Introduction
Brands are no longer operating in a single-channel world. Even if Amazon remains the center of your ecommerce business, your team may also be managing Walmart, Criteo, TikTok, Shopify, Target, and other channels.
That creates a familiar problem: the data is everywhere.
In this webinar, Kenton Snyder, Product Manager at Intentwise, walks through how teams can use Claude for cross-channel commerce analysis. The session shows how AI can help commerce teams bring performance data together, ask better questions, build dashboards, and identify opportunities across channels without spending hours downloading CSVs and stitching reports together manually.
What this webinar covers
This session focuses on how AI can help teams move from fragmented reporting to connected commerce analysis.
Kenton walks through practical examples using Claude and Intentwise AI Gateway to analyze performance across Amazon, Walmart, and Criteo. The goal is to show how teams can use natural-language prompts to understand what is happening across channels, where performance is changing, and what actions to take next.
The webinar covers three core use cases:
- Building cross-channel dashboards with AI
- Asking natural-language questions across Amazon, Walmart, and Criteo
- Finding optimization opportunities and turning them into a weekly action plan
Why cross-channel analysis is getting harder
Most ecommerce teams already know how messy Amazon reporting can be on its own. Ads data, inventory, finance, FBA, retail signals, and performance reports often live in different places.
Once you add channels like Walmart, Criteo, TikTok, Shopify, or Target, the complexity grows quickly.
Teams are not just trying to analyze data within one channel. They are trying to understand performance across channels. That means answering questions like:
Which channel is overinvested or underinvested?
Where is spend increasing without enough return?
Which keywords are working on one marketplace but missing from another?
Which products are performing well in Criteo but need more support elsewhere?
What changed this week, and what should we do about it?
This is where tools like Claude become more useful, especially when they can access structured commerce data through an MCP connection like Intentwise AI Gateway.
Building dashboards with Claude
One of the first examples in the session focuses on dashboarding.
Instead of building separate views for Amazon, Walmart, and Criteo, Kenton shows how Claude can help create a connected view across channels. In the example, Claude is used to compare spend, sales, and ROAS across multiple platforms so the team can quickly see where the brand may be overinvested or underinvested.
The key takeaway: AI can help teams create visuals from natural-language prompts, especially when the underlying data is already connected and structured.
A simple prompt might look like:
Build a live dashboard showing spend, sales, and ROAS across Amazon, Walmart, and Criteo for the last 30 days. Highlight which channels appear overinvested or underinvested.
Asking better performance questions
The session also shows how teams can use Claude to ask practical commerce questions across channels.
Examples include:
Which Amazon keywords are spending money but not converting?
Which of those keywords are performing on Walmart?
Why did Walmart sales change week over week?
What are my top products by revenue on Criteo?
Which high-performing Amazon keywords are not currently running on Walmart?
These questions are valuable because they help teams move beyond static reporting. Instead of only seeing what happened, teams can start asking why it happened and what should happen next.
Finding cross-channel opportunities
One of the strongest use cases from the webinar is opportunity discovery.
Kenton shows how Claude can identify keywords that are performing well on Amazon but are not currently being used on Walmart. That creates an immediate list of opportunities that a team can activate in another channel.
The same logic can apply across products, campaigns, audiences, budget allocation, and inventory planning.
For example, if a keyword is performing well on Walmart but not Amazon, the next question becomes: is this a listing issue, a campaign issue, or a product-market fit issue on that channel?
AI helps teams ask these follow-up questions faster and organize the answers into an action plan.
Turning insights into an action plan
A major theme from the webinar is that analysis is only useful if it leads to action.
After running several cross-channel prompts, Kenton asks Claude to create a weekly action plan. This type of prompt can summarize the key moves by channel, identify expected impact, and help the team track what was changed.
A prompt like this can be especially useful:
Based on everything we reviewed in this chat, write an action plan for this week. Include the channel, the recommended move, and the expected impact.
This gives teams a practical hit list, such as pausing non-converting keywords, launching high-performing keywords in another channel, shifting budget toward stronger products, or investigating listings where performance differs by marketplace.
Why data connection matters
A major takeaway from the Q&A was that how you connect data to AI matters.
Uploading CSV files can work for basic analysis, but it can also create challenges around security, speed, context size, and manual effort. Kenton explained that an MCP connection, like Intentwise AI Gateway, gives Claude access to the relevant data without requiring teams to manually upload files each time.
That means AI workflows can become more scalable, more repeatable, and easier to run on a schedule.
Automating recurring analysis
The webinar also covered how teams can schedule recurring prompts inside Claude.
For example, a team could create a recurring task that runs every week and surfaces cross-channel keyword opportunities, budget shifts, or performance changes. Instead of manually prompting Claude every time, teams can start building recurring workflows that deliver insights on a set schedule.
This is where AI starts to become more than an ad hoc assistant. It becomes part of the weekly operating rhythm for ecommerce teams.
Key takeaways
Cross-channel commerce analysis is becoming more important as brands operate across Amazon, Walmart, Criteo, TikTok, Shopify, Target, and other channels.
AI tools like Claude can help teams build dashboards, ask natural-language questions, find opportunities, and summarize weekly action plans.
The most valuable workflows are not generic AI prompts. They are commerce-specific workflows connected to real performance data.
MCP connections can make these workflows more secure, scalable, and repeatable than manual CSV uploads.
The next step for many teams is to identify the recurring questions they ask every week and turn those into AI-powered workflows.
