Case Studies > Local TV Broadcasting Leader in U.S. Optimizes Revenue with Sigma

Local TV Broadcasting Leader in U.S. Optimizes Revenue with Sigma

Company Size

1,000+

Region

America

Country

United States

Product

Sigma
Snowflake
Tableau

Tech Stack

Cloud Data Warehouses
BI Tools
Data Analytics

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Customer Satisfaction
Productivity Improvements
Revenue Growth

Technology Category

Analytics & Modeling - Big Data Analytics
Analytics & Modeling - Real Time Analytics
Functional Applications - Remote Monitoring & Control Systems

Applicable Industries

Telecommunications

Applicable Functions

Business Operation
Sales & Marketing

Use Cases

Real-Time Location System (RTLS)

Services

Cloud Planning, Design & Implementation Services
Data Science Services
System Integration

About The Customer

The customer is the fourth largest independent TV station owner in the U.S., operating 60 television stations across 42 markets. They share news and entertainment content and rely heavily on audience engagement metrics and performance data from sources like Nielsen and Comscore. The company ingests and analyzes billions of rows of operational and TV ratings data to understand audience engagement and measure both national and local media group performance. Their primary goal is to optimize content and advertising revenue by leveraging data-driven insights.

The Challenge

The fourth largest independent TV station owner in the U.S. faced significant challenges with their legacy data analytics architecture. The system required constant administrative oversight, and basic queries took hours to run. Adding new data sources could take up to three months, stifling data exploration and delaying insight discovery. The architecture couldn't handle the volume of data necessary for key revenue-driving reports, leading to abandoned projects. Scale and performance limitations made it impossible to effectively analyze data across sources, with filters taking an hour or more to load. Additionally, the company lacked a way for the BI team to collaborate on analytics with their line of business counterparts or quickly share new insights with TV station leaders.

The Solution

The company implemented Sigma, a cloud-native BI tool purpose-built for Snowflake and cloud data warehouses. This solution provided direct access to live data in Snowflake, ensuring that everyone was always looking at the same current data. Station leaders gained instant access to the information they needed to make informed decisions while data remained secure in Snowflake. Sigma's unlimited scale and speed allowed the BI team to analyze and filter billions of rows of ratings data across multiple sources without latency delays. The spreadsheet interface of Sigma enabled self-service data exploration, making iterative ad hoc analytics available to anyone. The BI team now has a single source of truth for data and can easily add new sources to analyses without help from the data team, accelerating and improving insights.

Operational Impact

The BI team built a live dashboard with ratings and revenue data that informs critical business decisions.
The content team can now monitor and optimize audience engagement effectively.
The sales team leverages data to sell advertising placements based on a mix of programming, demographics, and market.
Station leaders have instant access to the information they need to make informed decisions.
The BI team can easily analyze and filter billions of rows of data across multiple sources without latency delays.

Quantitative Benefit