Media Intelligence uses data science to analyze public social and editorial media content. It refers to solutions that synthesize billions of online conversations into relevant information that allow organizations to measure and manage content performance, understand trends, and drive communications and business strategy.
Media intelligence typically takes form as a big data SaaS solution that takes traditional monitoring one step further by providing real-time insights and answers to marketing and business questions that would be difficult to answer without it. This includes questions about messaging efficiency, share of voice, audience geographical distribution, message amplification, influencer strategy, journalist outreach, creative resonance, and competitor performance in all these areas.
Media intelligence differs from business intelligence in that it uses and analyzes data outside company firewalls. Examples of that data are user-generated content on social media sites, blogs, comment fields, and wikis etc. It may also include other public data sources like press releases, legal filings and job postings.
Technologies used
Different media intelligence platforms use different technologies for monitoring, engagement and measurement. These technology providers may connect to the API provided by social platforms that are created for 3rd party developers to develop their own applications and services that access data. Facebook's Graph API is one such API that social media monitoring solution products would connect to pull data from. Technology companies may also get social data from a data reseller, such as DataSift or Gnip, the latter of which was acquired by Twitter.
Some social media monitoring and analytics companies use calls to data providers each time an end-user develops a query. Others archive and index social media posts to provide end users with on-demand access to historical data and enable methodologies and technologies leveraging network and relational data. Additional monitoring companies use crawlers and spidering technology to find keyword references, known as semantic analysis or natural language processing. Basic implementation involves curating data from social media on a large scale and analyzing the results to make sense out of it.