Product discovery in ecommerce is the process of helping shoppers find relevant products, whether they search directly, browse categories, or engage with recommendations. It’s not just about what users type in a search box. It’s about what they’re likely to want, even if they don’t know it yet.
Modern product discovery uses AI, machine learning, and behavioral data to deliver highly relevant results across three key touchpoints: search, product listings and category pages, and recommendations. From intent-aware search results and personalized filters to dynamic product suggestions throughout the journey, discovery transforms static ecommerce experiences into adaptive, high-performing ones.
In digital storefronts, product discovery is the engine behind increased engagement, higher conversion rates, and greater average order value. It’s how today’s ecommerce leaders help shoppers find what matters — faster, easier, and more intuitively.
Most shoppers don’t search with perfect terms. They misspell words, use vague language, or type in phrases your catalog doesn’t recognize. Traditional keyword-based search
That’s a problem when 72% of shoppers say they’ll leave a site entirely if they can’t quickly find what they need.
Lexical (keyword) search is fast and works for precise queries, but struggles with ambiguity and long-tail searches. Semantic search improves this by understanding meaning and intent, but it can lack the precision and control merchandisers need.
That’s why today’s standard is hybrid search — an intelligent blend of lexical accuracy, semantic flexibility, and behavioral signals. It ensures product discovery works for every kind of query, from exact SKUs to open-ended needs.
AI is reshaping ecommerce product discovery by moving beyond the limitations of keyword-based search. Instead of relying on shoppers to know exactly what to type, AI uses Natural Language Processing (NLP) and machine learning to decode typos, vague queries, and behavioral cues to return relevant results instantly.
Coveo’s product discovery solutions harness AI across search, product listings, and recommendations. This means search results are ranked by relevance, influenced by intent, and tuned in real time based on user behavior.
Shoppers receive personalized query suggestions, context-aware product rankings, and dynamic recommendations that reflect where they are in their journey. Whether browsing categories or landing directly on a product detail page, AI ensures that discovery feels seamless, intuitive, and tailored — every time.
AI doesn’t replace merchandisers but empowers them to deliver smarter, faster product discovery at scale. Instead of relying on static rules or manual updates, AI continuously analyzes behavior, context, and performance to surface the most relevant products in real time.
Product discovery becomes a dynamic system where shoppers see what matters most to them, and merchandisers can still influence outcomes. Want to spotlight seasonal trends? Surface overstocked items or tailor assortments to local preferences? AI makes it seamless, while intuitive tools keep teams in control.
The fusion of AI and merchandising brings together automation and intent with strategy and creativity. It transforms product discovery from a backend task into a business growth driver that adapts with every click, query, and customer.
Shoppers are already using generative AI to explore and compare products — just not always on your site. Since late 2022, tools like ChatGPT and Perplexity have become go-to sources for questions like “What do I need for a weekend hike?” or “Best laptop for photo editing under $1,000.”
That’s a problem. Brands invest heavily to bring shoppers in — but if your site can’t support open-ended, natural language questions, they’ll bounce elsewhere.
Generative product discovery changes that. It brings GenAI directly into your ecommerce experience, turning the search bar into a two-way conversation. No more forcing unnatural keywords like “hiking backpack.” Shoppers can ask real questions and get AI-generated answers, curated product suggestions, and relevant content — all tailored to their intent.
At the heart of this experience is the intent box — an evolution of the traditional search bar. Instead of matching keywords, it interprets what shoppers mean and why they’re searching, using GenAI to surface helpful results, not just links.
This is discovery that listens before it speaks — making shopping more intuitive, personal, and effective.
Agentic commerce is on the rise, with a shift toward using agentic AI in ecommerce where autonomous agents act on behalf of shoppers or business users to accomplish specific goals. One especially compelling use case is in assessing the relevance of result sets.
Traditionally, relevance was judged either through human annotation, which is accurate but doesn’t scale, or through behavioral signals, like clicks and conversions, which rely on history and can introduce bias.
Today, large language models (LLMs) can act, assess, and decide. For example, they can evaluate whether a product listing generated for SEO purposes is actually relevant to a query. This enables scalable, consistent judgments that strongly align with human evaluations, without needing historical user data.
Product discovery in ecommerce goes beyond search results and is the process of helping shoppers find products through personalized, relevant, and context-aware experiences. Modern product discovery is powered by AI and includes three essential components:
1. Search
AI-enhanced search allows shoppers to find products using natural language or keyword queries. Features like: predictive query suggestions, intent-aware product ranking and automatic relevance tuning ensure search results are not only fast but contextually relevant — adapting in real time based on user behavior.
2. Product Listings
These are category or collection pages where users browse by filters and facets. AI optimizes the ranking of products based on past visitor interactions, while merchandisers can manually refine product order.
3. Recommendations
Personalized product recommendations surface relevant items across the shopper journey, from the homepage to the cart. Coveo’s recommendation models adapt dynamically to behavior and intent, increasing engagement, AOV, and conversion.
Together, these elements create a cohesive product discovery experience that adapts to each shopper’s journey.
Because attention is short, and shoppers don’t always know what to search for.
Product discovery bridges the gap between intent and action — surfacing relevant products through AI-driven search, dynamic navigation, and personalized recommendations. It doesn’t just respond to queries; it anticipates needs, adapts in real time, and guides customers toward high-value items they may not have found on their own.
As catalogs grow and consumer expectations rise, product discovery becomes essential to reducing friction, improving engagement, and increasing both conversions and average order value.
In short, it’s how ecommerce brands move from transaction to experience and from visits to revenue.
Product search is about finding something specific. Shoppers type a query like “running shoes” or “black dress pants” into a search box, expecting fast, accurate results. Traditional search focuses on retrieving what was explicitly asked for, ideally with support for typos, synonyms, and filters.
Product discovery, on the other hand, is about helping shoppers find what they didn’t know to look for. It uses AI-powered recommendations, behavioral signals, and contextual cues to surface products that align with a shopper’s needs, even if they never searched for them directly.
While product search helps people find what they want, product discovery helps them uncover what they might want — increasing engagement, average order value, and unplanned purchases.
In a modern ecommerce experience, search and discovery are essential functions that work together. Search supports efficiency; discovery supports growth. When integrated effectively, they reduce friction, improve findability, and increase revenue.
Coveo approaches product discovery as a system of intent understanding, not just search. While many platforms rely on keyword matching or limited personalization, Coveo uses a combination of machine learning models to adapt to different query types, shopper behaviors, and catalog challenges in real time.
key differences include:
This combination helps ensure that relevance is delivered consistently, across catalog types, customer intents, and device contexts.
