Alan Segal on CNN Digital’s data and AI strategy
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Alan Segal
Grow media company revenue with data, AI
The 8th Big Data & AI for Media Conference, organised and hosted by World Newsmedia Network in association with FIPP, takes place on 1-2 November at Microsoft’s headquarters in London.
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Confirmed speakers include data and revenue executives from BBC, CNN, Condé Nast, Mittmedia, Expressen, United Robots, Financial Times, Neue Zürcher Zeitung, Pangaea Alliance, Civil and Styria Media.
Now, back to Alan.
How would you describe the skills makeup and scope of your data team?
We have a combination of computer science and analyst skills on the team. We need to run the gamut because data/computer scientists have the ability to go deep, but they aren’t subject matter experts around the business needs. By pairing the two groups together, we find we are able to accomplish more.
What are the most important components of your data and AI strategy?
Given the size of our team, it’s critical we work on problems that either lead to important insights about our audience or how we can work smarter.
How is audience data used to drive advertising and subscription revenue?
We are heavily focused on our ad supported business. We use our data to improve our understanding of the audience. This in turn drives product changes, workflow changes, and strengthens our relationships with the audience.
If you were advising other data directors on how to build their operations, what key lessons would you impart?
It feels as if everyone wants to jump to the data science component of the solution, but there are so many things to get right first. Is your data available for use? Is it clean? Can it be manipulated? Do you have a way to inject your understandings into your products? Data scientists are a critical piece of the puzzle, but it requires a much larger cast to make meaningful progress.
How does audience data factor into product development and improvements at CNN?
Audience data is instrumental in so many ways, but perhaps the simplest is through A/B testing. The importance of understanding how the audience reacts to a product change – both the change itself and its downstream implications – cannot be overstated. Another area of great importance from a product perspective is understanding which cohorts to develop features for. Obviously, not all users of our products are the same and their impact on consumption is equally varied. It’s a thorny multivariate problem that has to balance user and business needs, but is impossible without our growing trove of audience data.
In your opinion, what does the future hold for the business use of data and artificial intelligence in media?
Truthfully, I think the media industry is still at the beginning of the data and AI journey. We’ll look back in 5-10 years and marvel at how far we’ve come around improvements to both the user experience and the business models needed to support it.
Join us at the Data & AI Media Week events in autumn 2018. The 8th Week of events is in London and is themed Data- and AI-driven Revenue and Audience Development for Media.
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