Increase in return on ad spend (ROAS) through standardized measurement across brands and campaigns
Improvement in campaign conversion efficiency by optimizing audience segments using AMC insights
Reduction in manual reporting effort through automated data pipelines and unified dashboards
What Happens When Amazon Ad Data Finally Tells a Clear Story
Analytic Vizion partnered with a large enterprise retailer to address a growing challenge: heavy investment in Amazon advertising without a clear understanding of its true impact. With campaign performance, customer data, and sales signals spread across systems, the client’s analytics and marketing teams lacked confidence in their KPIs, especially Return on Ad Spend (ROAS).
Together, the client and Analytic Vizion used Amazon Marketing Cloud as a proof of concept to unify fragmented datasets, align on what “success” actually meant, and create a clearer view of customer behavior and campaign effectiveness. The result wasn’t just better reporting, but stronger adoption, more confident decision-making, and a scalable foundation for measuring what truly matters in marketing analytics.
The Challenge
A large retailer with multiple consumer brands was spending heavily on Amazon advertising without fully understanding its performance. Although Amazon provided clean room access via Amazon Marketing Cloud (AMC), the organization lacked the internal expertise to translate this complex data into actionable insights. The client’s analytics and marketing teams faced several key hurdles:
- Limited understanding of AMC’s data model and clean room architecture
- Fragmented reporting across tools (e.g., QuickSight vs. Power BI)
- No centralized, automated pipeline for ongoing data refreshes
- Inconsistent metrics definitions across brands and business units
- Inability to compare ROAS across segments or optimize campaigns based on behavioral data
These gaps prevented marketing and analytics leaders from understanding customer behavior, segmenting audiences effectively, and justifying ad spend with measurable returns.
Business stakeholders responded by becoming much more strategic. They could finally design future campaigns based on past performance, negotiate smarter on cost structures, and use AI/ML models to target the right customers.”
Our Approach
Analytic Vizion partnered with the retailer to activate AMC data within its enterprise environment, working directly with stakeholders across marketing, analytics, and operations. The engagement followed Analytic Vizion’s people-first approach, starting with stakeholder alignment and success metric definition, then executing with process discipline and technology integration.
Key steps included:
Change Management via user acceptance testing (UAT), documentation, and stakeholder training
Stakeholder Workshops to align on KPIs, brand segmentation needs, and desired outcomes
Data Strategy Definition to map AMC outputs to enterprise data standards
Automated Pipeline Development to move clean room data from AWS S3 into Snowflake
Custom Dashboard Buildout in Power BI aligned to brand-specific ROAS, funnel analysis, and audience segmentation
The How
Rather than relying on Amazon’s native QuickSight dashboards, Analytic Vizion re-platformed insights into the client’s enterprise toolset, namely Power BI and Snowflake. This allowed for greater customization, brand-level views, and scalable reporting.
- Data Movement: AMC query outputs were exported to AWS S3, then ingested into Snowflake for historical retention and daily refreshes.
- Orchestration: Python-based scripts and AWS-native tools like SageMaker and Jupyter notebooks were used to automate extraction and refresh logic.
- Governance: A multi-environment deployment pipeline (Dev > QA > UAT > Prod) ensured compliance and scalability across business units.
- Custom Metrics: Beyond ROAS, new KPIs were defined including purchase rate, detailed page view rate, conversion rate, and audience overlap by brand.
- Audience Segmentation: AI/ML models were used to define high-propensity customer groups, supporting targeted advertising campaigns.
Strategically, the project prioritized business alignment and iterative delivery. Early wins were delivered through phased dashboard releases, while maintaining a flexible roadmap for brand-specific enhancements.
Tools & Technology
The Results
Despite working with complex datasets and a fragmented starting point, the retailer quickly saw measurable gains:
Vendor Cost Negotiation: With better insight into platform performance (DSP vs. Sponsored Ads), the client was able to negotiate more favorable campaign terms with Amazon.
15% Increase in ROAS: Brands previously underperforming were able to optimize campaigns using funnel insights and behavioral metrics.
10-15% Growth in Loyalty Subscriptions: Subscription-based product lines saw improved engagement through targeted segmentation and campaign timing.
Faster Campaign Optimization: Automated daily refreshes reduced lag in reporting, giving teams real-time feedback on campaign performance.
Cross-Functional Enablement: Analytics and marketing teams gained shared dashboards, aligned KPIs, and the ability to jointly plan strategic ad spend.
“You need someone who understands both worlds: Amazon’s ecosystem—AMC, AWS, QuickSight—and the enterprise stack like Snowflake, Power BI, or Tableau, and can build a bridge between them. Beyond the technology, you need someone who can communicate clearly with marketing, IT, and senior leaders, translate business questions into data models and KPIs, and keep everyone aligned on outcomes all the way through.”
-Jignesh Patel
Senior Data Engineer, Analytic Vizion
Key Takeaways
Activate Clean Room Data Strategically
Integrating AMC data into your enterprise BI tools enables deeper, more customizable insights than using out-of-the-box dashboards alone.
Align on KPIs Early and Often
Success requires cross-functional agreement on what matters, whether it’s ROAS, conversion, or audience lift. Building that alignment upfront speeds delivery and adoption.
Automate for Scale and Refresh
Daily, automated pipelines ensure marketing insights stay current. This is crucial when attribution windows shift and historical data needs ongoing updates.
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