How 3 businesses sped up Amazon analytics with Claude

One Friday night, Tiide Commerce founder Mark Lathrum was at dinner when a major client texted him with alarming news: sales on a top SKU were inexplicably plummeting.

This SKU, which normally brings in $1m in sales per month, was suddenly seeing sales numbers drop 30-40%.

Lathrum was at dinner, but he knew this wasn’t a problem he could wait to resolve. He pulled out his phone, and asked Claude to identify the possible reasons why that SKU was suddenly seeing low revenue. 

Lathrum then pieced together the problem: the SKU had flipped from FBA to FBM. 

He texted back the client within 10 or 15 minutes, and was able to return to dinner, knowing he had resolved a problem that, a year ago, could have taken hours of frantic searching in reports to unravel. 

This is what work can look like when you have a sophisticated MCP connecting your commerce data to Claude or Copilot: answers to deep questions about performance swings or shopper trends can be returned within minutes. 

Today, we’re highlighting a few stories from clients who use our AI Gateway MCP, to illustrate the immense value that comes when you fix your data fragmentation woes. 

How do you choose the right MCP for your commerce data? 

Let’s first take a moment to frame the problem. 

A lot of companies are now talking about MCP connections to AI agents. Securely funneling your data into Claude, Copilot, or wherever you work is exciting—but you don’t want to rely on just any MCP connection. 

The magic of Intentwise is that our data infrastructure is designed to fix fragmented data gaps. 

Our proprietary data model finds the connections between all of your data sets, across all of your channels. 

Meanwhile, our query engine sharpens your questions and returns the answers faster, in a self-improving loop. 

We also embed industry-specific and brand-specific knowledge, so your agent can really reason through your query. 

That foundation allows Claude to answer your questions far faster, and with greater accuracy. 

Because of these built-in audit and self-improvement capabilities, our MCP also speeds up the gap between prompt and response, ensuring our system points Claude to the answer to your questions far faster, with fewer tokens burned. 

How are brand and agencies using Claude for commerce analytics today? 

To give you a sense of what’s possible with MCPs today, here are a few real-world stories our clients have told us. 

Tiide Commerce: In addition to resolving customer problems overnight, as we discussed in the intro, Tiide Commerce has used our AI Gateway MCP to set up workflows, build decks automatically, create performance alerts, and run deep diagnostics. 

With our MCP, the Tiide team can automate the deck-building process. 

Tiide can make lengthy client decks, customized to the needs and most essential data points for each client, that Claude automatically builds with new data ahead of client calls. 

(Read the full case study here to learn more about how Tiide Commerce utilized AI Gateway.) 

Franklin Sports: For this sports brand, the magic of Claude came from its ability to upskill lower-level employees. 

With the backing of Intentwise’s knowledge layer—our data model, semantic layer, query engine, and more—Franklin Sports knows that Claude will answer questions with expert-level sophistication, accuracy, and speed. 

That means the agency can trust it to offer sharp responses to entry-level employees. 

Now, when an entry-level employee notices a performance swing in a client account, the employee can ask Claude deep questions about what went wrong. 

They can have a back-and-forth conversation with Claude about where the problem is coming from, and whether action needs to be taken. 

Employees can then come to the rest of the team with a sophisticated explanation of what changed and why. 

New actions can be taken faster, with new employees able to implement changes far faster than before.

(Read the full case study here.) 

eAccountable: This digital marketing agency began using our data foundation because it wanted an Amazon Marketing Cloud solution more sophisticated than just a generic query library. 

In particular, the agency wanted to combine multiple query outputs together to create a granular view of client performance. 

We’re talking LTV-to-CAC ratios, and 12-month cohort matrices that measure how shoppers behave from month to month for specific ASINs. 

Intentwise’s powerful foundation seamlessly connects all of these outputs together, making it easy to tie together queries into one output, and build custom views on top of it. 

When eAccountable utilized Intentwise’s MCP to connect all of its Amazon data, including its Amazon Marketing Cloud data, to Claude, that interconnected view of client performance only deepened. 

Claude could now trend query data—to show, for example, how LTV or CAC changed over time for specific products. 

Or it can answer thoughtful questions across search terms, Share of Voice, and AMC-derived insights. 

Our MCP saved eAccountable 50% of its time, which it could then re-allocated into the granular, strategic thinking that sets it apart from the rest of its clients. 

(Read the full case study here.) 

How else can I use Claude across my Amazon business? 

Above, we highlighted a few stories from our actual Intentwise clients, but we also have a ton of resources about AI use cases for brands and agencies. 

Download our free whitepaper guides, for instance, to see how to use Claude to: 

• Analyze, and grow, your Subscribe & Save numbers
• Build Amazon performance decks
• Assess the reasons behind ad performance swings

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