Conversational eCommerce: A Guide to AI-Powered Product Discovery

When a shopper types a query into your search bar and hits a dead end, you don’t just lose a click - you lose a customer. That attempt at conversational shopping has fallen flat.

The “no results” page has become one of the most expensive missed opportunities in ecommerce. Every failed search represents both a need and a signal your system could have understood - but didn’t.

What is Conversational eCommerce?

Conversational shopping is redefining ecommerce, moving beyond static search bars to interactive, natural language experiences. Today’s shoppers expect digital storefronts to act like knowledgeable shop assistants - answering questions, understanding nuance, and guiding product discovery in real time. For Syntheum, this means empowering merchandisers with Agentic AI that turns complex catalog data into intuitive, revenue-driving conversations.

Instead of navigating complex filters, a user can simply ask, "I need a breathable outfit for a summer wedding in Italy," or "Show me high-performance running shoes under $150." Syntheum’s agents analyze the intent behind these queries, instantly curating personalized results that streamline the path from curiosity to checkout.

Key benefits for Syntheum users:

Merchandiser Control: Abstracts the technical complexity of ranking algorithms and schema markup, letting teams focus on strategy while AI handles the operational grunt work.
Zero-Click Readiness: Ensures brand visibility not just on-site, but across the emerging landscape of off-site AI discovery engines (like ChatGPT and Gemini).
Contextual Conversion: The Store Associate agent acts as a guide, turning vague inquiries into precise transactions by understanding shopper intent, lowering the barrier to purchase.

Conversational commerce transforms how brands engage with customers in the AI era. It allows users to shop by describing their needs rather than learning site navigation, making the digital experience feel less like a database search and more like a consultation with a trusted expert.

The High Cost of a Dead End

A “no results” page is more than an inconvenience. It tells your customer, we don’t have what you’re looking for - even when you do.

Industry studies show that between 10% and 20% of onsite searches end with zero results. That’s not just lost traffic; it’s lost revenue. When faced with a dead end, most shoppers don’t refine their search. They leave. The damage compounds as frustrated users associate your brand with inefficiency.

Merchandisers already know how expensive it is to attract a visitor. Paying to bring them in, only to send them away empty-handed, undermines every marketing dollar spent upstream.

Why Traditional Search Fails

Traditional search engines rely on literal keyword matching. If a user’s phrasing doesn’t match your product catalog exactly, the system draws a blank.

Here’s how it typically breaks down:

Long-tail queries - like “black vegan boots for winter hiking” - exceed the system’s ability to parse intent.
Synonyms cause mismatches (“hoodie” vs. “sweatshirt”).
Misspellings or regional terms (“color” vs. “colour”) block otherwise relevant items.
Structured data gaps - incomplete attributes or inconsistent tags - make the problem worse.

These limitations create the illusion of scarcity. You may have the perfect product, but if your system can’t understand how people talk about it, it will never appear in results.

The Power of AI and Semantic Search

AI search doesn’t look for phrases or words that are an exact match for a human user’s query. We’re used to that, but time has marched on.

No, it looks for meaning. Through semantic search, machine learning models understand relationships between terms - like “joggers” and “athletic pants” - and use those connections to return relevant results even when no direct match exists.

Unlike rule-based search engines, AI systems learn continuously. They observe what users click, how they refine their queries, and which products ultimately lead to conversions. Over time, they become fluent in your customers’ language.

This is where AI-powered product discovery becomes essential. It’s not just about fixing search; it’s about understanding intent and mapping it to inventory in real time.

Introducing the Semantic Merchandising Mesh

This shift, from keyword matching to intent-based discovery, requires a powerful system behind it. Merchant Agent’s core technology - the Semantic Merchandising Mesh - was built to resolve “no results” gaps automatically by applying semantic intelligence at scale.

It works behind the scenes, mapping user intent to product attributes through an intelligent network that learns from every interaction.

When a user searches for something that isn’t a direct match, the system:

Interprets intent through semantic vectors.
Identifies related categories, materials, or use cases.
Suggests compatible alternatives or visually similar products.

For example, if someone searches for “linen blazer” and your catalog only includes “cotton sport coats,” Merchant Agent connects the dots - showing the closest match rather than an empty screen.

The result: fewer dead ends, longer sessions, and higher conversion rates.

(Internal link recommendation: resolve gaps automatically)

Beyond Search: AI as a Merchandising Partner

AI’s strength doesn’t stop at avoiding zero-result pages. It provides merchandising intelligence. Every search reveals what customers expect to find - even when you don’t carry it yet.

That data can inform:

Product assortment decisions: Identify trends from failed searches.
Synonym libraries: Expand keyword coverage based on user phrasing.
Content enrichment: Add missing attributes and improve tagging accuracy.
UX design: Tailor filters, sorting, and navigation around actual behavior.

Merchant Agent uses this information to strengthen both product discovery and catalog integrity. Instead of fixing errors reactively, teams can anticipate customer needs and respond strategically.

Actionable Steps to Improve Your Search Experience

Before implementing any AI solution, conduct a search audit to understand the scope of your “no results” problem.

Here’s a practical framework:

Collect search data
Export the past 90 days of search logs. Identify queries that returned zero results.
Categorize by intent
Separate spelling errors, long-tail queries, and product-gap searches. This helps you see whether the issue lies in data quality or catalog breadth.
Audit your product data
Look for missing attributes or inconsistent taxonomy. For instance, if “navy blue” isn’t tagged as “blue,” your search engine may fail to associate them.
Review your synonym mappings
Customers often use different words for the same thing. A synonym list can patch minor gaps, though it’s only a stopgap compared to semantic AI.
Evaluate your “no results” page design
Replace the dead end with meaningful suggestions - featured collections, recent searches, or related categories.

The Business Case for Fixing “No Results”

Solving this issue isn’t just a UX improvement. It’s a revenue multiplier.

Consider the compounding effects:

A 15% reduction in zero-result searches could yield a 5 - 10% lift in conversion rate.
Improved product discovery decreases bounce rates and increases average order value.
Fewer search failures mean more engaged shoppers- and better first-party data quality.
Alternatives to no results pages exist.

For ecommerce leaders, this is a rare area where improving customer experience directly drives measurable financial gain.

How Merchant Agent Differs

Most onsite search solutions rely on partial fixes - synonym lists, basic NLP, or manual tuning. Merchant Agent goes further. Its semantic engine creates a mesh of meaning across your catalog, letting it infer relationships automatically.

Where others match strings, Merchant Agent matches intent.

That difference matters for teams managing thousands of SKUs across changing assortments. Instead of constant configuration, the system adapts to new data and user behavior, turning what used to be maintenance work into an automated process.

FAQs

How do I know if “no results” is costing my business money?
Check your analytics for sessions that include a search with zero results followed by an exit. Multiply that by your average conversion rate and order value - the cost becomes clear quickly.

What’s the difference between semantic search and keyword search?
Keyword search looks for literal matches. Semantic search understands meaning and context, enabling results that align with intent rather than syntax.

Can AI really handle long-tail or ambiguous queries?
Yes. AI models trained on behavioral data can interpret complex phrases like “eco-friendly toddler rain jacket” by mapping each concept (“eco-friendly,” “toddler,” “rain jacket”) to product attributes.

Do I need to rebuild my entire site search system to adopt AI?
Not necessarily. Merchant Agent can integrate with your existing infrastructure, enhancing results without overhauling the frontend experience.

Looking Ahead

The “no results” problem is one of the most visible signs of outdated onsite search - and one of the easiest to solve with intent-aware AI. As ecommerce competition intensifies, the brands that thrive will be those that make every query productive, every session rewarding, and every shopper feel understood.

Merchant Agent’s approach turns product discovery from a static process into a living, learning system that connects meaning, context, and commerce - at scale.

About Syntheum.ai

We help e-commerce retailers implement agentic ecommerce merchandising solutions that go beyond basic automation. By integrating truly intelligent systems into merchandising strategies, we help businesses unlock their full potential - delivering efficiencies that improve operations and redefine what’s possible in online sales.

Empower Merchants with Ease and Intelligence

Syntheum is the Semantic Merchandising Platform for Agentic Commerce - powering onsite search, conversational shopping, and AI discovery through one merchandising brain your team controls.

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