How Conagra Scaled CPG Analytics with Sigma Data Apps

Before adopting Sigma, the demand science team operated in a fragmented, manual environment. Reporting and collaboration were spread across static dashboards, spreadsheets, and disconnected tools. Analysts and stakeholders were forced into inefficient workflows that created friction at every turn—reviewing reports in one system, logging updates or corrections in another, and relying on IT for even small data changes.

This created recurring bottlenecks, especially during key reporting periods. The team worked with a variety of complex data types—including search trends, reviews, and menu data—much of it semi-structured or unstructured. But without a unified environment, integrating those sources and applying business context required repeated manual effort.

Brand managers, meanwhile, had limited ability to interact with data directly. When they needed to input marketing events, annotate results, or override assumptions, the requests had to be funneled through the BI team. That limited agility, slowed decision-making, and increased the risk of data loss, especially when projects changed hands or team members rolled off.

By building a data app in Sigma, everything changed. With the ability to write data back to the warehouse, a unified app could truly answer every question.

One of the most impactful applications of Sigma has been the creation of a brand-centric reporting tool. Designed to surface micro and macro-level trends, this dashboard allows teams to compare brand-specific promotions with broader external factors like weather patterns, mortgage rates, and CPI shifts.

Users can toggle between brands and sub-brands, change timeframes, and immediately see the results reflected in the dashboard—making trend analysis faster and more intuitive.

The tool also detects volume anomalies and flags periods where sales performance deviates from historical expectations, helping brand leaders zero in on what’s working—and what’s not.

Conagra’s Input Tracker, built as a Sigma Data App, gave Conagra’s brand managers direct control over their data. Instead of routing requests through BI or IT, they now log marketing events—like limited-time promotions or distribution changes—right into the dashboard.

This change eliminated a major workflow bottleneck and gave business users more ownership over the data they rely on.

The new setup also ensures better continuity. Inputs are now stored in a centralized, governed location—reducing knowledge loss and preserving historical context as people move between roles or projects.

For BI and data engineering teams, Sigma also reduced the overhead associated with transforming and maintaining input-heavy datasets. The team can now handle input directly in the reporting layer, cutting down on time spent writing and managing scripts or manual ETL processes.

“From a developer standpoint, being able to take that unstructured data from brand teams and create views or procedures off of input tables has saved us a lot of pain moving forward,” said Henkel.

Rolling out a new BI tool across multiple departments can be a heavy lift—but Sigma’s intuitive, user-friendly interface helped Conagra accelerate onboarding and minimize friction. Business users who were previously reliant on static reports or spreadsheet-based workflows were able to transition into Sigma with minimal training.

Because Sigma combines reporting and data entry in the same environment, it eliminates the need for separate tools or technical handoffs. That simplification helped boost confidence and self-sufficiency among brand teams and other non-technical stakeholders. Because the data is written back to Snowflake, all inputs and interactions remain secure, governed, and up to date—further supporting trust and scalability.

Looking ahead, Conagra plans to expand Sigma’s footprint beyond the demand science team by embedding data apps and self-service workflows across more departments, including retail finance, sales, and marketing operations.

As part of that expansion, the team is particularly excited about integrating AI functionality using Snowflake’s Cortex. By embedding large language models directly into reports, Conagra aims to provide dynamic, context-aware recommendations that can be modified or overridden by users in real time—blending machine insight with human expertise.

This vision aligns with Conagra’s broader goal: to make analytics more flexible, more accessible, and more actionable across the organization.

With Sigma, they’ve laid the foundation—not just for better dashboards, but for a more agile and innovative enterprise.

Read more about building data apps in Sigma.