Profound Shopping Analysis The New Front Door to Retail in the Age of AI
Artificial intelligence is no longer just answering questions. It is making shopping decisions. Customers tell an AI assistant what they need and the assistant decides which products to browse, how to describe them, and which brands to compare. Profound’s new feature Profound Shopping Analysis finally gives retailers visibility into that journey.
Index
- The new AI first shopper
- Why retailers have been flying blind
- What Profound Shopping Analysis reveals
- How AI chooses which products to recommend
- The rise of agentic commerce
- Why richer product content wins
- What this means for retailers and brands
- How to optimise for AI driven shopping
- Final thoughts
- Frequently asked questions
The new AI first shopper
The modern shopper does not start with a homepage or a category menu. They start with a question.
- What are the best carbon plate running shoes for a marathon
- What is a durable carry on backpack for weekend trips
- Which everyday sneakers are comfortable for flat feet
They ask these questions inside ChatGPT, Perplexity, Gemini, Copilot, or a retailer’s own assistant. The AI becomes a personal shopper. It decides which products to surface, which attributes to emphasise, and which brands to compare side by side.
In this world product discovery has moved from the retailer’s site into the AI interface. The first impression of your product is no longer a category grid. It is an AI generated recommendation.
Why retailers have been flying blind
Until now retailers had almost no visibility into this behaviour. They could not see:
- Which of their products appear inside AI shopping answers
- How often those products show up for key intents
- How the AI chooses to describe each item
- Which rival products appear in the same response
- Which images the AI displays when it highlights a product
This created a serious blind spot. AI systems were already shaping purchase decisions yet teams in ecommerce and merchandising could not measure or influence that moment.
Profound Shopping Analysis fixes this gap. It gives commercial teams clear sight lines into how AI engines present their catalogue and where they are losing ground to competitors.
What Profound Shopping Analysis reveals
Profound Shopping Analysis is a new way to understand product visibility inside AI driven shopping. For each product in a catalogue retailers can now see things like:
- How often the product appears in AI recommendations for specific intents or prompts
- Which assistants such as ChatGPT, Gemini, Perplexity, and Copilot are surfacing it
- How the product is described in natural language including benefits and use cases
- What other products appear alongside it in comparison lists
- Which images are used and how prominent the placement is
Profound turns opaque AI behaviour into concrete analytics that a merchandiser, category manager, or growth marketer can actually act on.
How AI chooses which products to recommend
Dylan Babbs, co founder at Profound and former leader at Uber, summarised the challenge simply. More customers now start with a question instead of a website. AI decides which products to browse and how to talk about them. That is the new purchase funnel.
Profound’s research across thousands of product pages points to a consistent pattern in how AI assistants pick winners. They pay close attention to:
- Richness of product content and depth of description
- Clarity of attributes such as weight, materials, fit, cushioning, or use case
- How well the content matches the intent behind the question
- Presence of structured data that makes it easy to compare products
- Evidence of differentiation such as unique technology, claims, or reviews
The more clearly a product page explains who the product is for and why it is a strong choice the more likely AI is to select it as a recommendation. Thin generic copy simply does not compete.
The rise of agentic commerce
Profound describes this new behaviour as part of a larger movement toward agentic commerce. Instead of clicking through filters or reading dozens of reviews users lean on an AI agent to do the work.
They expect that agent to:
- Understand their needs and constraints
- Scan the market and shortlist viable options
- Explain each pick in simple language
- Highlight trade offs between comfort, price, and performance
- Update recommendations in real time as the user adjusts constraints
In this flow the AI is the new category manager. It decides which products go on the shelf in front of the shopper. Profound Shopping Analysis is the first tool that lets brands see that virtual shelf.
Why richer product content wins
One of the most important insights from Profound’s early work is that richer product content wins more AI visibility.
Winning product pages tend to share common traits:
- High quality hero imagery plus detail shots that clearly show design, cushioning, and materials
- Descriptions that focus on benefits such as speed, stability, or all day comfort rather than only technical jargon
- Complete attribute sets including sizing, weight, heel to toe drop, and surface type
- Supportive content such as fit guides, use case suggestions, and care instructions
- Structured comparison blocks that make it easy to contrast models within a range
When AI systems see this level of clarity they can confidently state things like “The New Balance Fuelcell Max is a strong option for runners who want a fast shoe with a propulsive ride and a lightweight feel.” That kind of description only appears when the underlying content enables it.
What this means for retailers and brands
Retailers who adapt to this new reality will reach entirely new pools of demand. They will be the products that AI assistants suggest first to curious customers who are still early in their decision journey.
Retailers who ignore it risk quiet invisibility. Their products may still live on the site and in classic search results yet remain absent from the AI answers that shape the first impression.
Think about it this way. In the search era the goal was to rank near the top of the results page. In the AI era the goal is to be one of the few products an assistant is willing to describe by name and image.
How to optimise for AI driven shopping
Here are practical steps retailers can take right now that align with Profound’s findings.
Upgrade product descriptions
- Write in clear everyday language that focuses on benefits
- Answer questions such as who is this for, when should they use it, and what makes it different
- Avoid copy that feels like a template reused across the range
Complete every attribute
- Ensure technical specifications are accurate and consistent
- Include measurements, materials, cushioning levels, and use case tags
- Structure information so that it is easy for machines to read
Invest in better imagery
- Provide multiple angles showing key details and profile
- Use lifestyle shots that demonstrate real use cases
- Keep image quality high so that crops in AI interfaces still look sharp
Support the product with contextual content
- Add sizing and fit guides that help users choose the right option
- Include FAQs that mirror the questions people ask AI systems
- Provide comparison charts within a product family so AI can lift those distinctions
These improvements make the catalogue more helpful for humans and more intelligible for AI. Profound Shopping Analysis then shows whether those investments produce more visibility.
Final thoughts
Profound Shopping Analysis arrives at a perfect moment. Retailers are heading into high stakes periods such as Black Friday while customer behaviour is shifting toward AI first discovery. Having a real measurement layer for AI driven shopping is no longer a nice to have. It is essential.
Answer engines are becoming the new front door to retail. Profound is giving brands the analytics they need to stay visible at that door.
Full credit to co founder Dylan Babbs and the Profound team including Sherman Grewal, Joey Loi, Julia Moseyko, and Josh Blyskal for bringing this product to market in record time, and to Trishla Ostwal at ADWEEK for covering the launch.
Frequently asked questions
What is Profound Shopping Analysis
Profound Shopping Analysis is a feature from Profound that shows retailers how their products appear inside AI driven shopping experiences. It reveals which items surface, how often they are recommended, how they are described, and how they compare with competitor products.
Why does AI matter for retail shopping
More shoppers are asking AI assistants for product advice instead of browsing category pages. These assistants shortlist products, explain trade offs, and often shape the final purchase decision. If a product never appears in those answers it may never enter the customer’s consideration set.
Can retailers influence how AI describes their products
Yes. While retailers cannot directly control model outputs they can strongly influence them by improving their product content. Clear descriptions, complete attributes, rich imagery, and structured comparisons all help AI systems understand and accurately present a product.
What is agentic commerce
Agentic commerce describes shopping journeys where an AI agent actively helps the buyer by discovering, filtering, and explaining options on their behalf. The agent becomes a kind of digital retail associate that can search across many brands at once.
How should retailers get started
A good first step is to audit key product pages for content richness and clarity, then use Profound Shopping Analysis to see how those products currently appear in AI answers. From there teams can prioritise improvements to content and track whether AI visibility improves over time.
