Utilizing Data and Data Science to Optimize Tune In

• Verfasst von: Salon, Data [VerfasserIn]
• Titel: Utilizing Data and Data Science to Optimize Tune In
• Institutionen: Safari, an O’Reilly Media Company. [MitwirkendeR]
• Verf.angabe: Salon, Data
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: Data Science Salon
• Jahr: 2019
• Umfang: 1 online resource (1 video file, approximately 20 min.)
• Fussnoten: Online resource; Title from title screen (viewed September 10, 2019)
• ComputerInfo: Mode of access: World Wide Web.
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Electronic videos ; local
• K10plus-PPN: 1733129804
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m3755946033
• Lokale URL Inst.: Zum Volltext

Abstract

Presented by Diane Leung, Principal, Analytics Innovation at Altman Vilandrie & Company Measuring and optimizing tune-in is critically important for the media and entertainment industries. This discussion focuses on best practices for utilizing data and machine learning to optimize tune-in on national linear inventory. In order to do this properly, advertisers need to unify their marketing ecosystem, design a holistic measurement approach, and break down barriers for closed-loop, incremental measurement. In this session you will learn how to: 1) Create a framework for utilizing data and machine learning to maximize tune-in and 2) Overcome analytical obstacles created from fragmented and incomplete data.