In this webinar, Kenton Snyder, Product Manager at Intentwise, explores how brands can use AI agents alongside Amazon Marketing Cloud (AMC) to move beyond one-off queries and turn AMC data into recurring, actionable insights.
AMC is powerful, but many brands still struggle to use it consistently. Queries can be technical to build, data is often fragmented across different reports, and analyzing results manually makes it difficult to turn AMC into a regular part of a brand’s reporting and optimization workflow. Kenton walks through how AI can help solve these challenges by combining AMC queries, connecting AMC with other Amazon datasets, and automating recurring analysis.
Combining multiple AMC queries
The first set of use cases focuses on bringing multiple AMC queries together to answer more strategic questions.
One example compares first-touch, last-touch, and linear-touch attribution. While standard Amazon Ads reporting tends to emphasize last-touch attribution, AMC can reveal how upper-funnel tactics such as DSP, Prime Video, and Twitch contribute earlier in the customer journey. Comparing attribution models can help brands identify which tactics are better at introducing customers to the brand versus driving the final conversion.
Kenton also demonstrates how brands can combine new-to-brand data with repeat-purchase and product-overlap data. This can reveal which products are most effective at acquiring new customers and which of those products subsequently drive purchases of complementary products. Those insights can inform both product prioritization and follow-up targeting strategies.
Another use case connects branded search behavior with DSP campaigns and paths to conversion. By combining branded-search rates with conversion-path data, brands can identify campaigns that are effective at generating branded demand and determine whether those searches ultimately lead to purchases.
Connecting AMC with other Amazon datasets
The next step is combining AMC with data outside of AMC.
Kenton highlights Search Query Performance (SQP) as one valuable example. SQP can contain thousands of search terms, making manual analysis difficult. By combining SQP with AMC data, brands can identify search terms where click share is higher than purchase share—or where purchase share is disproportionately strong—and then investigate why. Time-to-conversion and path-to-conversion data can provide additional context around whether customers are taking longer to convert or whether certain advertising exposures are influencing the outcome.
Another example combines AMC with advertising performance data to analyze campaign overlap. Instead of evaluating Sponsored Products, Sponsored Brands, and Sponsored Display independently, brands can examine how combinations of exposures affect ROAS, reach, and acquisition costs. This can reveal opportunities where a lower-spending ad type is contributing disproportionately to higher-performing customer journeys.
Kenton also demonstrates how AMC can be combined with Subscribe & Save data. Comparing ad-exposed and non-ad-exposed subscribers can help brands understand whether advertising is contributing to first and repeat subscriptions. Adding Subscribe & Save revenue trends makes it possible to identify whether changes in subscription revenue are connected to shifts in advertising-driven purchases.
Automating AMC analysis with AI
The webinar then moves from individual analyses to recurring, automated workflows.
Kenton shows how AI tools can create trend analyses for metrics such as new-to-brand performance. Rather than manually combining monthly AMC reports in Excel, an AI agent can aggregate the data, identify meaningful changes at the campaign or product level, and surface potential problem areas.
He also demonstrates how brands can monitor DSP frequency over time. AMC can help identify the exposure range where campaigns achieve the best balance between purchase rates and media costs. By connecting that analysis with DSP campaign settings, an AI workflow can flag when the optimal frequency appears to be shifting and recommend reviewing frequency caps.
Building recurring AI-powered reports
The final demo shows how to turn these workflows into scheduled tasks using Claude and an Intentwise MCP connection.
Kenton creates a Monthly New-to-Brand Analysis that pulls AMC data, trends new-to-brand performance at the campaign level, identifies where new-to-brand sales percentages are changing, and produces a report highlighting the key trends. The task can be scheduled to run automatically, allowing the analysis to be delivered weekly or monthly without manually executing and combining individual queries.
The resulting report combines new-to-brand purchases, product sales, campaign-level trends, advertising revenue, and suggested next steps. Because the workflow is prompt-driven, the report can also be modified simply by asking the AI agent to change what it analyzes or how the output is presented.
Key takeaway
The goal isn't simply to use AI to write AMC queries. The bigger opportunity is to use AI agents to connect multiple datasets, automate recurring analysis, identify trends, and turn AMC insights into decisions.
For brands, the most valuable workflows will ultimately be those built around their specific business questions—whether that's customer acquisition, attribution, repeat purchases, DSP optimization, branded demand, or subscription growth. By combining AMC with other Amazon data and automating the analysis, teams can make AMC a recurring part of their decision-making process rather than an occasional reporting exercise.
