The 2026 reporting stack for Amazon agencies

Introduction

In this webinar, Sreenath Reddy and Kenton Snyder walk through how the Amazon agency reporting stack is evolving heading into 2026, and what agencies need to change now to stay competitive.

The discussion focuses on data infrastructure, AI-driven reporting, Amazon Marketing Cloud (AMC), cross-channel measurement, and scalable client reporting, based on real agency use cases.

The Five Fundamental Shifts Reshaping Agency Reporting

1. The Explosion of Retail and Advertising Data

Amazon and other retail media networks are releasing significantly more data than ever before, especially through platforms like Amazon Marketing Cloud. AMC signals continue to expand, offering deeper visibility into shopper behavior, purchase history, and long-term performance.

Agencies now face the challenge of organizing, harmonizing, and activating this growing data volume in a way that supports real decision-making.

2. Profitability Has Replaced Growth-at-All-Costs

With tariffs, tighter margins, and increased private-equity ownership, brands are prioritizing efficient growth and ROI over raw revenue.

As a result, agencies are expected to:

• Understand profitability, not just ad performance
• Connect advertising decisions to retail outcomes
• Provide insight beyond campaign metrics

Pure ad-level reporting is no longer sufficient.

3. Cross-Channel Influence Is Now the Norm

Brands are actively investing across:

• Amazon Ads
• TikTok and TikTok Shop
• Shopify (including Buy with Prime)
• Google and Meta driving traffic to Amazon

Clients increasingly ask how non-Amazon channels influence Amazon performance, making cross-channel measurement a requirement—not a nice-to-have.

4. Amazon Marketing Cloud Has Evolved Beyond Ads

AMC is no longer just an advertising optimization tool. With access to up to five years of shopper-level purchase data, agencies can now analyze:

• Customer lifetime value (CLV)
• Repeat purchase behavior
• Subscribe & Save performance
• Churn and retention patterns

AMC insights are now valuable across retail strategy, lifecycle marketing, and long-term growth planning.

5. AI Is Transforming How Agencies Work With Data

AI is fundamentally changing:

• How data is explored
• How dashboards are built
• How insights are delivered

As AI becomes widely accessible, the differentiator for agencies will be:

• Data quality
• Data connectivity
• Embedded domain expertise

What the Ideal Reporting End State Looks Like for Agencies

Sreenath frames the reporting stack around the agency lifecycle:

1. Winning new clients (audits and pitches)
2. Maintaining service quality at scale
3. Expanding existing client accounts

To support this, agencies need reporting systems that enable:

Unified Retail + Advertising Reporting

Agencies must easily combine ad data and retail data to understand overall channel health. Without this, agencies remain tactical rather than strategic partners.

Cross-Channel Performance Views

Understanding how platforms like TikTok or Shopify influence Amazon outcomes is essential for client conversations and long-term retention.

Client-Facing Reporting Portals

Modern agencies need white-labeled, client-friendly portals that allow:

• Custom views per client
• Consistent, branded reporting
• Easy access without manual reporting overhead

Proactive Troubleshooting and Diagnostics

Agencies should detect issues before clients do, including:

• Buy Box loss
• ASIN suppression
• Inventory or pricing issues

Fast diagnostics reduce churn risk.

Ad Hoc Analysis Capability

Clients frequently ask questions such as:

• Where should incremental budget be allocated?
• How should next year’s budgets be structured?

Agencies need fast, flexible access to data to answer these questions without weeks of manual work.

The Five Data Buckets Agencies Must Connect

To get a holistic view of performance, agencies must bring together:

1. Advertising data
2. Retail data (sales, inventory, pricing, promotions)
3. Shopper intelligence (AMC, path-to-purchase, audiences)
4. Competitive intelligence (share of voice, market share)
5. Client-specific data (unit costs, margins, custom taxonomies)

Only when these datasets are connected can agencies deliver meaningful insights.

Understanding the Data Supply Chain

Every reporting stack follows the same underlying flow:

1. Data collection
2. Client data ingestion
3. Data harmonization
4. Visualization and applications

This supply chain does not go away—but how it’s implemented is changing.

The Traditional Agency Data Stack (and Its Limitations)

Historically, agencies built stacks consisting of:

• Custom API connections
• Data warehouses
• BI tools like Looker, Power BI, or Tableau

This approach created major challenges:

• High engineering overhead
• Slow dashboard development
• Limited flexibility
• Poor AMC integration
• Difficulty measuring cross-channel impact

The Shift to AI-Driven Reporting and Smart Applications

The most significant recent evolution is that:

• BI tools are being replaced by AI development tools
• Static dashboards are becoming interactive applications

What once took weeks can now be built in days using natural-language prompts, assuming the data foundation is in place.

Intentwise has already transitioned to this model, enabling:

• Faster dashboard creation
• More interactivity
• Better client experience

Examples of the New Reporting Stack in Action

Agencies can now quickly build:

• Unified advertising + retail performance views
• Custom account audit dashboards
• Cross-channel views combining Amazon, Shopify, and TikTok
• Customer lifetime value analysis by acquisition period
• Promotional vs non-promotional performance analysis
• Agency-wide health and pacing dashboards

The key takeaway: if the data exists, the use case can be built quickly.

What’s Coming Next: Apps and Agents

The next major shift is embedding agency domain expertise directly into systems.

Instead of relying solely on human SOPs, agencies will be able to:

• Run automated audits
• Diagnose performance declines
• Identify root causes using AI agents

This leads to a stack composed of:

• A high-quality data foundation
• A knowledge layer
• Interactive applications
• AI-powered agents

Customization at scale is now feasible.

What Agencies Should Do Now

Sreenath outlines three immediate priorities:

1. Own and control your data
   AI output quality depends entirely on data quality.
2. Experiment with AI-driven visualization
   Start exploring how AI can accelerate reporting and insight generation.
3. Document your expertise
   Capture your team’s analysis processes and SOPs to form the basis of a future knowledge layer.

How Intentwise Supports Agencies

Intentwise supports agencies through:

• Automated data collection across Amazon, Walmart, Shopify, TikTok, Google, and Meta
• Pre-built, white-labeled reporting applications
• Advertising optimization tied to commerce data
• Rapid custom development for unique agency needs
• Early access to agentic workflows via Intentwise MCP (Model Control Protocol)

Agencies can adopt one or multiple components depending on their maturity.

Closing Thoughts

Agency reporting is moving rapidly toward AI-driven, application-based, and highly customized systems.

The agencies that succeed in 2026 will be those that:

• Control high-quality data
• Connect retail, advertising, and shopper insights
• Leverage AI to scale expertise—not replace it

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