Conversational Search: How Consumers Ask AI for Product Recommendations

AI shopping is accelerating faster than most retailers expected. Studies show that shopping queries on ChatGPT doubled between January and June 2025, highlighting how quickly AI assistants are becoming part of the product discovery journey.

Discovery strategies have traditionally been built around keyword-based search engines, optimizing product pages to rank in blue-link results. But conversational search works differently, allowing shoppers to ask natural-language questions and receive a direct recommendation rather than browsing a list of links.

Instead of typing fragmented keywords like “lightweight running shoes for men,” shoppers now ask their AI assistants questions such as “what are the best lightweight running shoes for beginners under $150?” The AI system then synthesizes the information and directly recommends products.

Retail visibility increasingly depends on whether your products appear in AI-generated answers, not on whether they rank in search results.

In this article, we’ll review what conversational search is, how consumer shopping behavior is shifting, and what this change can mean for the visibility of your product catalog.

What is conversational search?

Conversational search is a type of product discovery in which users interact with AI systems using natural, full-sentence questions rather than short keyword phrases. Instead of browsing multiple links, shoppers receive a direct, synthesized answer—often including top recommended products, explanations, and cited sources.

In this model, the user isn’t searching—they’re asking, expecting a specific, contextually relevant answer that matches their intent.

Aspect	Traditional search	Conversational search
Query style	Short keyword strings (e.g., “best running shoes men”)	Full natural-language questions (e.g., “What are the best running shoes for men under $200?”)
User behavior	Users browse and compare multiple links	Users ask a question and expect a direct answer
Results format	List of ranked links (SERPs)	AI-generated response with recommendations and citations.
Discovery flow	Click → scan → compare → decide	Ask → receive → evaluate → decide
Role of product data	Supports ranking via keywords and SEO	Must be structured, complete, and context-rich for AI interpretation

The average AI search query is now 7.22 words long, showing how users are moving toward more detailed, conversational inputs. 

Source: Semrush

Conversational search is already embedded in mainstream platforms where consumers discover products:

• ChatGPT
• Perplexity
• Google AI Overviews
• Google AI Mode
• Amazon Rufus

These platforms are operating at a massive scale:

• Google AI Overviews reach over 2 billion monthly users, making AI-generated answers a primary discovery layer for search.
• Perplexity processed 780 million queries in May 2025 alone, showing rapid adoption of AI-native search behavior. 

AI tools are rapidly becoming the core entry points for product discovery.

How is consumer shopping behavior shifting?

Consumer shopping behavior is evolving quickly as AI becomes part of everyday discovery. Instead of browsing product pages or comparing links, shoppers are increasingly asking AI systems for recommendations and acting on those answers.

Shoppers are moving away from keyword strings

Search behavior is becoming more conversational and intent-driven. According to the Bloomreach survey:

• 54% of consumers say their search habits have become more conversational in the past 12 months.
• 1 in 3 shoppers now use full-sentence questions when searching.
• 93% said that it’s crucial for the e-commerce search to understand their conversational queries.
• Over 60% of consumers have used AI assistants like ChatGPT or Gemini to help them through their shopping journey.

Source: Bloomreach

AI is entering the funnel earlier — and converting better

AI is no longer just a research tool; it’s becoming a starting point for product discovery.

• Shopping-related queries on ChatGPT doubled between January and June 2025.
• AI search traffic converts at 14.2%, compared to 2.8% from traditional search engines.

What shoppers expect from AI recommendations

As consumers rely more on AI, their expectations for results are rising. They ignore generic or vague recommendations and expect precise and relevant answers. A survey by Bloomreach indicates that 50% of consumers are more likely to use AI assistants that take preferences such as size, style, or fit into account.

AI systems prioritize products with complete, structured, and detailed data. Products with missing attributes or thin descriptions are less likely to be surfaced.

Why does conversational search create a visibility problem?

Unlike traditional search engines, AI systems don’t rank pages based on keywords or backlinks. Instead, they extract, interpret, and synthesize structured information to generate a single response.

It changes how products get surfaced:

• AI systems evaluate product attributes, context, and relevance.
• Queries are multi-factor by default (e.g., “under $200, lightweight, good for running”).
• If a product is missing even one key attribute, it may fail to match the query entirely.

That creates a filtering effect:

• Products with complete, structured data are easier for AI to interpret and cite.
• Products with thin or inconsistent descriptions are more likely to be skipped.

Even when your product is relevant, visibility doesn’t guarantee traffic. 93% of Google AI Mode sessions result in zero clicks to external websites, fundamentally changing how performance should be measured.

Source: Semrush

Traditional metrics like click-through rates, organic traffic, and page rankings are becoming less reliable indicators of visibility.

The key question is “Does your product appear in the AI-generated answers at all?”

If it doesn’t, your products are effectively invisible, regardless of how well your pages rank in traditional search.

What do retailers need to do differently?

Structure product data for how AI reads, not how humans browse

AI systems don’t interpret product pages the way humans do. They rely on structured, complete, and context-rich data to determine relevance, which makes it critical to have complete and accurate attributes, such as:

• Size, dimensions, and fit.
• Material and composition.
• Use case (e.g., “for running”, “outdoor use”).
• Occasion (e.g., travel, formal, everyday).
• Compatibility (e.g., devices, accessories, environments).

If any of these attributes are missing, your product may not match multi-factor queries.

Similarly, the structure of descriptions needs to change:

• Keyword-stuffed descriptions → optimized for ranking.
• Question-oriented descriptions → optimized for answering.

For instance, instead of writing “Lightweight running shoes men breathable”, you should structure the description to answer “What are the best lightweight shoes for running?” 

This approach aligns with Answer Engine Optimization (AEO), which focuses on making the content interpretable and usable by AI systems.

Think in queries, not keywords

You should shift from targeting keywords to understanding how your customers are asking questions. The process starts with mapping product categories to conversational queries:

• “Best laptop for remote work under $1,000.”
• “Comfortable chair cushion for home office.”
• “Moisturizer for sensitive skin in winter.”

After identifying the categories, these queries should shape:

• Product titles
• Descriptions
• Attribute fields
• FAQs and supporting content

The goal is to mirror how buyers naturally phrase their needs, so AI systems can match your products to those queries.

Maintain data quality at scale with agentic AI

The challenge isn’t just structuring the product data; it’s maintaining that quality across thousands (or millions) of SKUs.

Manual product catalog management can’t keep up with:

• Constant product updates.
• Expanding attribute requirements.
• Increasing query complexity in AI search.

However, fabric’s Product Agent can help you:

• Enrich product data with missing attributes.
• Standardize and structure catalog information.
• Continuously optimize data for AI-driven discovery.

By automating product enrichment at scale, fabric’s Product Agent ensures that your product data stays complete, consistent, and ready for AI retrieval.

Conversational search visibility starts with your product data

Consumer search behavior has drastically shifted from manually browsing links to asking AI for answers. Treating conversational search as a future trend can effectively cost you visibility as the market continues to evolve.

The rules of product discovery have changed. Visibility now depends on whether your product data is complete, structured, and aligned with how AI systems interpret queries, rather than on how well your pages rank.

To stay competitive in conversational search, you need to focus on and prioritize:

• Auditing product data against real queries: Map your product catalog to the conversational questions shoppers are asking today. Identify where your data fails to support those queries.
• Identifying critical attribute gaps: Look for missing or inconsistent attributes (e.g., size, use case, compatibility) that could prevent AI systems from selecting your products.
• Evaluating your ability to scale data quickly: Assess whether your current processes can maintain structured, high-quality product data across your entire catalog as complexity grows.

Take our free AI Search Assessment to understand the AI and conversational search readiness of your product catalog.

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