6 Practical Ways to Boost Product Discovery in 2026

Product discovery in 2026: why traditional approaches no longer work

Product discovery has fundamentally changed. The old formula of decent product titles plus basic category pages is no longer enough. In 2026, shoppers expect to find the right product in seconds, whether they type a detailed query, upload a photo, or ask a voice assistant. When your catalog cannot keep up with that expectation, they leave.

The numbers tell the story clearly. Over 56 percent of shoppers now expect always-personalized offers. Visual search queries have grown 70 percent year-over-year globally. Zero-click searches account for roughly 69 percent of all queries, meaning AI systems answer questions directly without sending traffic to your site. And 64 percent of consumers say they are willing to buy items recommended by generative AI.

For ecommerce teams, this means product discovery is no longer just a UX consideration. It is a data infrastructure problem. If your product data is incomplete, inconsistent, or poorly structured, every downstream discovery mechanism suffers: on-site search returns weak results, filters show irrelevant options, marketplace listings lack required attributes, and AI assistants skip your products entirely.

This guide covers six practical areas where catalog and merchandising teams can make measurable improvements this quarter. Each section focuses on what to do, not just what to know, so you can turn these into action items for your team.

Build intent-aware on-site search that handles real queries

The bar for ecommerce search has moved far beyond keyword matching. Shoppers now expect your search to understand natural language queries like "lightweight waterproof jacket for hiking under 100" and return precisely relevant results. If your search engine only matches title keywords, you are losing conversions on every complex query.

A practical search stack in 2026 needs several capabilities working together:

• Semantic understanding that interprets intent, not just keywords. When someone searches "gift for 10-year-old boy who likes science," the engine should surface science kits and educational toys, not just products with those exact words in the title.
• Attribute-level matching so queries with specific parameters (size, color, compatibility, price range) filter results accurately without requiring the shopper to use separate filter controls.
• Zero-result fallbacks that offer related categories, similar terms, or popular alternatives instead of an empty page. For a detailed playbook on handling this, see our guide to fixing zero-result pages.
• Search analytics broken down by query type (brand, use case, compatibility, size) so your team can identify and prioritize gaps.

The operational discipline matters as much as the technology. Review your top 100 queries weekly. Classify them as performing, underperforming, or zero-result. For each problem query, trace the root cause: is it a missing attribute, a synonym gap, a relevance ranking issue, or a catalog data problem? This creates a concrete backlog instead of vague "improve search" initiatives.

For a comprehensive walkthrough of building this kind of audit loop, the ecommerce site search checklist provides a step-by-step framework.

Design filters around buying decisions, not database fields

Filters are one of the most underestimated discovery levers. When they work well, shoppers narrow thousands of products to a handful of relevant options in seconds. When they are poorly designed, they create confusion, dead ends, and category abandonment.

The most common mistake is building filters from internal data structures instead of actual buying behavior. Your database might have 30 attributes per product, but shoppers typically make decisions on four to six dimensions: price, brand, a key physical attribute (size, capacity, weight), compatibility, and availability.

A practical filter design process:

1. Pull search and category exit data. Which queries lead to bounces? Which category pages have high exit rates?
2. Group the data by decision factor. Are shoppers leaving because they cannot filter by compatibility? By specific dimensions? By certification?
3. Build progressive filter sets: show the five to six most-used filters by default, then reveal advanced options after the first refinement. This works especially well on mobile where screen space is limited.
4. Enforce consistent value formatting. "128 GB" and "128GB" fragmenting into separate filter options erodes trust and splits results.

Run a nightly validation check against inventory. Filter values that return only out-of-stock products create dead ends. Automatically suppress or deprioritize these. And localize filter labels to match how customers actually talk. "Water resistance rating" may be clearer than "IP68" for mainstream buyers, while technical audiences may want both.

A deeper look at filter strategy, including dynamic facets and mobile considerations, is available in the faceted navigation best practices guide.

Treat taxonomy as a discovery system, not a navigation menu

Taxonomy errors are silent discovery killers. A product can have excellent copy, strong images, and complete attributes, yet still underperform because it sits in the wrong category branch, inherits incorrect metadata, or has its variants scattered across multiple paths.

Three rules that keep taxonomy working for discovery:

• One primary category per product family. Duplicate paths for the same product create confusion in navigation, dilute search relevance, and cause issues in feed exports.
• Clean variant relationships. Size, color, and pack count should be variants under a single parent, not independent products. Variant fragmentation inflates your catalog artificially and confuses both shoppers and algorithms.
• Category-specific required attributes. Each category template should define mandatory fields, recommended enrichment attributes, naming conventions, and image requirements. No product publishes until it passes template validation.

Run a quarterly taxonomy audit with input from SEO, merchandising, and catalog operations. The audit typically reveals three recurring defects: overloaded categories that mix different purchase intents (accessories with replacement parts, for example), duplicate category paths for the same product family, and variant fragmentation where colors or sizes appear as separate listings.

Fixing these issues improves not just on-site discoverability but also feed quality, crawlability, and paid channel performance. For more on how taxonomy connects to SEO and marketplace visibility, see product taxonomy for ecommerce SEO and search.

Expand reach with synonyms and intelligent query rewriting

Even the best-structured catalog misses conversions when shoppers use different language than your product team. Synonyms and query rewriting are consistently the lowest-effort, highest-impact improvements teams can make.

Build synonym sets from real evidence, not assumptions:

• Site search logs: focus on high-frequency queries that produce zero results or zero clicks
• Support conversations: customer service interactions reveal the exact terminology real buyers use
• Regional language differences: US vs UK spelling, local slang, industry jargon versus consumer terms
• Competitor catalog language: how do shoppers describe similar products on other sites?

Then apply governance. Synonyms should be versioned, tested in batches, and monitored after deployment. A poorly constructed synonym map can create false matches that degrade trust. For example, mapping "tablet" to "iPad" might make sense in consumer electronics but would be disastrous in a pharmacy context.

Beyond basic synonyms, implement controlled query rewriting for common shorthand and attribute inversions:

• "laptop bag 15 inch" should match "15-inch laptop bag" and "laptop sleeve 15 inches"
• "usb c charger macbook" should match "USB-C MacBook charger"
• "kids helmet 52" should match "children's helmet size 52 cm"

Use a holdout group when rolling out new synonym bundles. If relevance drops for even a small set of high-value queries, roll back quickly. This test-and-release discipline prevents silent degradation that goes unnoticed for weeks.

Tools like Lasso help make synonym and rewriting strategies safer by standardizing attribute values and product naming across supplier feeds before query logic is applied. When your underlying catalog language is consistent, query rewriting produces more predictable results.

Prepare your catalog for AI-powered and zero-click discovery

This is the trend that changes everything else. In 2026, a growing share of product discovery happens outside your website entirely. AI shopping assistants, generative search results, and conversational commerce interfaces are surfacing products directly to shoppers, often without a traditional click-through to your product page.

Zero-click searches now represent roughly 69 percent of all queries. AI agents parse structured product data: titles, attributes, dimensions, materials, use cases, compatibility, certifications, pricing, availability, and shipping rules. If your product data is thin or unstructured, these systems simply skip your products in favor of competitors with richer data.

What this means practically:

• Schema markup is no longer optional. Pages with structured data achieve 20 to 40 percent higher click-through rates, and rich results deliver up to 82 percent higher CTR compared to non-rich results. Product schema should include granular attributes like material composition, certifications, and compatibility matrices, not just name and price.
• Write for AI parsability. Product descriptions need to answer specific questions that AI assistants ask on behalf of shoppers: What is this product made of? Who is it for? What is it compatible with? What problem does it solve?
• Complete your attribute coverage. Every field that an AI agent might use to match a shopper query needs to be filled. This goes far beyond basic compliance fields. For a prioritized checklist, the product data quality checklist covers which fields matter most by category.

Lasso's enrichment features are designed precisely for this challenge: ingesting messy supplier data, mapping it to a clean schema, filling attribute gaps with AI-assisted enrichment, and publishing consistent structured data across all channels. When AI agents evaluate your products, the completeness and consistency of your data determines whether you get recommended or ignored.

For a broader look at how AI enrichment tools are reshaping catalog management, see the AI product data enrichment tools overview.

Use merchandising rules to amplify discovery, not override it

Merchandising rules shape business outcomes, but they should never replace relevance as the foundation of product discovery. When every search query returns the same promoted products regardless of intent, shoppers learn that your search is promotional rather than helpful and they stop trusting it.

A three-layer merchandising model keeps this balance:

• Relevance layer: semantic and attribute-based matching determines the core result set
• Business layer: margin targets, stock depth, seasonality, and campaign priorities adjust ranking within relevant results
• Safety layer: suppress out-of-stock items, high-return products, and policy-risk SKUs before they reach shoppers

Track impact by query cohort, not just aggregate revenue. A merchandising rule that boosts total revenue by 3 percent might simultaneously destroy conversion for a specific query segment. Without cohort-level analysis, that damage stays hidden.

Practical KPIs for merchandising-aware discovery:

• Zero-result rate and trend direction
• Filter usage rate and post-filter conversion
• Search click-through rate segmented by intent type
• Revenue per search session
• Time to first meaningful product interaction

For seasonal campaigns, predefine expiration rules. Promotions should auto-expire from pinned placements when stock runs low or campaign windows close. Stale merchandising rules that linger for weeks after a campaign ends silently degrade search quality.

For deeper strategies on using product attributes to power smarter merchandising decisions, see merchandising with attributes. And if you are looking to optimize the product detail pages that discovery leads to, the PDP optimization guide covers the conversion side of the equation.

The bottom line: product discovery in 2026 is a data problem first and a technology problem second. Teams that invest in structured attributes, clean taxonomy, and disciplined search operations will outperform those chasing the latest search tool without fixing the data underneath it. Start with one high-volume category, measure the before and after, and expand from there. That is how discovery improvements compound into lasting competitive advantage.