Conversational search examples: How leading brands turn queries into conversions

Key takeaways from this article

• Understand how conversational search differs from traditional keyword-based search and why it matters for ecommerce today
• Learn how leading brands like Microsoft and Canon use conversational search to drive conversions and improve customer experiences
• See real examples of digital assistants guiding users through complex buying journeys with natural language interactions
• Explore how conversational search supports decision-making, reduces friction, and builds confidence
• Gain actionable insights into implementing conversational search to elevate product discovery and stay competitive

When a shopper searches for "What laptop should I buy for school?" they’re not just looking for specs. They want a recommendation that fits their situation. Traditional keyword-based search engines aren’t built for that kind of intent. But conversational search is.

This blog explores real conversational search examples, why they matter, and how ecommerce brands are using this technology to guide users with personalized, dialogue-driven discovery experiences.

Why conversational search matters

Most ecommerce platforms are still built on traditional keyword-based search. Users type in a term like "wireless headphones" and scroll through hundreds of results. The experience is static, impersonal, and disconnected from how people naturally ask questions.

Modern shoppers expect more. They use natural language queries like "What are the best wireless headphones for running?" They expect to receive relevant answers, not just product lists. They want to ask follow-up questions. They want a search experience that feels like a human interaction, not a database lookup.

That’s where conversational search platforms come in.

What is conversational search?

Conversational search is a user experience that simulates a dialogue. Instead of entering a single keyword or phrase, users engage in a back-and-forth conversation with a digital assistant or chat interface.

These systems are powered by natural language processing (NLP), artificial intelligence, and vector embeddings that analyze user queries in real time. They adapt based on user intent, previous interactions, and conversation memory and deliver more accurate search results with each input.

Unlike voice search, which focuses solely on audio input, conversational search combines input with dynamic, personalized responses that evolve throughout the session. It's the bridge between human conversation and data-powered commerce.

Key features of a conversational search experience

A modern conversational search engine includes:

• Natural language understanding: Interprets everyday language and detailed questions
• Conversational tone: Mimics human interaction, making the experience feel intuitive
• Follow-up questions: Refines results based on new information
• Contextual awareness: Uses previous steps to personalize results
• Multilingual capabilities: Responds in native languages when needed
• Conversational API response: Powers scalable experiences across multiple channels

These features work together to transform a standard search query into a guided journey.

Real examples of conversational search in ecommerce

If you're looking for conversational search examples that show how this works in the real world, here are five standout deployments powered by Zoovu’s AI Search platform:

Side-by-side: Noble Knight vs. Troll & Toad

Let’s start by comparing two game retailers using different search strategies.

Noble Knight (powered by Zoovu's conversational search)

Noble Knight uses Zoovu’s conversational search capabilities to translate vague shopper queries into highly relevant product results. For example, when someone searches for "5 person strategy games," the system understands that the user is looking for games that support five players. It uses product attributes, like player count, to match the query with compatible options such as "Nothing Personal" and its expansions. The assistant dynamically adjusts filters based on the user's intent, maintaining context across the journey. The result is a responsive, needs-based search experience that helps users find what they're really looking for.

Troll & Toad (traditional keyword search)

Troll & Toad uses a basic keyword-matching system. When the same search term is entered, the engine returns no results, even if compatible products exist in the catalog. The system doesn’t understand the user intent or apply semantic logic. It lacks conversational capabilities and delivers a frustrating zero-results experience.

The difference? Conversational search platforms like Zoovu do the heavy lifting. They understand conversational queries, interpret meaning beyond keywords, and provide accurate search results that drive conversions.

Microsoft: Digital assistant for Surface devices

Microsoft’s conversational assistant for Surface devices helps users quickly identify the best-fit product through a structured, natural language-style experience. It begins with a broad question:

How will you use your Surface? (Users can select multiple options)

• Surf and connect: Social media, online shopping, file storage
• Entertainment: Video calls, music, casual gaming
• Get things done: Productivity with Microsoft 365 apps and multitasking
• Creating: Adobe Creative Cloud, Visual Studio, digital design
• Top performance: High-powered graphics and processing for gaming, coding, and heavy workloads

Each selection informs the assistant’s follow-up questions and narrows down device recommendations based on actual use cases. The experience is designed to be completed in under a minute and mimics the conversation a customer might have with an in-store rep.

Key strength: Microsoft combines user feedback, visual cues, and layered preferences to deliver results that reflect intent, not just filters.

The conversational search system remembers the user's path and streamlines discovery across over 250 of Microsoft’s retail partners.

Outcome: Increased engagement by 90% and boosted retail sales by 270%.

Logitech: Ultimate desktop setup recommender

Logitech's conversational search experience begins with a question: "First off, where do you work?" The assistant presents three visual choices:

• Dedicated Workspace: "I have a fixed desk."
• Pop-Up Workspace: "I split time between locations."
• Anywhere and Everywhere: "I work from cafes, couches, or wherever I can."

Each choice triggers a series of guided questions that surface relevant accessories like ergonomic mice, compact keyboards, wireless headsets, and high-resolution webcams tailored to the user’s environment and needs.

Once the assistant gathers enough context, it presents a complete visual bundle recommendation that includes all selected gear, displayed within a mockup of the user’s ideal workspace.

Why it works: Logitech blends conversational search functionality with bundling and merchandising best practices. The assistant curates high-intent product combinations based on individual use cases, simplifying cross-sell and upsell. This turns a single-product search into a high-value multi-item purchase, increasing AOV while keeping the experience hyper-relevant.

Canon: Guided camera selector

Canon’s guided assistant delivers a highly structured conversational experience that helps users navigate their camera lineup in a human-first way. The experience begins by asking:

What kind of device do you currently use?

• Smartphone
• Compact camera
• Interchangeable lens camera
• Professional camera

What type of camera are you looking for?

• Something advanced
• Something compact

What’s your main use case?

• Hobby photography
• Professional use
• Content creation
• Capturing daily life

As users respond, the assistant dynamically adjusts and guides them through a series of eight structured questions. This dialogue-driven approach replaces the need for users to self-navigate via specs or categories.

Each step considers user intent and behavior, and culminates in a personalized product recommendation. The assistant supports detailed follow-up questions and presents the benefits of each recommended model with natural language responses.

Instead of forcing users to choose between dozens of SKUs, Canon's assistant compares features in a way that's easy to understand. It highlights relevant details based on user intent, not just megapixels or model numbers.

Key capability: Offers side-by-side product comparisons and uses generative AI to explain compatibility.

Trek: Lifestyle-driven bike finder

Trek’s Bike Finder assistant delivers a clean, conversational experience that reflects how real customers choose a bike. Rather than filtering by specs, it opens with a context-driven question:

Where do you want to ride?

• Open road: Long miles mostly on pavement
• Dirt trails: Mountain biking, jumps, off-road
• Cities, towns, neighborhoods: Suburban and city streets, bike paths
• Gravel roads & paths: Bikepacking, gravel racing, off-pavement riding

Once a user selects their preferred terrain, the assistant guides them through a branching set of questions based on riding style, frequency, fit preferences, and desired features. Each step uses visual cues and conversational search functionality to surface relevant models.

The assistant builds a personalized journey based on individual user choices, replacing filter-heavy search facets with an intuitive discovery path. It's not just about specs, it’s about real use cases.

Value add: Supports complex decision-making with structured questions and natural user flow, reducing choice overload and increasing product relevance.

How conversational search outperforms traditional search

Modern search engines that support conversational search functionality deliver far more relevant outcomes, especially for complex queries or subjective needs.

How conversational search supports better product discovery

Conversational search platforms support every stage of the buyer journey by:

• Reducing friction: No need for exact search terms or technical jargon
• Personalizing results: Responding to individual users in real time
• Handling complex logic: Matching customer use cases, compatibility constraints, and configuration needs
• Supporting chat-based commerce: Turning static websites into interactive experiences
• Answering common questions: Direct answers without sending users to a FAQ or PDP

These systems are optimized not just for transactions, but for guidance and education as well.

Why search 3.0 requires conversational capabilities

We’re entering an era of Search 3.0, a shift from keyword-based search to goal-driven search. Ecommerce brands can no longer rely on boolean search logic and filter-heavy UX. They need conversational search systems that:

• Understand user questions, not just keywords
• Support LLM-generated answers
• Guide shoppers using detailed, branching paths
• Integrate across channels and devices

Platforms built on conversational search architecture combine semantic search logic, and vector-based product discovery. These technologies power next-generation discovery tools that outperform even the most advanced traditional search engines.

What to look for in a conversational search platform

If you’re evaluating tools or conversational search software, prioritize platforms that:

• Are built around user questions and intent
• Support follow-up questions and dynamic UI elements
• Offer multilingual support and native language understanding
• Deliver fast, relevant results across channels

Bonus: Look for systems that include conversation analytics and allow you to test different search experiences using user feedback.

Implementing conversational search: Key steps

Getting started with conversational search doesn't have to be a long, resource-heavy initiative. With Zoovu's Data Studio and Search Studio, you can go from zero to live in weeks, not months. Here’s how to approach it:

1. Evaluate your current discovery journey

• Are users primarily relying on internal search or navigating through categories?
• What percentage of your search queries return no results or irrelevant products?
• Where do users drop off? Identify where friction occurs, whether it's filter fatigue or lack of clarity.

2. Pinpoint high-intent journeys to guide

• Start with product categories where decision support matters most, items that are highly configurable, technically complex, or bundled.
• Think: laptops, CPQ product kits, skincare regimens, or workspace solutions.

3. Enrich product data for semantic matching

• Organize product attributes into buyer-relevant structures: use cases, goals, personal preferences.
• Zoovu’s Data Studio enables you to standardize and enhance metadata for semantic search and guided selling experiences.

4. Design question flows that reflect customer language

• Use Zoovu’s ontology to map real-world questions to decision logic.
• Replace technical filters with intuitive inputs like "Where do you work?" instead of "keyboard layout."

5. Build and deploy using Zoovu's Search Studio

• With no-code tools, you can build interactive assistants and deploy them across PDPs, collection pages, or site search bars.
• Test logic, preview recommendations, and go live fast without waiting on dev sprints.

6. Continuously optimize through data

• Zoovu's analytics dashboards help you spot drop-off points, surface new questions, and track which answers drive conversion.
• Use this insight to refine your flows, expand to other product lines, and increase performance over time.

Final thought

Search isn’t just about results. It’s about relevance.

Conversational search examples like Microsoft, Logitech, Canon, Trek, and Noble Knight prove that guiding users through detailed conversations, not just queries, leads to higher conversion rates and more satisfying shopping experiences.

If you want to offer a modern, effective search experience, start by rethinking how your users interact. Are they forced to scroll? Or are they empowered to ask?

The best ecommerce brands aren’t waiting for users to figure it out. They’re having the conversation first.

Get a demo of Zoovu’s conversational search platform.