Enabling High-Performance Analysis of National TV Viewership Data at Scale

An audience measurement company transformed how massive volumes of television exposure data were queried and delivered, enabling real-time analysis and unlocking new product opportunities.

Context

The client is an audience measurement and consumer research company that provides viewership and exposure data across television, radio, print, online, gaming, and other media channels. Their data is collected through a combination of offline reporting, in-home metering, and wearable devices, spanning multiple markets and demographic segments.

This exposure-level data was processed and factored to model national viewership behavior, allowing analysis down to 15-second intervals across both television programs and commercials.

Challenge

While the company could capture highly granular viewership data, the national volume of this data across all television networks became unwieldy. Queries were slow to execute, limiting the company’s ability to offer interactive, self-service products to customers.

Operationally, some customer-generated requests required offline processing and could take days to complete. The performance limitations constrained both internal workflows and the company’s ability to monetize its data through scalable, customer-facing products.

The challenge was to redesign how the data was structured and accessed so that granular questions could be answered quickly and reliably at scale.

Solution

Unista began by ingesting the raw, exposure-level data on a recurring basis and applying the same factoring logic used by the client. We then designed a data architecture that intelligently segmented the dataset into logical partitions optimized for query performance.

On top of this foundation, we built a multi-tenant platform that allowed users to break down viewership data by market, demographic, program, and commercial—down to the 15-second interval—with fast response times. The system was designed to support both internal analysts and external customers, enabling interactive exploration of data that had previously required manual, offline workflows.

Impact

The company dramatically improved query performance, reducing analyses that once took days to execute to seconds. This shift enabled new self-service capabilities for customers and streamlined internal operations.

By transforming how data was structured and delivered, the platform not only improved efficiency but also created a new revenue channel for the business. The result was a scalable, high-performance system that turned a data volume challenge into a competitive advantage.