to maintain the brand’s premium positioning, even if it means some short-term conversion loss. That’s a strategic business decision that relevance algorithms can’t make.

Or imagine an electronics retailer with excess inventory of a particular laptop model. Boosting that product in search results for “laptop” helps clear inventory and improve cash flow, even if other models might be technically more relevant to some shoppers. The search experience becomes a strategic tool for managing business operations, not just a product-finding utility.

The art and science of modern search lies in this balance. AI handles the complex work of understanding intent, personalizing results, and managing thousands of products across millions of queries. But merchant expertise guides the strategy. It’s merchants who know which business goals matter most, when to prioritize brand positioning over immediate conversion, and how to align search with broader company objectives.

With a solid understanding of search intent and the foundational concepts, let’s dive into the top nine internal site search best practices you need to prepare yourself for where the overarching ecommerce industry is heading.

Best Practice #1: Prioritize Real-Time Personalization and Context-Aware Ranking

True personalization in search has transformed from segmenting shoppers into broad categories to dynamically ranking products based on individual signals. It happens in milliseconds and adapts to each shopper’s unique preferences and current context.

Behavioral data

The foundation of effective personalization is behavioral data. When a shopper clicks on products, views certain categories, or adds items to their cart, they’re revealing preferences. For returning customers, purchase history provides even richer signals — preferred brands, typical price ranges, style affinities, and even seasonal shopping patterns.

A robust site search solution enables real-time personalization and context-aware ranking for ecommerce sites, ensuring that search results are tailored to each user's intent and behavior as they interact with the site.

Session context

But personalization can’t rely solely on historical data. Session context matters just as much. What has this shopper searched for in the current visit? Which categories have they browsed? What filters have they applied?

Dynamic signals

That’s why modern search engines layer on dynamic signals that change in real time. Some of them include:

• Inventory status: Showing products that are actually available rather than out-of-stock items that frustrate shoppers matters
  
  
• Time of day: This provides clues about urgency, as evening shoppers might prioritize quick-ship options
  
  
• Location: This influences product selection (i.e., showing weather-appropriate clothing based on local climate)
  
  
• Ongoing promotions: Create timely opportunities, like highlighting items on sale when shoppers search relevant categories

Personalization in action

The power of this approach becomes clear in specific examples. Two shoppers search for “running shoes” on the same site. Because the first shopper has previously browsed hiking gear and outdoor equipment, their results prioritize trail running shoes with aggressive tread patterns. The second shopper’s browsing history indicates an interest in urban fitness and road running content. So, they see lightweight road running shoes designed for pavement.

Same query, dramatically different needs, appropriately different results.

With personalization as a foundation, the next step is to optimize how users begin their search journey with autocomplete.

Best Practice #2: Optimize Autocomplete, Where Your Search Experience Starts

Autocomplete is often the first — and sometimes only — interaction a shopper has with your search system. Before they finish typing, before they see results, autocomplete shapes their expectations and guides their journey.

A well-designed search tool with advanced autocomplete features can significantly improve the user experience and drive conversions. Done well, it accelerates product discovery and boosts conversion. Done poorly, it creates friction and drives abandonment.

The basics matter when it comes to making your autocomplete experience memorable:

Speed and relevance

Autocomplete must be instant, with suggestions appearing without perceptible delay. But speed alone isn’t enough. Autocomplete needs to be genuinely helpful, offering suggestions that move shoppers closer to products they want to buy.

Intent-based suggestions

Intent-based suggestions go beyond simple product names or past queries. When someone types “office,” showing “office chairs,” “office desks,” and “office supplies” is functional. Showing “office clothes for summer” or “office-appropriate shoes” demonstrates an understanding of what people actually search for and the context behind those searches.

Visual cues

Consider how visual cues transform autocomplete from a text-based navigation tool into a rich preview of the shopping experience:

• Product thumbnails, which help shoppers confirm they’re on the right track before clicking
• Price indicators that let budget-conscious shoppers filter options quickly
• Badges that highlight sale items, bestsellers, or low stock create urgency and guide decisions

Target AU suggests a variety of categories for the query “office,” including product thumbnails to further guide shoppers along their journeys.

Category suggestions

Also consider how category suggestions baked into autocomplete can serve a different, equally beneficial purpose: helping shoppers who aren’t sure exactly what they’re looking for. Someone searching “gifts” might benefit from seeing “Gifts for Her,” “Gifts Under $50,” or “Last-Minute Gifts” as category options, each leading to a curated collection rather than a generic search results page.

Mobile friendliness and voice search

When optimizing for autocomplete, ensure mobile friendliness as well, in the shape of touch-friendly design and voice search readiness. (We dive more into mobile-first search design later!)

AI-powered autocomplete

Last but not least, AI enhances autocomplete by understanding natural language and predicting complete thoughts rather than just keywords. Traditional autocomplete might suggest “women’s shoes” when someone types “women’s s.” AI-powered autocomplete might suggest “women’s shoes for wide feet” or “women’s shoes comfortable for walking” — complete phrases that reflect how people actually search and speak.

Our AI Shopping Agent (ASA) takes this further by powering autocomplete with conversational suggestions. Instead of forcing shoppers to know the right keywords, it can interpret vague needs and suggest specific product directions. Someone starting to type “what should I wear to…” might see suggestions like “what should I wear to an outdoor wedding” or “what should I wear to a job interview in summer." These are suggestions that traditional search struggles to handle but conversational AI can address naturally.

ASA can accelerate the product discovery journey by suggesting a wide range of ideas for the search term "office."

With autocomplete optimized, the next challenge is ensuring that even difficult or unusual queries are handled gracefully.

Best Practice #3: Gracefully Handle "No Results" and Long-Tail Queries

Zero result searches, or “no results found,” is one of the most expensive pages in ecommerce. When shoppers encounter it, they face an immediate choice: try searching again, or leave. Many choose the latter.

Traditional approaches to zero results include “Did you mean” suggestions that correct obvious typos, or automatically relaxing search constraints to show related results. These help in some cases, but they don’t address the fundamental problem: the shopper wants something specific that your catalog doesn’t contain, or they’ve expressed their need in a way your search engine can’t interpret.

Modern AI-powered search takes a more sophisticated approach via several ways.

Semantic understanding

Semantic understanding allows search engines to surface related products even without exact keyword matches. If someone searches for “summer dress with pockets,” but your products aren’t tagged with “pockets,” semantic search can still identify dresses based on image analysis and product descriptions that mention functional features.

This approach ensures that shoppers receive relevant results, even when their queries do not exactly match product tags, improving user satisfaction and increasing the likelihood of conversions.

This popular UK fashion retailer’s search engine returns attractive results for the query “summer dress with pockets,” avoiding potential loss of conversions.

AI Agents

AI agents like ASA excel at handling zero-results scenarios through conversational guidance. Rather than showing a dead end, ASA can explain why products don’t exist and suggest attractive alternatives. For example: “We don’t currently carry men’s paisley shirts. But we do have other prints that might catch your eye. Check out our floral and vintage patterns.”

Additionally, ASA can display related products or results based on the user's original query, helping to keep users engaged even when exact matches are unavailable. This encourages continual product exploration, demonstrates helpfulness, and introduces shoppers to products they might not have considered.

Synonym understanding

Synonym handling addresses a different challenge: shoppers use different search terms to describe the same products. “Sneakers,” “tennis shoes,” and “trainers” all refer to athletic footwear, but if your products are only tagged “sneakers,” shoppers using other terms hit dead ends.

Modern search engines automatically learn these synonyms from clickstream data. When people search “trainers” and click on products labeled “sneakers,” the system learns the terms are equivalent.

Query reformulation

Query reformulation helps when searches are too broad or too narrow. Someone searching just “shoes” needs guidance, in the shape of suggesting categories or facets like “toddler shoes,” “basketball shoes,” or “nike shoes.” (Facets are filters that allow users to narrow down search results by attributes such as size, color, or brand.) Filters and sorting options also help users narrow down search results, especially on content-heavy websites. Conversely, someone searching “men’s size 10 red leather high-top basketball shoes” might need help relaxing constraints if that exact combination doesn’t exist.

This famous US footwear retailer offers a variety of categories to kickstart searches for the query “shoes.”

The goal isn’t to always return results, but to always provide a path forward. Even when you genuinely don’t carry what someone wants, helping them understand why and suggesting relevant alternatives maintains trust and keeps them engaged with your brand.

With robust handling of no-results and long-tail queries, the next focus is on optimizing the overall search user experience.

Best Practice #4: Consider Search UX to Drive Engagement

The best search technology in the world can’t overcome poor user experience (UX). An intuitive site interface — with proper visual elements and interaction patterns — enhances user engagement and satisfaction by making it easier for visitors to find what they need via your site search.

Search results pages should be visually rich, not just text lists. Here are some visual elements to consider:

• Product thumbnails: They help shoppers quickly scan options and make decisions based on visual appeal. Clear pricing information — including regular price, sale price, and percentage discounts — lets budget-conscious shoppers filter efficiently
  
  
• Facets and filters: These need to be smart, not static. When someone searches for “blazer,” display facets such as “fit,” “material,” and “formality level.” When they search “pants,” show “inseam,” “rise,” and “leg opening.” Dynamic faceting based on query context makes filtering feel intuitive rather than overwhelming. (Also, optimizing category pages with personalized search results and dynamic filters can significantly improve product discovery and user engagement.)
  
  
• Intent clarification elements: These help shoppers narrow their focus without starting over. Category tags or inline filter options like “Did you mean: Women’s Blazers | Men’s Blazers | Kids’ Blazers” let shoppers quickly correct when search returns mixed results. This prevents the frustration of seeing irrelevant products mixed with what they actually want
  
  
• Status indicators: These create urgency and set expectations. Badges highlighting “bestseller,” “low stock,” “new arrival,” or “on sale” help shoppers prioritize which products to explore first. Clear availability messaging — “In stock,” “Ships in 2-3 days,” or “Low stock: only 3 left” — reduces uncertainty and prevents disappointment. [Our Machine Learning (ML) Ranking team found that they help shoppers make faster decisions.]
  
  
• Visual hierarchy: This guides attention to the most important elements. High-attractiveness products should be visually prominent, while lower-priority items can be smaller or less emphasized. This doesn’t mean hiding less attractive options entirely, but rather helping shoppers navigate toward products they’re more likely to want

This US retailer for healthcare apparel includes facets like brand, collection, product type, style, fit, color, length, and more for the query “women’s pants,” making it easier and quicker for visitors to filter through their options.

Mobile-first search design

With mobile devices driving an increasing share of ecommerce traffic, search must work flawlessly on small screens. A mobile-optimized online store ensures a seamless experience for shoppers on any device.

• Search bar: It needs to be prominent and easily accessible, preferably sticky so it remains available as shoppers scroll. Touch-friendly design ensures autocomplete suggestions and filters are large enough to tap accurately without zooming
  
  
• Mobile-optimized loading: This is critical, especially for autocomplete and initial results. Mobile networks are often slower than broadband, making speed optimization even more important. Opt for progressive loading (i.e., showing some results quickly while others load in the background) to maintain the perception of speed even when network conditions aren’t ideal
  
  
• Simplified navigation: While desktop can show dozens of facets and filters, mobile needs to prioritize the most useful options and make others accessible through progressive disclosure. The goal is to reduce cognitive load and minimize scrolling while still providing sophisticated filtering capabilities
  
  
• Voice search enabled: Many mobile shoppers use voice assistants to search while driving, cooking, or in other situations where typing isn’t convenient. Search engines need to handle the conversational, long-form queries that voice dictation produces (“find me a blue cocktail dress under two hundred dollars that ships by Friday”) rather than expecting terse keyword phrases

Speed and performance

Search latency kills conversions. Research consistently shows that every 100 milliseconds of delay impacts revenue. Shoppers expect instant responses. Anything slower than 300 milliseconds feels sluggish, and delays of more than a couple seconds cause noticeable frustration.

Technical optimization requires attention to multiple layers, including:

• Efficient indexing, which ensures the search engine can retrieve relevant products quickly
  
  
• Caching frequently-accessed data to reduce database load
  
  
• CDN distribution to put search infrastructure geographically close to shoppers, minimizing network latency

It’s also important to remember that the perception of speed matters as much as actual speed. Offer instant autocomplete to create the feeling of responsiveness even before shoppers see full results. Skeleton screens — placeholder content that shows where results will appear — also make wait times feel shorter by giving shoppers something to look at while content loads.

Also, search infrastructure needs to scale elastically, automatically adding capacity during high-demand periods without degrading performance. Tracking search volume is crucial for anticipating and managing spikes in search activity, ensuring your infrastructure can handle increased demand. Load handling ensures performance remains consistent during periods of holiday shopping (like the most recent BFCM period), flash sales, and social virality.

Accessibility

Last but not least, meeting user expectations for accessibility ensures that all shoppers, including those with assistive technologies, can effectively use the internal site search. Keep the following in mind when optimizing your site for accessibility:

• Keyboard navigation allows people who can’t use a mouse to interact with autocomplete, filters, and results
  
  
• Screen reader compatibility ensures blind shoppers can understand search results and product information
  
  
• Clear focus states show which element is currently selected during keyboard navigation
  
  
• High contrast improves readability for shoppers with visual impairments
  
  
• Text needs to be large enough and have sufficient color contrast against background
  
  
• Important controls and calls-to-action should be visually distinct, not relying solely on color to convey meaning (which causes problems for colorblind users)

And don’t forget semantic HTML, using proper heading tags, lists, and form elements to help assistive technologies understand the structure and purpose of search interfaces. This technical foundation is invisible to most users but critical for those who depend on it.

With a strong search UX in place, the next step is to balance AI automation with human expertise through merchant controls.

Best Practice #5: Balance AI with Human Expertise via Key Merchant Controls

AI-powered search is remarkably effective at processing millions of products and personalizing results for individual shoppers. But AI doesn’t have intuition, business context, or strategic vision. The most effective search experiences combine AI’s computational power with human expertise.

This is the essence of searchandising: using technology to power the product discovery experience while giving merchants meaningful control over how products appear and which business priorities take precedence. Additionally, insights gained from internal site search can be leveraged to inform and enhance your overall marketing strategy, helping you better understand customer behavior and optimize your on- and offsite marketing efforts.

Choose a site search tool that provides robust merchant controls and customization options, ensuring you can tailor the internal site search experience to your business needs:

• Boosting and burying: Give merchants the power to manually promote or demote specific products
  
  
• Slotting: Pin specific products to specific positions in search results
  
  
• Attribute-based slotting: Slot by attributes, such as brand or price, for more flexible control
  
  
• Blocklisting: Hide products from results without removing them from your catalog
  
  
• Facet management: Control which filters (facets) appear, in what order, and how they’re labeled. (Facets are filters that allow users to narrow down search results by attributes such as size, color, or brand.)
  
  
• AI-generated rules: Use AI to suggest merchandising actions, with merchants reviewing and approving these suggestions

How to strike a balance

Well-designed site search combines advanced AI capabilities with merchant expertise, ensuring optimal product discovery, user satisfaction, and business outcomes.

The most effective approach uses AI for the mundane, heavy lifting: handling typo tolerance, detecting synonyms, maintaining base relevance, and personalizing results for millions of shopper-product combinations. This frees merchants to focus on strategic decisions that require business context: maintaining brand positioning, managing inventory strategically, executing promotional strategies, and responding to competitive dynamics.

Human override mechanisms ensure merchants can always step in when AI recommendations don’t align with business strategy. If the AI wants to promote a high-margin product but you’re trying to clear overstock of a different item, merchant rules take precedence. If personalization would create a poor brand experience (like showing only discount items to price-sensitive shoppers), you can enforce quality standards.

Again, let's circle back to the example of a premium fashion retailer. Imagine their AI detects that sale items convert extremely well and decides to rank them highly. The merchandising team can step in and choose to bury those sale items in search results to maintain the brand’s luxury positioning, even though it means some short-term conversion loss. That strategic decision — prioritizing long-term brand perception over immediate revenue — is exactly the kind of judgment AI can’t make.

With merchant controls in place, it’s important to ensure that your search experience is also trustworthy, transparent, and ethically personalized.

Best Practice #6: Consider Trust, Transparency, and Ethical Personalization

As search becomes more personalized and AI-driven, transparency becomes essential for maintaining shopper trust. People want to understand why they’re seeing certain results, and retailers need to maintain ethical standards in how they personalize experiences. This not only builds trust, but also leads to higher customer satisfaction and loyalty by ensuring shoppers feel respected and understood.

Shoppers increasingly expect to know why they’re being shown specific products. Simple explanatory text — “Recommended based on your recent searches for hiking gear” or “Popular with customers who bought running shoes” — provides context that makes personalization feel helpful rather than invasive.

Merchandisers also need visibility into AI decisions. When the AI ranks a product highly, merchants should understand why: “This product has a 15% conversion rate among shoppers with similar browsing history” or “This item performs well with your demographic segment.” This transparency allows merchants to validate that the AI is making sensible decisions and override when necessary. When paired with site search data analysis, it helps merchandisers understand and validate these AI-driven recommendations by revealing which queries and behaviors are influencing the results.

For this milk product that had no prior clickstream data, Constructor's base score played the biggest factor in its ranking.

Your site's search engine should clearly differentiate between organic and paid placements to maintain user trust. Mixing sponsored and organic results without clear differentiation erodes trust and can create legal liability.

home24, a leading home living ecommerce company, uses Constructor’s Retail Media Suite to return attractive ads that complement (don’t compete with) organic rankings.

Also keep in mind privacy preferences. This means honoring shoppers’ choices about data collection and personalization. Some shoppers are comfortable with extensive personalization; others prefer minimal data collection.

Safe defaults when personalization isn't possible

A strong internal search function can keep visitors engaged by providing relevant results. This is even possible in cases where 1:1 personalization can't be applied, due to shoppers not having enough history (in the case of new visitors, shoppers who've cleared cookies, those browsing in incognito mode, etc.) — as long as search has graceful fallbacks.

One of those fallbacks is known as group attractiveness, a core element to Constructor’s product offering. It’s based on the overarching principle of ranking products according to conversions garnered from collective clickstream data.

There are a couple of ways group attractiveness works and scores are calculated.

• It picks up on current trends. Group attractiveness algorithms decipher which product attributes are performing well given the search query and other context signals. Then, they boost products with those attributes in results sets in real-time
  
  
• Group attractiveness algorithms also rank products according to your ecommerce site’s historically best-performing items. For example, if someone with no prior history on the site searches for “t-shirt,” those with the most best-performing attributes (i.e., long-sleeve, v-neck, cotton) will surface among the top in search results pages

The key is avoiding the opposite extreme: showing products that don’t have strong ranking signals nor are backed by strategic business decisions (which can lead to experiences that feel disconnected).

Governance frameworks

Clear governance ensures that site search functionality remains aligned with business goals and user needs. It defines who can modify search rules and under what circumstances.

In smaller organizations, this might be simple: the merchandising manager approves all site search changes. In larger enterprises, governance gets more complex: category managers control their domains, but changes affecting multiple categories need director-level approval.

Approval workflows ensure changes are reviewed before going live. Major algorithm updates, new personalization strategies, or significant rule changes should go through testing and sign-off processes. This prevents well-intentioned but poorly-executed changes from harming the shopper experience or business metrics.

Audit trails maintain a record of who changed what and when. This isn’t just about compliance (though regulations often require it); it’s about learning from experience. When a search optimization drives great results, you want to know what changed so you can replicate the approach. When results decline, audit trails help diagnose the cause.

The goal isn’t to slow down optimization with bureaucracy, but to ensure changes are thoughtful, tested, and aligned with business strategy. The best governance frameworks balance speed with safety, allowing rapid iteration while preventing catastrophic mistakes.

With trust and transparency established, the next best practice is to monitor and analyze your search performance for continuous improvement.

Best Practice #7: Monitor Search Analytics [+ Key Metrics to Track]

Monitoring search trends and site search analytics provides valuable insights into user behavior, helping you identify what visitors are seeking, how they interact with your site, and where improvements can be made.

This analysis not only optimizes your content and product offerings but also enhances customer satisfaction by ensuring your internal search system adapts to evolving needs and delivers a more seamless experience.

Here are some key metrics to track:

• Search conversion rate: Tells you what percentage of searches lead to purchases, is your north star metric. It’s the clearest indicator of whether search is helping or hindering sales. Track this overall and by query category to identify which types of searches perform well and which need improvement
  
  
• Zero-results rate: Measures how often shoppers search for something your catalog doesn’t contain — or at least, something your search engine can’t find. A high zero-results rate indicates gaps in your catalog, poor synonym handling, or ineffective semantic understanding
  
  
• Query reformulation rate: Shows how often shoppers search multiple times in succession, typically because their first search didn’t return what they wanted. High reformulation rates suggest your search isn’t understanding intent or your autocomplete isn’t guiding shoppers effectively
  
  
• Search exit rate: Captures how many shoppers leave your site directly from search results. High exit rates indicate that search isn’t showing attractive products or that the results page experience itself has friction
  
  
• Popular queries and trending searches: Reveal what shoppers are looking for right now. This informs merchandising decisions (which products to promote), content strategy (which landing pages to create), and even purchasing decisions (which products to add to your catalog)
  
  
• Mobile search bounce rates: Specifically track how mobile search performs, given the unique constraints and behaviors of mobile shoppers. If mobile bounce rates are significantly higher than desktop, your mobile search experience needs attention
  
  
• Impact of merchandising changes on KPIs: Measures whether your boost/bury rules, promotional strategies, and other interventions actually improve results. It’s easy to make changes that feel right but don’t actually drive business outcomes. Measuring impact keeps optimization data-driven

Search analytics in action

Monitoring query patterns reveals opportunities:

• What are shoppers searching for that you don’t carry? These are potential gaps in your catalog.
• What seasonal trends are emerging early? Get ahead by promoting relevant products before competitors notice the trend.
• What queries generate high traffic but low conversion? These are prime candidates for optimization.

Identifying zero-results opportunities requires investigating which queries return nothing. Sometimes this reveals catalog gaps — shoppers want products you don’t carry. Other times it reveals tagging or synonym problems (as in, the products exist but aren’t findable). Each scenario requires a different solution.

Likewise, tracking reformulations shows where search is failing. When shoppers search for “running shoes,” then immediately search again for “womens running shoes,” your autocomplete or initial results page failed to guide them effectively. Patterns in reformulations reveal systematic problems that need fixing.

Instead of tracking all this data manually, your merchandising team can surface opportunities and make data-driven decisions automatically using search intelligence tools. Automated alerts can flag underperforming queries (“This search has high volume but 50% lower conversion than average”), identify seasonal trends as they emerge (“Searches for ‘patio furniture’ are up 30% week-over-week”), or highlight products that should be boosted (“This item has a 20% conversion rate but appears in position 10 on average”).

Merchandisers have the opportunity to track zero result searches in the Constructor dashboard and searchandise accordingly.

A/B testing search improvements

Intuition isn’t enough to validate search changes. A/B testing one variable at a time provides empirical evidence of what’s driving results (and what isn’t).

To run your experiments with proper statistical controls that provide clearer insights, partner with the Data Science team of a search and product discovery vendor that offers complimentary A/B testing. This will allow you to not only have the ability to effectively measure impact on conversions, revenue, and engagement metrics, but also avoid putting more work on your merchandisers’ plates.

With analytics in place, it’s time to look toward the future of search, including conversational and agentic AI.

Best Practice #8: Look Toward the Future, with Conversational and Agentic Search

The line between “searching” and “shopping” is blurring as interfaces become more fluid, contextual, and conversational. As search evolves, having a good internal site search is essential for ecommerce websites to deliver the best search results in a given context, enhance user experience, and maintain a competitive edge.

This is where conversational search and other future-forward technologies shine.

Conversational Search

Traditional search assumes shoppers know what they want and can express it in a few keywords. But many shopping missions don’t start that way. Shoppers often have vague, situational needs: “show outfits for an outdoor wedding in 90 degree weather” or “What should I pack for a week-long backpacking trip?”

These complex, context-rich queries don’t fit neatly into keyword search boxes.

Conversational search handles these queries naturally. Instead of forcing shoppers to break down their needs into searchable fragments, it accepts natural language input and engages in dialogue to understand intent. This isn’t just about parsing longer queries. It’s about dynamic interaction that clarifies needs, asks follow-up questions, and guides shoppers toward products that truly fit.

Agentic AI for product discovery: AI Shopping Agent (ASA)

Constructor’s AI Shopping Agent (ASA) represents the evolution of search for these complex discovery scenarios. Unlike traditional search, which returns a static list of products, ASA can be embedded as a “search bar mode” that provides conversational guidance throughout the shopping journey.

Agentic AI refers to artificial intelligence systems that can act autonomously on behalf of users, engaging in multi-step reasoning and decision-making to guide shoppers through complex discovery journeys. Agentic AI builds on conversational search by not only understanding natural language but also taking proactive steps to clarify intent, ask follow-up questions, and curate personalized recommendations.

When someone searches “What hiking backpack should I buy for a week-long hike,” ASA doesn’t just show all backpacks. It asks clarifying questions: What’s your torso length? Will you be camping or staying in huts? How much gear do you typically carry? Based on the answers, it narrows options to backpacks that genuinely fit the shopper’s specific situation — considering their physical dimensions, trip type, and gear load.

For shoppers with aspirational but vague intent — “Show me Euro summer-inspired outfits for my trip to Italy” — ASA can curate complete looks based on current trends, the shopper’s style preferences from their browsing history, and practical considerations like weather and activities. It handles complex styling requests that traditional search simply can’t process.

Recipe generation for grocery retailers demonstrates another powerful use case. Instead of searching for individual ingredients, shoppers can request “gluten-free blueberry muffin recipe” and receive not just the recipe but personalized ingredient recommendations based on their brand preferences and dietary restrictions. They can add all items to their cart in one action, transforming meal planning from a multi-step research project into a streamlined shopping experience.

Agentic AI for decision making: AI Product Insights Agent (PIA)

Constructor’s other proprietary AI agent, AI Product Insights Agent (PIA), addresses a different part of the journey: the moment of decision on product detail pages (PDPs). (A PDP, or Product Detail Page, is the page on an ecommerce site that provides detailed information about a specific product.) When shoppers reach a PDP, they often have specific questions: “Does this fit true to size?” “Is it in stock in my region?” “Will this work with my existing equipment?” Traditional PDPs display static information that might — or might not — answer these questions.

PIA acts as an on-page assistant that answers these questions conversationally, pulling from product metadata, customer reviews, and inventory data. It reduces the friction that causes shoppers to bounce from PDPs to search for answers elsewhere. By providing immediate, relevant answers at the point of decision, it increases confidence and drives conversion.

Constructor’s Ecommerce Large Reasoning Model

The technology underlying these experiences is Constructor’s Ecommerce Large Reasoning Model, a specialized AI model trained on petabytes of ecommerce journey data and every interaction across the site. This model is designed to understand shopping behavior, product relationships, and conversion patterns in ways that general-purpose language models cannot, making it highly relevant for powering advanced search and discovery experiences.

Agentic AI is the future

Looking forward, retailers need to think about future-readiness. As AI agents become more common in shopping — both on-site assistants like ASA and PIA, and external agents like ChatGPT’s shopping features — ecommerce infrastructure needs to support agent interactions. This means ensuring product data is structured, updated in real-time, and accessible in ways that agents can consume and act on.

Composable commerce architectures (a modular approach to building ecommerce systems where components can be swapped or updated independently) and headless implementations provide flexibility for deploying AI-powered discovery across multiple touchpoints. As the distinction between “website,” “app,” and “conversational interface” continues to blur, retailers need infrastructure that can power discovery experiences regardless of where they happen.

Investing in AI-native search infrastructure now positions retailers for whatever comes next. Shoppers currently experimenting with conversational AI for product research will soon expect to find those capabilities on your site. The question isn’t whether agentic and conversational search will become mainstream, but how quickly… and whether your search experience will keep pace.

With the future in mind, the final best practice is to embrace visual search for even more intuitive product discovery.

Best Practice #9: Embrace Visual Search in Enterprise Ecommerce

For sites with vast and diverse product catalogs, visual search is a must-have.

Visual search allows users to upload a photo — whether it’s a screenshot from social media, a picture of a friend’s outfit, or an image from a(n online) magazine — and instantly receive relevant search results featuring similar or matching products from your catalog, thus bridging the gap between inspiration and discovery.

How visual search works

This technology uses advanced image recognition and machine learning to analyze visual attributes such as color, shape, pattern, and even finer details like fabric or embellishments. This allows your internal site search engine to surface the most appropriate products, even when users can’t articulate their needs in words.

This not only enhances the shopping experience but also increases conversion rates, as shoppers are presented with options that closely match their visual intent.

The Future Is Yours For the Taking. Here's Where to Begin

Internal site search has evolved from simple keyword matching into a sophisticated, AI-powered discovery experience that drives revenue and builds trust.

The best search experiences combine multiple elements: real-time personalization that understands individual shopper context, conversational capabilities that handle complex queries naturally, merchant controls that balance AI intelligence with human expertise, and ethical governance that maintains transparency and respects privacy.

As on-site search features become more advanced, their role in driving business outcomes — such as higher conversion rates, increased user engagement, and improved site performance — has become critical.

Here are some quick steps you can take to ensure you're starting off on the right foot this year: 

1. Audit your current search performance against the best practices outlined in this guide.
2. Identify quick wins such as enhancing autocomplete with visual elements, handling “no results” more gracefully, or implementing basic personalization.
3. Build toward more sophisticated capabilities: real-time context-aware ranking, conversational search for complex queries, and predictive analytics that surface optimization opportunities automatically.

The retailers who transform search from a basic utility into a strategic advantage will capture more revenue from their most valuable shoppers, build stronger customer relationships through helpful guidance, and future-proof their business as commerce continues its evolution toward more intelligent, conversational experiences.