7 Ways AI Can Improve Product Discovery in Your Magento Store
What Is AI-Powered Product Discovery?
Product discovery describes the process customers use to find, compare, and select products in an online store. It includes site search, category pages, filters, recommendations, related products, merchandising, and product content.
AI-powered product discovery uses technologies such as machine learning, behavioural analysis, semantic matching, and personalization to make those experiences more relevant.
Instead of relying entirely on exact keyword matches or manually created product relationships, an AI-powered system can evaluate signals such as:
- The meaning and context of a shopper’s search query
- Products viewed during the current session
- Previous clicks, purchases, and browsing behaviour
- Product attributes, categories, and catalog relationships
- Popular, trending, and frequently purchased products
- Merchandising rules and business priorities
Understand What Shoppers Mean, Not Just What They Type
Traditional Magento search generally depends on the words stored in product names, descriptions, SKUs, and searchable attributes. This can work well when the customer uses the same language as the catalog.
The problem is that customers often describe a need instead of entering an exact product name. AI-powered semantic search can analyze the meaning and context behind a query and connect it with relevant products.
A shopper searches for “lightweight bag for a weekend flight.”
A keyword-only system may look for products containing the exact words “weekend” and “flight.” Semantic search can interpret the intent and return suitable cabin bags, compact duffle bags, or lightweight carry-on luggage.
This helps customers discover products even when they do not know the exact product name, category, technical specification, or terminology used by the merchant.
Potential benefits
- More relevant results for natural-language searches
- Better support for broad or descriptive queries
- Less dependence on exact catalog terminology
- A faster path from search to product page
Reduce Zero-Result and Low-Relevance Searches
A zero-result search occurs when the store cannot find a product that matches the shopper’s query. This creates a dead end at a moment when the visitor has clearly expressed purchase intent.
AI can reduce these dead ends by combining semantic understanding with existing search tools such as:
- Synonyms and related terminology
- Searchable product attributes
- Spelling tolerance
- Search rules and product boosts
- Category and facet information
- Meaning- and context-based matching
Your catalog uses the term “sofa bed,” but a shopper searches for “couch that turns into a bed.”
An AI-enhanced search experience has a better chance of understanding that these phrases describe the same product requirement.
Store owners should still review search reports regularly. AI improves matching, but search data can also reveal missing products, weak product descriptions, incorrect attributes, and customer terminology that has not been added to the catalog.
Personalize Product Recommendations
Generic recommendations show the same products to every visitor. AI-powered recommendations can use catalog information and aggregated shopper behaviour to present products that are more relevant to the customer’s current journey.
Depending on the platform and data available, recommendation units can include experiences such as:
- Recommended for you
- Customers who viewed this also viewed
- Customers who bought this also bought
- Trending products
- Most viewed products
- More like this
- Recently viewed products
A customer views several beginner-level photography products. Instead of promoting the most expensive professional equipment, the store can recommend entry-level lenses, camera bags, memory cards, and starter accessories that better fit the shopper’s journey.
Effective personalization helps customers discover products they may not have found through navigation or search alone.
Improve Search and Category Product Ranking
Product discovery depends not only on which products appear, but also on the order in which they appear.
AI-assisted merchandising can help determine which products should be presented first by evaluating relevance, behavioural signals, product performance, and merchandising rules.
Store owners can combine automated ranking with business controls such as:
- Boosting high-margin or strategically important products
- Promoting new arrivals
- Prioritizing in-stock products
- Reducing visibility for unavailable products
- Highlighting seasonal collections
- Applying campaign-specific search rules
The strongest approach usually combines AI relevance with human merchandising. AI can identify patterns at scale, while the merchant retains control over inventory, promotions, brand priorities, and commercial strategy.
During a back-to-school campaign, the store can prioritize relevant backpacks, stationery, laptops, and student accessories while still allowing the search engine to personalize ordering based on each shopper’s behaviour.
Support Conversational and LLM-Based Product Discovery
Product discovery is expanding beyond traditional search boxes. Some shoppers now expect to describe what they need in complete sentences, ask follow-up questions, and compare options conversationally.
AI-powered conversational experiences can help customers ask questions such as:
- Which running shoes are suitable for flat feet?
- What laptop is best for video editing under my budget?
- Which skincare products are suitable for dry skin?
- What should I buy for a three-day camping trip?
A well-designed system can translate these questions into catalog attributes, filters, product comparisons, and recommendations.
Merchants should also prepare product content for discovery through external AI assistants and large language models. Complete titles, descriptions, specifications, structured data, availability, pricing, and product relationships make catalog information easier for both traditional and AI-led channels to interpret.
Improve Cross-Selling, Upselling, and Bundle Discovery
Cross-selling and upselling are most effective when the suggested products are genuinely relevant to the customer’s current purchase.
AI can analyze product relationships and shopper behaviour to identify useful complementary items, substitutes, upgrades, and bundles.
A customer viewing a coffee machine could receive recommendations for compatible filters, coffee beans, cleaning tablets, milk frothers, or a higher-capacity model.
These recommendations can be placed across the customer journey:
- Product detail pages
- Category and search-result pages
- Mini-cart and cart pages
- Checkout pages, where appropriate
- Customer account areas
- Order-confirmation pages
- Email and remarketing campaigns
Avoid displaying the same recommendation strategy everywhere. A “similar products” unit may be useful on a product page, while “frequently bought together” may be more useful near the cart.
Continuously Optimize Product Discovery Using Data
AI product discovery should not be treated as a one-time installation. Search behaviour, customer language, product demand, inventory, and merchandising priorities change over time.
Search and recommendation analytics can help store owners identify:
- Popular search terms
- Searches producing no results
- Queries with low click-through rates
- Products receiving recommendation impressions
- Recommendations generating clicks or purchases
- Categories with weak engagement
- Products frequently viewed together
These insights can support better catalog content, search rules, synonyms, recommendation placement, inventory planning, and marketing campaigns.
Business Benefits of AI Product Discovery
Faster Discovery
Customers can reach suitable products with fewer searches, filters, and category-page visits.
Better Relevance
Search results and recommendations can better reflect the shopper’s intent and behaviour.
More Engagement
Relevant recommendations encourage customers to explore more products and categories.
Higher Conversion Potential
Reducing discovery friction can help more shoppers move from search to product page, cart, and checkout.
Improved Order Value
Relevant accessories, bundles, substitutes, and upgrades can support cross-selling and upselling.
Less Manual Merchandising
Automated product relationships can reduce repetitive manual work across large catalogs.
Adobe Commerce vs Magento Open Source AI Options
| Capability | Adobe Commerce | Magento Open Source |
|---|---|---|
| Semantic search | Available through supported Adobe Commerce Live Search configurations. | Usually requires a third-party search service, extension, or custom integration. |
| AI product recommendations | Native Adobe Commerce Product Recommendations services are available for supported implementations. | Requires an extension, external recommendation platform, or custom recommendation engine. |
| Behavioural personalization | Can use Adobe Commerce and connected Adobe Experience Cloud services. | Depends on the analytics, personalization, and consent platforms integrated with the store. |
| AI merchandising | Available through supported Adobe Commerce search, recommendation, and merchandising capabilities. | Can be implemented through third-party merchandising tools or custom business logic. |
| Conversational shopping | Availability depends on the selected Adobe services and current release status. | Typically requires an external AI platform and custom catalog integration. |
The best option depends on catalog size, traffic, existing Adobe services, storefront architecture, budget, internal expertise, and the complexity of the customer journey.
AI Cannot Fix Poor Magento Product Data
AI performance depends heavily on the quality of the data provided to it. Missing attributes, vague titles, duplicate products, inconsistent categories, and incomplete descriptions can reduce search and recommendation relevance.
Before implementing AI product discovery, review:
- Product titles and descriptions
- Category assignments
- Searchable and filterable attributes
- Brand, size, color, material, and compatibility data
- Product images and alternative text
- Pricing and inventory accuracy
- Related products and accessory relationships
- Structured product data
- Duplicate and discontinued products
Magento AI Product Discovery Implementation Checklist
- Define the customer-discovery problem you want to solve.
- Review current search terms and zero-result searches.
- Audit Magento product titles, attributes, and descriptions.
- Confirm whether the store uses Adobe Commerce or Magento Open Source.
- Review the compatibility of the theme and storefront architecture.
- Evaluate native, third-party, and custom AI options.
- Document privacy, consent, and data-governance requirements.
- Configure search synonyms, facets, rules, and product boosts.
- Select recommendation types for each storefront location.
- Test the implementation in a staging environment.
- Validate mobile, desktop, and headless storefront experiences.
- Test different customer queries and natural-language searches.
- Track search, recommendation, conversion, and revenue metrics.
- Review performance regularly and continue optimizing.
Frequently Asked Questions
Can AI product discovery be used with Magento Open Source?
Yes. Magento Open Source merchants can implement AI-powered search and recommendations through compatible third-party extensions, external SaaS platforms, or custom integrations. However, native Adobe Commerce services may not be included in Magento Open Source.
What is semantic search in Adobe Commerce?
Semantic search uses AI to match products based on meaning and context instead of relying only on exact keywords. It can help shoppers find relevant products when their wording differs from the terminology used in the catalog.
Which Adobe Commerce versions support semantic search?
Adobe currently documents semantic search support for merchants using Adobe Commerce version 2.4.4 and newer with supported Live Search configurations. Availability and setup requirements should be confirmed against the latest Adobe documentation.
Will AI automatically increase Magento conversions?
No technology can guarantee a conversion increase. Results depend on catalog quality, product demand, pricing, user experience, search configuration, recommendation placement, site performance, and ongoing optimization.
Does AI replace Magento merchandising teams?
No. AI can automate repetitive analysis and identify useful product relationships, while merchandisers remain responsible for campaign strategy, brand priorities, inventory considerations, promotions, and customer experience.
What data is needed for AI product recommendations?
Recommendation systems commonly use catalog data and aggregated behavioural signals such as product views, clicks, cart activity, and purchases. Exact requirements depend on the selected platform and implementation.
How should AI product discovery performance be measured?
Track metrics such as zero-result rate, search click-through rate, search conversion rate, recommendation engagement, revenue per search session, average order value, and assisted conversions.
Final Thoughts
AI can improve Magento product discovery by helping customers express their needs naturally, find relevant products faster, receive personalized recommendations, discover useful accessories, and navigate large catalogs more effectively.
The technology alone is not enough. Successful implementation also requires accurate product data, clear business goals, suitable privacy controls, careful storefront testing, and continuous performance analysis.
Start with a specific problem, such as high zero-result searches or weak product recommendation engagement. Measure the current performance, implement the appropriate solution, and optimize based on real shopper behaviour.