The AMEC Global Measurement and Evaluation Summit is a two-day professional development program, for attendees from around the world. It is an opportunity to be immersed in best practice of communication measurement and evaluation, including research, analysis and actionable insights from a line-up of international speakers.
Delegates will explore best practices encompassing communication accountability: planning, purpose and proof.
The AMEC Global Summit will be held in virtual format, from 26 to 27 May, and it is being powered by Intrado Studio.
The AMEC Global Summit embraces also technology, and delegates will be able to explore best practices across the communications funnel, including innovative artificial intelligence and machine learning.
DataScouting is a software research and development company, specialized in media monitoring solutions for communicators. Our platforms minimize time and effort needed to search and find actionable information across all media – broadcast, online, social, and print. Using text analytics and automatic classification, speech and optical character recognition, ad monitoring, logo and face detection, we help media monitoring companies and organizations streamline their workflow, create a database of media intelligence information, and share content. Our dashboards allow PR and media monitoring organizations to present content, visualize analytics, and create reports.
And there is more we can talk about, so visit our booth and let’s discuss your needs. We will demonstrate how you can stream-line your media monitoring workflow using our Suite.
We will be speaking at a special AMEC badge session. Our Data Scientist, Christina Tzogka, will be joining a panel of data scientists, discussing what data scientists and machine learning experts face when they enter the PR and marketing measurement and evaluation industry, what are the most common questions they are being asked, what are the problems they aim to solve, which are the low-hanging fruits, and which are the most complex challenges for algorithms and predictive models.
The key takeaways include: