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Media intelligence uses data mining and data science to analyze public, social and editorial media content. It refers to marketing systems that synthesize billions of online conversations into relevant information. This allow organizations to measure and manage content performance, understand trends, and drive communications and business strategy.
Media intelligence can include software as a service using big data terminology.[1] 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, news, blogs, legal filings, reviews and job postings.
Media intelligence may also include competitive intelligence, wherein information that is gathered from publicly available sources such as social media, press releases, and news announcements are used to better understand the strategies and tactics being deployed by competing businesses.[2]
Media intelligence is enhanced by means of emerging technologies like ambient intelligence, machine learning, semantic tagging, natural language processing, sentiment analysis and machine translation.
^Leslie Nuccio (January 19, 2015). "Digital Breadcrumbs and the New Media Intelligence". Social Media Today. Retrieved March 23, 2017.
^Oh, Onook; Agrawal, Manish; Rao, H. Raghav (2013). "Community Intelligence and Social Media Services: A Rumor Theoretic Analysis of Tweets During Social Crises". MIS Quarterly. 37 (2): 407–426. doi:10.25300/MISQ/2013/37.2.05. ISSN 0276-7783. JSTOR 43825916. S2CID 16343216.
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