A four-year-old London startup called Echobox found traction in Europe and is poised to further expand in the U.S. with its AI-assisted social media product.
Echobox, which has about 30 employees and raised $3.4 million in seed funding last year, aims to develop “AI for automating repetitive tasks” for publishers, according to CEO and founder Antoine Amann. The first product the company has brought to market is an automated social media tool that uses a publisher’s internal audience analytics along with machine learning to completely run social media accounts. Le Monde in France and The Guardian in the U.K. are clients.
A challenge with publishing analytics today is how to make sense of them, and Echobox isn’t alone in trying to solve this. When everything is measurable and everything is tracked, the problem is no longer a paucity of meaningful data but rather how to separate the signal from the noise. And in a very real way, analytics are noise. From the real-time analytics screens, often many of them, that hang in newsrooms to alerts in Slack and via email to multiple analytics tools running in newsrooms for product, ad, revenue and editorial teams, data can be as much a distraction as a tool to help people do their jobs better.