Introduction
Amazon shopping is changing. Keywords still matter, but they are no longer the full story. As Amazon continues to build AI-assisted discovery experiences through tools like Alexa Shopping, Rufus, and Cosmo, brands need to think more deeply about the customer intent behind each search.
In this webinar, Jerry Vida, Co-Founder and Head of Amazon Strategy at Peak ROAS, joined Intentwise to walk through how brands can rethink the Amazon path to purchase in an AI-driven shopping environment.
The core message was clear: the opportunity is not just collecting more data. The opportunity is connecting the data you already have and using it to build listings, creative, and ad strategies around buyer intent.
The Click Is Not the Beginning of the Customer Journey
Jerry emphasized that the click is not the start of the customer journey. It is the middle.
Before a shopper clicks, they already have a need, a problem, or a specific intent. After they click, the product detail page needs to confirm that the product is relevant, credible, and worth buying.
That means brands need to think about the full path to purchase:
- What the shopper is searching for
- What problem they are trying to solve
- What questions they need answered before buying
- What proof they need to feel confident
- What friction might stop them from converting
For Amazon brands, this means the listing, images, A+ content, backend fields, and advertising structure all need to work together around the same buyer intent.
Amazon Already Gives Brands the Signals
A major theme of the session was that brands already have access to many of the data points needed to understand buyer intent.
Jerry pointed to several Amazon data sources that can help brands build a clearer picture of the customer journey, including:
- Search Query Performance
- Search Catalog Performance
- Brand Analytics
- Market Basket Analysis
- Repeat Purchase Behavior
- Demographics
- Business Reports
- Advertising Reports
- Bulk Files
- Inventory Reports
The challenge is that these reports are often disconnected. Each one shows part of the story, but none of them tells the full story on its own.
When brands connect these reports, they can better understand what customers search, where demand exists, where conversions break, and what needs to change to improve performance.
Build Listings Around the Ideal Customer
One of Jerry’s key recommendations was to build Amazon listings around the ideal customer, not just around keywords.
He explained that two shoppers can search for similar products but care about very different things. For example, two people might be interested in the same vehicle, but one might care about safety, storage, and gas mileage, while the other cares about towing capacity, ground clearance, and performance.
The same logic applies to Amazon products.
Brands need to understand who the product is really for, then use that understanding to answer buyer decision questions throughout the listing. Those questions might relate to use case, product benefits, safety, ingredients, durability, size, compatibility, value, or proof.
The goal is to reduce purchase friction by giving the right shopper the right information at the right moment.
AI Shopping Requires Intent-Based Listing Optimization
The old approach to Amazon listing optimization was heavily keyword-driven. Brands would identify relevant keywords, add them to the title, bullets, backend fields, and content, then hope that shoppers found and purchased the product.
That approach still has value, but Jerry explained that Amazon’s AI-driven discovery systems are increasingly focused on context and intent.
Instead of only optimizing for keywords, brands need to optimize for the customer behind the keyword.
That means making the product easier for Amazon’s AI systems to understand. The listing should clearly communicate what the product is, who it is for, what problem it solves, when it should be used, and why it is relevant to a specific shopper.
PDP, Creative, and Ads Need to Align
Jerry described Amazon performance like a diamond, with multiple facets that all need to work together. The PDP, image stack, A+ content, backend data, advertising account, inventory, pricing, and promotional strategy all influence performance.
Two of the most important pieces are the listing and the advertising structure.
The listing needs to answer buyer decision questions. The image stack should visually reinforce those answers. A+ content should expand the product story. Backend fields should give Amazon more context. Advertising should route the right search intent to the right product message.
When all of those pieces are aligned, shoppers experience continuity from search to click to purchase decision.
Semantic Relationships Matter for AI Discovery
A key part of the discussion focused on semantic relationships and how Amazon’s AI systems understand products.
Jerry explained that Amazon’s AI-assisted shopping experiences are looking beyond simple keywords. They need to understand relationships like:
- What function does the product perform?
- What problem does it solve?
- Who is it for?
- When is it used?
- What audience or interest does it connect to?
- What attributes make it relevant?
For example, if a shopper asks Alexa Shopping for a cleaner for an electric smoker, Amazon needs to understand which products are degreasers, what surfaces they are meant for, and whether they are relevant to that use case.
That is why listings need to be structured in a way that helps Amazon understand the product’s function, use case, audience, and relevance.
AI Can Help Turn Amazon Data Into Listing and Creative Direction
Jerry also shared how Peak ROAS uses AI tools like ChatGPT and Claude to help process Amazon data and generate optimization outputs.
The process includes feeding AI tools inputs such as the current listing, brand website, Amazon Store, competitor listings, Search Query Performance data, demographics, Market Basket data, advertising data, bulk files, and other reports.
From there, the AI workflow can help generate:
- Positioning documents
- Competitive advantage analysis
- PDP copy recommendations
- Title and bullet point direction
- Image stack design briefs
- A+ content briefs
- Backend optimization guidance
- Semantic relationship mapping
The goal is not to replace strategy, but to make the analysis more repeatable and structured.
New Listings Still Need Testing and Iteration
During the Q&A, Jerry addressed how this process applies to new listings or new variations.
For seasoned ASINs, brands can rely more heavily on existing Amazon data. For new products, brands may need to start with competitor research, third-party tools, customer research, and assumptions around the ideal customer profile.
Either way, the process should not be treated as a one-time setup. Brands should run periodic audits, review performance, identify gaps, and continue refining the listing over time.
The AI-driven path to purchase is iterative. Brands should launch, gather data, evaluate performance, and improve.
Key Takeaways
The biggest takeaway from the webinar is that Amazon brands need to move beyond keyword stuffing and toward intent-based optimization.
As AI-assisted shopping becomes more common, the brands that win will be the ones that make it easier for Amazon to understand what their products are, who they are for, and why they should be recommended.
For brands looking to adapt, the key steps are:
- Connect the Amazon data you already have
- Build around the ideal customer profile
- Identify and answer buyer decision questions
- Align ads, PDP copy, images, A+ content, and backend fields
- Optimize for buyer intent, not just keywords
- Use AI to make analysis and creative direction more repeatable
- Test, audit, and refine over time
AI shopping is adding a new layer to the Amazon path to purchase. Brands that understand this shift can improve discoverability, reduce purchase friction, and create listings that are better aligned with how shoppers make decisions today.
