Search is the backbone of the ecommerce experience, yet it remains a persistent challenge, often delivering frustratingly irrelevant results. Search is supposed to be intuitive: type in what you need, get the right results — simple, right? But in reality, relevance is highly subjective. A B2B buyer looking for industrial sealant and a B2C shopper searching for a winter coat may have completely different needs, but they share one universal truth: if they can’t find it, they can’t buy it.
At Coveo’s recent Relevance 360 virtual event, AI experts explored how AI-powered search and product discovery can finally solve the relevance equation — balancing precision, personalization, and profitability to optimize both customer experience and business outcomes.
Here’s what you need to know.
Relevance is Subjective
Relevance is the foundation of ecommerce success, ensuring customers find the right content, product, or information at the right time. At its core, relevance is the ability to align with user intent, transforming search interactions into profitable conversions.
But relevance isn’t universal. Two people can type the same search query and expect completely different results. “Relevance isn’t one-size-fits-all,” said Peter Curran, GM of Commerce at Coveo. “What a shopper considers relevant might not align with what a search engine returns, and that’s where AI makes the difference.”
Consider this: You search for Stanley Cup during hockey season. Are you looking for the championship game or the popular drink tumbler? The answer depends on the user, but search engines often can’t tell the difference. Traditional keyword-based search treats them the same, failing to understand context and leading to frustrating, irrelevant results. The same challenge plays out in both B2C and B2B e-commerce, where search engines misinterpret intent, leaving customers without the answers they need— and businesses without the sales they should have had.
Great search doesn’t just match words — it understands meaning. That’s where AI-driven relevance steps in, bridging the gap between what customers ask for and what they truly want.
When Search Fails, Sales Suffer: The Cost of Poor Relevance
Peter illustrates a critical ecommerce challenge: when search misfires, shoppers disappear. Traditional search engines often over-recall (returning too many irrelevant results) or completely miss the mark, leading to frustration and lost sales.
He shares several real-world examples where search engines fail, whether by surfacing irrelevant products, failing to recognize user intent, or making it difficult for shoppers to refine their results. When search doesn’t deliver, customers assume the retailer doesn’t carry what they need and take their business elsewhere. Worse, they may find the right product through a competitor’s paid ad. Our recent Commerce Relevance Report recently showed that 72% of shoppers will quickly abandon an ecommerce site if search doesn’t yield the results they’re looking for.
Retailers can’t afford outdated search models. AI-powered search must evolve and move beyond basic keyword matching to truly understand intent, context, and behavior. By incorporating synonyms, variations, and long-tail queries, search engines can meet shopper expectations, reduce friction, and keep sales in-house.
AI-Relevance Explained
Coveo CEO Laurent Simoneau explains that relevance isn’t a single factor, but a dynamic equation that balances multiple variables to ensure shoppers find what they need while supporting business goals.
At scale, ecommerce search must handle massive product catalogs, diverse customer behaviors, and shifting demand patterns. To achieve this, AI-driven relevance incorporates several critical components:
- Business rules: Merchandisers need the ability to boost and bury products based on business priorities, promotions, or inventory levels.
- Precision: Eliminating noise is just as important as surfacing results. Lexical matching ensures exact keyword matches, but that alone isn’t enough.
- Similarity: AI must go beyond keywords to recognize natural language queries, context, and meaning. This balances precision and similarity, ensuring the right results appear — even when search terms vary.
- Popularity: Behavioral data helps refine search results. Products with high engagement and relevance should rise to the top based on real-time user behavior.
- Intent: AI can analyze browsing patterns and previous interactions, using product vectors and deep learning to surface results that match a shopper’s intent — even with just a few clicks.
Balancing Shopper Needs and Profitability
“Search isn’t just about surfacing the right product — it’s about surfacing the right product at the right time, in a way that supports business goals,” explains Curran.
Many ecommerce search platforms fall into a dangerous trap: prioritizing attractiveness over profitability. They assume that featuring eye-catching, deeply discounted, or trending products will automatically boost conversions. But this approach has a major flaw; it chases short-term wins at the expense of long-term success.
If search engines focus solely on conversion, they might push deeply discounted products to the top, cutting into margins. On the other hand, prioritizing only high-margin products without considering relevance can frustrate customers and lead to poor experiences. The result? A race to the bottom, where neither shoppers nor businesses truly win.
AI solves this by introducing business-aware product ranking, which blends user intent, behavioral signals, and business KPIs to optimize for both relevance and profitability. Instead of simply ranking by clicks or discounts, AI-powered search ensures that results strike the right balance — presenting products that are both meaningful to shoppers and valuable to the business.
Because at the end of the day, it’s not just about what sells — it’s about what sells profitably.
Personalization: AI That ‘Gets’ Your Customers
“One of the highest compliments a user can give a retailer is saying, ‘This retailer really understands me like no other,'” said Suzie Kronberger, AI Revenue Growth Leader. “And AI enables retailers to understand motivations, behaviors, and intent beyond just past purchases.”
Modern ecommerce shoppers demand more than just accurate search results—they expect personalized, seamless experiences across all touchpoints. AI-powered personalization enhances search in several key ways:
- Reducing Decision Fatigue: Shoppers often feel overwhelmed by too many choices. AI refines search results based on their preferences, guiding them effortlessly to the best options.
- Conversational AI Assistants: Instead of manually applying filters, users can describe their needs in natural language, such as “Show me waterproof hiking boots, but no leather options.”
- Cross-Channel Consistency: AI ensures that product recommendations remain consistent across a retailer’s website, mobile app, and customer service channels, reinforcing trust and engagement.
How to Get Started with AI Today
For retailers looking to optimize both relevance and profitability, AI-powered search is the most effective way to drive higher conversions, better customer experiences, and increased revenue.
- Evaluate your current search experience. Identify where shoppers struggle to find products.
- Align AI investments with business goals. Focus on improving search relevance, increasing AOV, or optimizing profitability.
- Prepare your data infrastructure. AI needs structured and unstructured data sources to maximize impact.
Embrace AI or Compete Against It
The future of ecommerce belongs to those who prioritize relevance, embrace AI, and optimize for long-term profitability.
“The ecommerce landscape is changing fast,” Curran warns. “Businesses that adopt AI-powered relevance and profitability strategies today will be the ones thriving tomorrow. Are you ready?”
Want to learn more? Explore how Coveo’s AI-powered search and product discovery can help you optimize relevance, boost conversions, and drive profitability.