Home The Winning Combination of RPA and AI for Media Research

The Winning Combination of RPA and AI for Media Research

by sol-admin

Nielsen Media Research (NMR) is an American firm that measures media audiences, including television, radio, theatre films and newspapers. NMR, headquartered in New York City, is best known for the Nielsen ratings, an audience measurement system of television viewership that for years has been the deciding factor in canceling or renewing television shows by television networks. Here is a digest of how RPA has helped Nielsen undertake its digital transformation journey by expediting the time it takes to deliver its analysis to customers.

Data is at the core of Nielsen’s entire business. The global market research firm aggregates and analyzes vast amounts of it to help organizations in retail, manufacturing, media and advertising make better business decisions and wiser investments.

This challenge is made even greater by rapid change in consumer-centric ecosystems – from the rise of e-commerce and the way people shop to the proliferation of digital devices and how people view shows or listen to the radio. The need for operational efficiency to deliver independent measurement to the market faster and help build action plans and strategies for clients is very critical to Nielsen’s success. And as Nielsen is an organization of more than 50,000 employees in over 100 countries, the scale of transformation presented immense challenges and opportunities.

Nielsen created a Center of Excellence (“CoE”) with about 12 core people. That core team promotes RPA and recruits “champions” within different business units, partly with the help of email blasts that document success stories within the company.

Since launching, the CoE has recruited about 150 champions from different Nielsen locations around the world. These champions are responsible for becoming knowledgeable about RPA, primarily using UiPath Academy resources. This expertise allows them to nominate automation processes in their business areas to the core CoE team, which can then assess their technical feasibility.

Over 347,997 hours saved increasing to 500,000 with Intelligent Automation by the end of 2020.

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