Retail is moving beyond clicks and carts into a world where shoppers interact with brands through conversations. Whether it is a chatbot on a retailer’s website, a voice assistant on a smartphone, or a social commerce app, the expectation is the same: shoppers want fast, accurate answers that feel natural. This is what we call conversational commerce, and it is reshaping how people discover and buy products.

But reality has not yet caught up with the promise. Too many retail chatbots frustrate customers, voice assistants give irrelevant answers, and social shopping tools leave gaps in the buying journey. The reason is not a lack of ambition or technology, it is the absence of AI-ready product data. Without enriched and structured catalogs, conversational systems simply do not have the fuel they need to succeed.

Why Most Retail Chatbots Fail

Retailers often launch chatbots quickly to keep up with competitors or meet customer service expectations 24/7 with a low budget impact. However, when these bots cannot answer basic product questions or give misleading recommendations, the experience damages trust with shoppers instead of building it. Below are some example of how chatbots get it wrong, and it happens every day.

Ambiguous Answers: A customer asks about “dairy free yogurt” and the bot returns generic yogurt products because dietary attributes were never captured. ‍

Limited Vocabulary: Bots fail to recognize synonyms or everyday language, such as “jumper” vs. “sweater,” when taxonomy is not standardized.

Missing Context: Without use case or compatibility attributes, bots cannot guide customers toward the right product for their needs.

Broken Escalations: Bots without structured product data often escalate too many queries to human agents, increasing costs rather than reducing them.

These failures highlight the importance of product data quality. Chatbots are only as smart as the catalog they sit on top of.

Data Requirements for Conversational Commerce

To deliver experiences that feel natural and useful, conversational commerce platforms require enriched product data that mirrors the way shoppers think and speak. Retailers must ensure that basic requirements are met before rolling out these systems or they'll suffer the same fate as so many failures before them.

• Benefit-Led Descriptions: Product copy must explain not just what an item is, but why it matters. Bots rely on this information to answer shopper questions. ‍
• Compatibility and Use Case Details: Shoppers ask about “phone cases that fit iPhone 15” or “detergent safe for wool.” Without explicit compatibility attributes, bots cannot respond accurately. ‍
• Structured Taxonomy: Standardized categories and attributes allow conversational systems to recognize synonyms and interpret intent. ‍
• Localization: Conversational queries vary across regions and languages. Product data must be enriched with local terminology and context. ‍
• Schema and Metadata: Structured feeds ensure bots and voice systems can map customer queries to the right product attributes.

These requirements are not optional extras, they are the foundation of making conversational commerce viable at scale.

Examples: Grocery, Fashion, and Electronics Voice Queries

Conversational commerce is not theoretical. Shoppers already interact with retailers across categories through voice and chat, and the data gaps are easy to see when experiences fail.

Grocery‍

Customers asking for “gluten free bread” often receive generic bread options if dietary attributes are not included in the catalog.

Fashion‍

Shoppers looking for “waterproof trail boots for women” may get irrelevant results if size and gender attributes are incomplete. ‍

Electronics‍

A query like “wireless headphones under $100 with noise canceling” cannot be answered correctly unless price, feature, and compatibility details are structured in the product data.

These examples make clear that conversational commerce is not about flashy AI models. It is about having the right data so those models can work as intended.

Preparing Your Catalog for Conversational AI

Retailers must see conversational commerce as part of a larger AI readiness journey. Without preparation, pilots will disappoint and waste resources. The result is the real impact of AI never being felt due tot he pilot never getting off the ground in the first place. On the flip side, conversational systems can become powerful tools for engagement and sales when the right steps and preparation arefollowed leading into their rollout.

1. Audit Product Copy: Ensure descriptions are benefit-led and conversational in tone. ‍
2. Enrich Attributes: Add missing details for compatibility, use cases, dietary preferences, and contextual benefits. ‍
3. Normalize Taxonomy: Standardize categories and synonyms to align with natural language. ‍
4. Localize Data: Adapt catalogs for regional vocabulary, units, and cultural context. ‍
5. Embed Governance: Create processes for continuous improvement, ensuring bots stay accurate as catalogs evolve.

This preparation transforms conversational commerce from a risky experiment into a reliable growth channel.

Conversations Need Clean Data to Work

Conversational commerce is one of the most exciting frontiers in retail, but it will not succeed unless product data is enriched, structured, and ready for natural language interactions. Customers expect accuracy and immediacy when they ask a bot or voice assistant for help. If the system cannot deliver, they abandon both the experience and the brand.

For executives, the takeaway is clear. Invest in product data quality before scaling conversational commerce. Doing so reduces pilot risk, builds customer trust, and ensures that AI-driven interactions translate into measurable business outcomes.

Explore our AI-Readiness for Retail Guide to see how structured data underpins conversational AI.