Today’s shoppers expect more than a search box. They want the experience to feel like ChatGPT for shopping, where the site understands their intent, anticipates their needs, and delivers results that feel almost like mind reading. In other words, interactions that are natural, efficient, and get them to the right products quickly. Conversational search makes that possible.
With AI-native platforms applying emerging technologies like transformer models behind the scenes — and shopper-facing agents like Constructor’s AI Shopping Agent (ASA) and AI Product Insight Agent (PIA) guiding the journey — retailers can turn this shift into measurable impact. The result is more satisfied customers, smoother decision-making, and higher conversion rates.
We discuss more about how (and why) conversational search has moved center stage for competitive retailers below.
What Is Conversational Search?
Conversational search lets shoppers find products by asking questions in natural language. It creates a back-and-forth experience where customers can refine results and get recommendations, much like talking with an in-store expert.
Conversational search brings the ease of everyday AI interactions into ecommerce. Shoppers now expect to search for products the same way they ask questions in ChatGPT: with natural, open-ended language, not keywords or clunky filters.
Instead of forcing customers to learn how a catalog is organized and pick the right keywords, conversational search adapts to how people think and speak. It understands intent, context, and follow-ups. So, a shopper can say, “Show me black running shoes under $100,” then refine with, “actually, make them waterproof.”
For retailers, this shift is about keeping pace with how customers already interact with technology and ensuring product discovery feels just as seamless.
Conversational search is one part of the broader shift toward conversational commerce, where shoppers engage with brands across chat, voice, and messaging interfaces. Together, they reflect a future where product discovery and customer engagement feel more like a dialogue than a transaction.
How conversational search compares to traditional keyword search
Traditional search engines rely on literal keyword matching. For example, if someone doesn't type the exact words from the catalog, the searcher may see poor, irrelevant, or no results (which is frustrating for shoppers and costly for retailers).
Solutions that use conversational search, by contrast, interpret meaning. They understand vague or exploratory queries, correct for language mismatches, and follow up when clarification is needed.
For example, if someone searches "gifts for dads who love hiking," a traditional search engine might struggle. An engine equipped for conversational search can break it down: "gifts" suggests a specific category of products for a special occasion, "dads" may imply a demographic, and "hiking" signals another category or activity. It can then return curated, high-intent products like hiking socks, daypacks, or water-resistant Bluetooth speakers.
Why Conversational Search Is Gaining Momentum
Shoppers are no strangers to conversation. They’ve spent decades interacting with human customer service reps, and now they’re used to full, natural exchanges with tools like ChatGPT, Alexa, and Siri. That expectation is spilling over into ecommerce, where the mandate is clear: keep up or get left behind.
It’s now possible to bring that same level of conversational intelligence directly into ecommerce. Generative AI and LLMs make conversational understanding accurate and scalable for retail, moving far beyond the limitations of early chatbots. Instead of stumbling on complex queries or follow-up questions, today’s AI can hold multi-turn conversations, maintain context, and surface personalized results that actually help shoppers decide.
And the standard is rising quickly. Amazon (Rufus), Walmart (Sparky), and Google are already setting new benchmarks for conversational shopping. Other retailers can’t afford to wait. Delivering seamless, intuitive interactions is necessary for staying afloat in a red ocean.
How It Works: AI, NLP, LLMs, and Transformers in Action
At the core of conversational search is NLP. NLP translates unstructured shopper language into structured intent. It makes sense of everything from long, highly specific queries to short, vague requests. Instead of forcing customers to adapt to rigid filters or exact keyword matches, the system interprets what they mean and delivers relevant results.
Large language models (LLMs) and transformer architectures take this further. They support multi-turn conversations, allowing shoppers to refine their requests naturally without starting from scratch.
This could play out in the following way:
- A user could say, “I’m looking for a warm winter coat.”
- Then, follow up with, “Only down-filled options, please.”
- And later, “Anything under $200?”
This layered back-and-forth feels intuitive, and in many ways, even better than working with a knowledgable store associate.
Shoppers don’t have to worry about phrasing questions politely or feeling judged for asking something obvious. An AI agent simply interprets the request, follows up when needed, and delivers useful answers without hesitation.
Because these models integrate behavioral signals, the experience gets smarter over time. Each interaction shapes the next, surfacing results based not just on relevance but also on attractiveness, or the products that a specific shopper is most likely to click on, add to cart, and purchase.
For merchandisers and product managers, this means less time spent manually configuring rules and more time focusing on strategy, while the system continuously learns and adapts to real shopper behavior. Win-win.
Real-World Conversational Search Use Cases
Here are a few ways retailers are already using conversational search today:
- Guided product finding: Conversational search helps shoppers, whether they know exactly what they want or need help figuring it out. Instead of clicking through endless filters, a shopper can say, “Show me running shoes under $100,” and the AI agent interprets the intent and refines results in real time. Or, if the need is more open-ended — “What should I wear to a wedding where it might be 90 degrees outside?” — the agent can guide them toward relevant options across categories. This makes conversational search especially powerful in nuanced industries like apparel, electronics, or beauty
- Product page Q&A integration: Shoppers often arrive at a product detail page (PDP) with unanswered questions about specs, shipping, sizing, returns, and more. Constructor’s AI Product Insight Agent (PIA) embeds conversational search directly into PDPs, enabling shoppers to ask their own questions or choose from suggested prompts. By delivering fast, contextual answers, PIA keeps customers engaged longer and builds the confidence they need to buy
- Voice-enabled shopping: Conversational search also pairs naturally with voice. On mobile or smart devices, shoppers can speak queries in their own words and receive guided, dynamic results. This makes discovery more accessible and convenient, especially for on-the-go users
Key Benefits of Conversational Search for Retailers and Shoppers
Conversational search brings measurable value on both sides:
- Better engagement: Shoppers spend more time on the site and explore more products
- Higher conversions: When customers can express what they want, it leads to more purchases
- Fewer dead ends: More natural inputs mean fewer "no results" pages
- Upsell and cross-sell opportunities: Follow-up prompts make it easier to recommend complementary items (we built PIA for this)
- First-party data collection: Conversational input reveals how customers really talk about your products
The insights from conversational search don’t stop at the query. Every question a shopper asks — and every product they ultimately buy — feeds back into the broader product discovery suite (better recommendations, smarter search rankings, etc.).
With Constructor, conversational interactions become a continuous feedback loop: the more shoppers engage, the more accurate and personalized the entire discovery experience becomes, aligning outcomes with both shopper needs and business goals.
How to Pilot Conversational Search on Your Site
You don't need to replace your entire search experience to get started. Here's how many retailers begin:
- Start small. Roll out conversational functionality on high-intent category or search result pages
- Layer on top of existing tools. Choose a solution that integrates seamlessly with your ecommerce tech stack to ensure product data and shopper experiences stay fully connected
- Train with real customer input. Use behavioral insights and actual queries to improve relevance continuously
- Test and iterate. A/B test conversational interfaces against traditional search to measure impact on conversion, engagement, and revenue
The Future of Ecommerce Search Is Conversational… And It’s Happening Now
Conversational search isn't replacing traditional search, but quickly enhancing it. It makes ecommerce sites feel more natural, responsive, and helpful. In an environment where Amazon and AI chat tools shape shopper expectations, that experience matters more than ever.
Retailers who adopt conversational search today can differentiate their brand, deepen customer loyalty, and gain access to a new layer of intent data that drives real revenue.
To see how conversational search can tangibly improve your shopping experience, reach out to learn more.