Use Claude to automate your Amazon analysis
In this webinar, Rolando Galeana, Marketing Manager at Intentwise, hosted Kenton Snyder, Product Manager at Intentwise, for a hands-on workshop on using Claude to automate Amazon analysis workflows.
The session focused on a practical question for brands and agencies: how can AI help teams save time, catch performance issues faster, and turn Amazon data into more actionable insights?
Rather than using AI for one-off questions or manual spreadsheet reviews, Kenton showed how Claude can become an always-on auditing layer across an Amazon account or client accounts.
Why Amazon analysis is hard to keep up with
Amazon teams spend a lot of time reviewing performance data, checking campaign trends, monitoring inventory, and looking for changes that need attention.
That work can be time-consuming, especially when teams are managing multiple campaigns, products, or client accounts. It is easy to miss targeting opportunities, overspend on low-performing keywords, or react too late to inventory and performance changes.
Kenton framed the challenge around three core problems:
- Teams spend hours every week reviewing performance manually.
- Anomalies can go unnoticed for too long.
- The time between spotting an issue and taking action is often slower than it should be.
Claude can help reduce that gap by surfacing the most important changes, flagging risks, and helping teams prioritize what to do next.
Start with workflows you already run
One of the key points from the session was that teams do not need to start by building something overly complex.
Instead of trying to create an AI agent that handles everything, Kenton recommended starting with recurring workflows your team already runs.
Good starting points include:
- Weekly Amazon account health checks
- Campaign performance reviews
- Low-inventory ASIN alerts
- Budget pacing reviews
- Keyword and campaign anomaly detection
- Brand vs. non-brand performance analysis
- Wasted spend checks
- Recurring client or internal reporting
The best workflows are repeatable, easy to explain in plain English, and tied to a clear output.
What makes a strong AI workflow
Kenton outlined four key pieces that every useful AI workflow needs.
First is context. Claude needs to understand the business, including key products, goals, ACOS or ROAS targets, launch priorities, seasonal trends, and anything else that affects how performance should be interpreted.
Second is strong data. If the data is inconsistent or incomplete, the output will be harder to trust. This is where a direct connection to clean Amazon data becomes important.
Third is a defined output. Teams should be clear on what they want Claude to create, such as a Slack alert, a dashboard-style artifact, a summary, or a weekly report.
Fourth is an actionable result. The output should not just summarize what happened. It should help the team understand what needs attention and what action to consider next.
Building a Claude project for Amazon analysis
During the live demo, Kenton walked through how to create a Claude project for a weekly Amazon health audit.
The example workflow pulled Amazon performance and inventory data, reviewed week-over-week trends, surfaced campaign and keyword anomalies, identified low-inventory products, and created a summary of recommended actions.
The workflow was designed to run on a schedule, allowing Claude to produce recurring analysis without requiring the user to start from scratch each week.
Kenton also showed how Claude can save trends and feedback into project memory, which helps the workflow become more useful over time.
Using Slack and artifacts for easier reporting
The session also showed how Claude can send summarized findings into Slack, making it easier for teams to review insights where they already work.
Instead of asking teams to dig through dashboards or read long reports, Claude can provide a concise summary of the most important changes, including performance alerts, inventory risks, and recommended next steps.
Claude can also generate artifacts that act like lightweight dashboards or reports, giving users a structured view of account health and performance trends.
Why connected data matters
Kenton explained that teams can upload CSV files from Amazon as a starting point, but manual uploads create extra work and may require more verification.
A stronger approach is using an MCP connection, such as Intentwise AI Gateway, to connect Amazon data directly into Claude.
With a direct connection, teams can build scheduled workflows that pull consistent data automatically, making the output more reliable and easier to operationalize.
Common audience questions
The session included several questions from attendees, including:
- How do I connect Claude to Intentwise?
- What should I upload if I cannot connect Amazon data directly?
- When should I use Claude Projects vs. Claude Chat?
- How should teams think about data security?
- Can Claude account for manual optimizations or recent account changes?
- Can outputs be updated if the original instructions are too general?
- Can findings be shared with a team through Slack or PDF?
These questions reflected a clear theme: brands and agencies are interested in using AI for Amazon analysis, but they need practical guidance on setup, security, workflow design, and data access.
Key takeaways
- Claude can help automate recurring Amazon analysis workflows.
- The best place to start is with tasks your team already does regularly.
- Strong workflows need context, reliable data, defined outputs, and actionable recommendations.
- Claude Projects can help preserve workflow-specific context and memory over time.
- Scheduled tasks can turn manual reporting into recurring analysis.
- Slack outputs and artifacts can make insights easier for teams to consume.
- Connected data through tools like Intentwise AI Gateway can make workflows more consistent than manual CSV uploads.
Final thoughts
The main takeaway from the session was simple: AI works best when it is applied to real workflows teams already run.
For Amazon brands and agencies, Claude can help reduce manual analysis, improve visibility into account changes, and surface issues that need attention faster.
The best next step is to choose one recurring workflow, define the context and output, connect the right data, and start testing.
