Ecommerce product data is becoming more important than ever.
For years, brands have focused on optimising product feeds around product titles, descriptions, categories, prices, availability and standard attributes such as size, colour and material.
But product discovery is changing.
Consumers are increasingly using natural, conversational queries to find products. Instead of searching for something as simple as “gold necklace,” a shopper might ask:
“What’s a lightweight gold necklace I can wear every day?”
Or:
“Show me a skincare product for dry skin that works well under makeup.”
These searches require more than a keyword match. They require an understanding of product attributes, context, use cases and shopper intent.
This is where Google’s Conversational Attributes become particularly relevant for ecommerce brands.
What Are Google’s Conversational Attributes?
Google’s Conversational Attributes are part of the broader evolution of product data as shopping experiences become more conversational and AI-driven.
Traditional product feeds primarily communicate structured information about a product. Conversational product information adds another layer of context that can help systems better understand how shoppers might describe or evaluate that product.
For ecommerce brands, the important point is not simply the introduction of another product-data capability.
The bigger shift is that product information increasingly needs to be understandable in the same way people naturally talk about products.
A product isn't just a collection of specifications.
It can be:
- Lightweight
- Everyday
- Travel-friendly
- Giftable
- Minimalist
- Statement-making
- Suitable for formal occasions
- Designed for sensitive skin
- Ideal for small spaces
These contextual characteristics can influence whether a product is relevant to a particular shopping query.
Why Product Context Matters for AI Shopping
Traditional search often works around keywords.
AI-powered shopping experiences work differently.
A shopper might describe a need, preference or situation without knowing the exact product name or technical terminology.
For example:
“I’m looking for a simple gold bracelet for everyday wear that would also make a good gift.”
To provide useful results, an AI shopping system needs to understand several layers of information:
Product → Attributes → Context → Use Case → Intent
The more clearly those relationships are represented in product data, the easier it becomes for systems to determine product relevance.
This is why ecommerce brands should start thinking about their product feed as more than a technical requirement for Google Merchant Center.
It is becoming part of their product knowledge layer.
From Keyword Optimisation to Context Optimisation
For a long time, ecommerce SEO and product-feed optimisation have focused heavily on keywords.
That still matters.
However, AI-powered discovery introduces another question:
Does your product data explain why the product is relevant?
Consider a jewellery product.
A traditional product title might be:
18K Gold Diamond Pendant Necklace
Useful, but limited.
Additional product information could communicate:
- Lightweight design
- Everyday wear
- Minimal styling
- Suitable for gifting
- Office-friendly
- Layering-friendly
- Festive or occasion-based use
These aren't simply additional marketing phrases.
They provide context that can help connect a product with different types of shopper intent.
At NOIR & BLANCO, we see this as a fundamental shift in ecommerce optimisation: brands need to make their product information understandable, not just searchable.
What This Means for Shopify Brands
For Shopify merchants, the opportunity goes beyond making changes inside Google Merchant Center.
The underlying product data on the Shopify store also matters.
Brands should look at their entire product-data ecosystem:
1. Product Titles
Titles should clearly communicate what the product actually is while incorporating the attributes shoppers are likely to use when searching.
2. Product Descriptions
Descriptions should provide useful product context rather than relying entirely on generic marketing copy.
3. Product Attributes
Important characteristics such as material, size, colour, fit, style and other relevant properties should be structured consistently.
4. Product Taxonomy
Products should sit within clear and logical categories.
A well-structured taxonomy helps both search systems and AI systems understand relationships between products.
5. Use Cases and Occasions
Where relevant, brands should communicate how and when a product can be used.
For fashion, this could include occasions, styling and fit.
For beauty, it could include skin type, routine or application context.
For jewellery, it could include gifting, occasions, styling and everyday wear.
6. Customer Language
Brands should also consider how customers actually describe their needs.
The words used by customers in reviews, search queries, customer-service conversations and product questions can reveal useful contextual attributes that may not exist in the current product feed.
Conversational Attributes Are Part of a Bigger Change
It would be easy to look at Conversational Attributes as another Google product-feed update.
But the bigger story is AI-powered commerce.
Search is becoming more conversational.
Shopping is becoming more conversational.
And increasingly, AI systems are helping shoppers discover, compare and evaluate products.
This creates a new challenge for ecommerce brands.
Their product data needs to work across multiple discovery environments rather than being optimised for one search interface.
That includes:
- Google Search
- Google Shopping
- AI-powered search
- AI shopping assistants
- Chat-based product discovery
- Emerging agentic commerce experiences
The common requirement across these environments is high-quality, structured and contextual product information.
What Ecommerce Brands Should Do Now
Brands don't necessarily need to completely rebuild their product feeds.
A better starting point is a product-data audit.
Ask:
Can an AI system clearly understand what this product is?
Can it understand who the product is relevant to?
Can it understand when or why someone would use it?
Are the important attributes consistently structured?
Does the product information reflect the language customers actually use?
Is the same product information consistent across Shopify, Merchant Center and other commerce platforms?
If the answer to these questions is no, there is an opportunity to improve the underlying product-data structure.
The NOIR & BLANCO Perspective
We believe the next phase of ecommerce optimisation will move beyond simply making websites and product feeds search-friendly.
They will need to become AI-readable and context-rich.
For brands, this means treating product data as an important digital commerce asset rather than something that simply exists to populate a feed.
A product catalogue should communicate more than:
What is this product?
It should also help answer:
Who is it for?
What makes it different?
When would someone use it?
What problem or intent does it address?
What other products or attributes is it related to?
That becomes particularly important for brands operating in categories such as jewellery, fashion, beauty and luxury, where purchase decisions are often driven by context, preference and intent rather than specifications alone.
Preparing Product Data for AI Shopping
The rise of conversational shopping doesn't mean traditional SEO or product-feed optimisation is becoming irrelevant.
It means the scope is expanding.
Brands still need accurate titles, descriptions, pricing, availability, categories and structured attributes.
But they increasingly need to add context, relationships and intent to that foundation.
The brands that invest in this now can build product data that is useful across both traditional search and emerging AI-driven shopping experiences.
And as AI agents become more involved in product discovery and commerce, that foundation could become even more important.
The Bottom Line
Google's Conversational Attributes are one part of a much larger evolution in ecommerce product discovery.
The fundamental shift is from keyword-focused product information to context-rich product understanding.
For ecommerce brands, the question is no longer simply:
“Can Google find my product?”
It is increasingly:
“Can AI understand my product well enough to know when it is relevant?”
That is the direction product data is heading, and brands that start building richer, more structured and context-aware product information now will be better prepared for the next generation of AI-powered shopping.
Comments
Post a Comment