A Top 10 worldwide mass media company which creates and distributes leading content across virtually every media and entertainment touchpoint including TV, radio, print, film and interactive.
With declining viewership for broadcast TV, the company faced increasing pressure to reduce costs, maximize opportunities for pricing, and create advanced non-linear models for new streams of advertising revenues.
The existing on-premise legacy data and analytics infrastructure was inadequate to support the new demands of the business. First of all, the current system was expensive to license and required significant cost and technical resources to maintain. In addition, updating the legacy infrastructure would have required additional investments and still would not have been able to handle the scale and sophistication of data and analysis required. It was clear a next-generation infrastructure was needed that could handle larger multi-platform and 3rd party data to get more granular insights and accelerate decision-making around topics such as: pricing, forecasting and sales.
In order to provide a modern, scalable, cost-effective analytics infrastructure, Core Compete designed a best-of-breed cloud-based architecture based on SAS analytics and Google Cloud Platform. The new architecture needed to handle multiple important business requirements including: i) ingesting 3rd party data in real-time, ii) integrating data into existing operational systems, and iii) providing advanced analytics to improve decision-making across key business areas. Since the legacy system was based on Teradata, the project involved a Teradata migration to Google BigQuery.
To accomplish this, Core Compete developed several essential capabilities including:
Integrated Forecasting Solution – 3rd party economic data is ingested on a real-time basis and integrated with internal sales and customer data. Then, SAS Forecasting analyzes the data at scale to deliver highly accurate financial forecasts that aid in strategic business planning and critical pricing decisions.
Customer Segmentation Analytics – Core Compete developed capabilities for segmenting customers based both on current and potential value through developing a 360-degree view of the customer’s spending patterns and projected revenue growth. These segmentation capabilities use SAS’s advanced analytics algorithms and aid in prioritizing sales and marketing initiatives.
Price Guidance Solution – To help identify the right price for an advertising spot, Core Compete developed a price guidance solution. The sales team uses SAS analytics to determine the optimized price to charge, the pricing model considers multiple dynamic factors such as real-time inventory sell-through and booking pace to determine price elasticity and demand forecasts.
Data Lake – To ingest, store and manage mass quantities of data from multiple different sources, we created a data-lake that integrates data from 3rd party sources, internal sales and inventory data. In addition, these data lakes are linked to both customer and program master data leveraging Google Cloud Platform’s (GCP) Big Query capabilities. More specifically, 3rd party data sources such as Wide Orbit, Nielsen, ComScore and Double Click for Publishers are integrated to deliver highly granular, minute-by-minute data that is refreshed daily to provide audience and marketplace insights. As a result, the data lake provides comprehensive, clean and readily usable data to the data scientists and developers that enables them to develop additional analysis techniques. Also, the elastic nature of this platform allows the data lake to expand and add new data sources in a matter of minutes.
The advanced analytical capabilities for forecasting, pricing and customer segmentation support better, faster decision-making that directly impacts sales revenue and ROI. Two key contributors are the ability of the sales team to optimize pricing for ad inventories and the ability to prioritize their sales efforts for the right customer segments.
The data lake implemented by Core Compete enabled the media giant to become an agile enterprise that rapidly on-boards and analyzes new data sources. At the same time, the customer benefitted from more capability and flexibility, they were able to save more than $3 million in two significant components. They saved $1.5 million through cost-avoidance by hosting all 3rd party data more efficiently on their now modernized analytics infrastructure. The second component of the saving is a potential $1.7 million in expected operating costs reduction by retiring the legacy data warehousing infrastructure.