The 2026 e-commerce data stack: What winning brands are doing

Introduction

In this webinar, Sreenath Reddy and Kenton Snyder break down how the e-commerce data stack is evolving heading into 2026, and what brands and agencies must change now to remain competitive.

The session focuses on the real pressures reshaping retail media and analytics, including profitability, cross-channel measurement, Amazon Marketing Cloud (AMC), AI-driven analytics, and scalable data infrastructure.

Why the E-Commerce Data Stack Needs a Rethink

Sreenath opens by outlining the core reality facing brands today: more data than ever, but fewer actionable insights.

Retail media platforms like Amazon, Walmart, and Instacart are releasing increasing volumes of:

• Batch and near-real-time data
• Clean room data
• Shopper-level signals

While access to data has expanded dramatically, most teams remain data-rich and insight-poor, struggling to translate signals into fast, confident decisions.

Five Forces Shaping the 2026 Data Stack

1. Data Explosion Across Retail Media

Amazon Marketing Cloud launched several years ago, and clean room technology is now becoming standard across platforms. Brands have access to unprecedented behavioral and purchase-level data—but only if they can operationalize it.

Collecting data alone is no longer a differentiator.

2. Profitability and Efficient Growth Are Non-Negotiable

Macroeconomic pressures, tariffs, and increasing private equity ownership have shifted priorities from pure growth to profitability and efficiency.

To understand true performance, brands must connect five critical data layers:

• Advertising data
• Retail data (sales, inventory, pricing, promotions)
• Shopper intelligence (including AMC)
• Competitive intelligence
• First-party brand data (unit costs, product taxonomy, margins)

Without combining these datasets, brands lack a complete view of channel or SKU-level performance.

3. Emerging Channels and Blurred Boundaries

Retail and media channels no longer operate in isolation. Brands are investing across:

• Amazon
• TikTok and TikTok Shop
• Shopify
• Google and Meta driving traffic to Amazon

The key question brands now ask is how activity on one platform influences performance on another. This makes cross-channel measurement a core requirement of the modern data stack.

4. Amazon Marketing Cloud Is Under-Leveraged

AMC is often viewed narrowly as an advertising optimization tool, but that perspective significantly underestimates its value.

With access to up to five years of shopper-level purchase data, AMC enables insights into:

• Customer lifetime value
• Retention and repeat purchase behavior
• Promotional vs non-promotional acquisition quality
• Long-term channel health

Brands that use AMC only for ads are missing its broader strategic value.

5. AI Is Reshaping How Teams Interact With Data

AI is making analytics faster and more accessible, from:

• Asking questions in natural language
• Building dashboards and applications on demand
• Exploring data without heavy engineering dependency

However, AI is only as effective as the quality, structure, and context of the underlying data.

What the Ideal End State Looks Like

An effective 2026 e-commerce data stack enables teams to:

• Combine advertising and retail data seamlessly
• Understand cross-channel impact quickly
• Diagnose performance issues in minutes, not weeks
• Answer ad-hoc strategic questions (budgeting, incrementality, investment allocation)
• Leverage AMC outputs without technical friction
• Expand toward agent-driven analytics over time

This is the foundation for faster, more confident decision-making.

The Data Supply Chain Hasn’t Changed—But Execution Has

Sreenath outlines the classic data supply chain:

1. Data collection
2. Ingesting brand-specific context (costs, categories, taxonomy)
3. Data harmonization across sources
4. Visualization, analysis, and applications

While this structure remains constant, traditional implementations have created major bottlenecks.

Limitations of the Traditional Data Stack

Legacy stacks relied on:

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

This approach created challenges:

• Constant API changes and maintenance overhead
• Fragmented, hard-to-harmonize data
• Long dashboard backlogs
• Inability to support AMC workflows effectively
• Slow response to new business questions

The Shift to an AI-Driven Data Stack

Over the last several months, reporting tools have begun shifting away from static BI dashboards toward AI-powered development environments.

This enables teams to:

• Build interactive applications instead of fixed dashboards
• Customize views rapidly
• Collapse development timelines from weeks to days

Visualization is no longer the bottleneck—it becomes a flexible interface for exploration and action.

From Dashboards to Applications

Modern analytics experiences are:

• Highly interactive
• Actionable (not just descriptive)
• Easily customizable

Examples include:

• Unified advertising and retail performance views
• Cross-channel dashboards combining Amazon, Shopify, and TikTok
• Advertising audit views for agencies
• AMC-powered customer lifetime value analysis
• Funnel abandonment and audience creation workflows

These experiences move teams from observation to execution.

Making Amazon Marketing Cloud Work in Practice

Despite its power, AMC adoption faces real obstacles:

• Limited awareness of available signals
• Difficulty mapping large catalogs and campaign taxonomies
• Lack of scheduling, automation, and error handling
• Challenges integrating AMC outputs into broader reporting

To unlock value, brands need:

• Custom data uploads
• Reusable query and audience libraries
• Scheduled execution and error handling
• Audience activation into Sponsored Ads and DSP
• Dashboards and data extraction workflows

AMC success depends on both education and infrastructure.

Education and Operational Readiness for AMC

Sreenath emphasizes:

• Investing in AMC education and certification
• Defining a roadmap of high-value business questions
• Building internal SQL and query capability
• Operationalizing the right supporting tech stack

AMC is becoming central to Amazon’s measurement strategy—not optional.

The Next Phase: Agents and Embedded Expertise

Looking ahead, analytics is moving toward agent-driven systems that automate expert workflows.

Instead of relying on institutional knowledge stored in people’s heads, expertise can be embedded directly into systems to:

• Run audits
• Diagnose performance issues
• Perform incrementality analysis
• Enforce consistent decision frameworks at scale

This shift enables consistency, speed, and scalability.

Data Quality Remains the Foundation

No matter how advanced AI becomes, outcomes depend on:

• Data completeness
• Connectivity across sources
• Reliability and accuracy
• A semantic or knowledge layer explaining what the data means

Capturing business context and decision logic today creates long-term leverage for AI systems.

What Brands and Agencies Should Do Now

Key actions to take:

1. Own your data to avoid dependency and data loss
2. Keep data comprehensive and connected
3. Invest in AMC and AI education for teams
4. Treat data and knowledge capture as strategic assets
5. Operationalize a flexible, future-ready tech stack

How Intentwise Supports the Modern Data Stack

Intentwise supports brands and agencies through:

• Automated data collection across Amazon, Walmart, Instacart, Shopify, TikTok, Google, and Meta
• An AI development layer on top of harmonized data
• Pre-built and white-labeled analytics experiences
• Rapid customization enabled by AI
• Intentwise MCP (Model Control Protocol) for agent and AI integration
• Smart applications including ad optimization, AMC analytics, and product diagnostics

Closing Thoughts

The 2026 e-commerce data stack is already taking shape. Winning brands are moving beyond dashboards toward AI-powered, connected, and action-oriented analytics systems.

Those who invest now in data quality, integration, and education will be best positioned to scale insight—and performance—into the future.

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