Schibsted’s Data- and AI-driven Revenue Strategy

Thursday, October 4th, 2018 Case Study Comments

Tor Jacobsen
SVP Consumer Marketing & Revenue
Schibsted Media

Tor Jacobsen is SVP Consumer Marketing & Revenue at Schibsted Media. He will speak at the 8th Data & AI for Media conference in London about how Schibsted leverages data and AI to acquire and retain subscribers. 

Data & AI for Media: How would you describe the skills makeup and scope of your data team?

Jacobsen: Schibsted’s data and analytic capabilities are primarily located close to the market and the brands. Data competences, primarily focusing at behavioral data, is paired with research, insight and business expertise to ensure that it drives actual business value. The output from the data can both be data driven products and decision support.

Data & AI for Media: What are the most important components of your data and AI strategy?

Jacobsen: The core of the Schibsted’s strategy is to minimize number of weak links in the processes of refining the raw data into insights and actionable conclusions. The data and analytics is seen as a crucial component in the vast majority of the core processes of Editorial and Consumer Business rather than a standalone function.

To ensure rapid development and high focus at business application a scalable data architecture will be crucial. Machine learning, AI, is a common component already used in various predictive models. However, developing the methodology itself is not in focus but the applications are.

Data & AI for Media: How is audience data used to drive advertising and subscription revenue?

Jacobsen: We use audience data for several purposes such as statistical modelling, content optimization, pricing (both B2C and B2B), segmentation, optimization of customer service etc. The overall objective is to provide our users and customers with relevant and personal(ized) experiences through fully data driven products.

  1. If you were advising other data directors on how to build their operations, what key lessons would you impart?
  • Clean up your data structure. You need high quality data you can trust
  • Focus at application and do not dive too deep into fancy techniques
  • Make sure your data and analytics team is close to core business. It is much harder to succeed if you have a highly specialized team disconnected from the people producing output (content) and/or doing marketing activities
  • Build data and analytics’ competences in your management team and/or management level
  • Minimize time spent in number logistics and try to automate recurring tasks
  • Make sure you have the right people and competences. Recruit new competences if necessary

Data & AI for Media: How does audience data factor into product development and improvements at Schibsted?

Jacobsen: We use audience data to identify new opportunities and optimize core processes across most of our business. How we do this varies from project to project and process to process. I believe this is more a of a mindset question: We need to make informed decisions. Looking at audience data is a very easy way of working data informed.

Data & AI for Media: In your opinion, what does the future hold for the business use of data and artificial intelligence in media?

Jacobsen: I believe data and AI will continue to be extremely important going forward. Our first experiences with algorithms running our front pages, optimizing advertising sales and improving how our products work are mostly positive. We believe that a combination of human creativity and smarter technology will help boost our business going forward. A higher degree of machine learning in our processes doesn’t have to imply less human interaction. Human creativity and fingertip feel can be enhanced with well design decisions support.

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. The 9th week of events is in Amsterdam and is themed Product Development for Media Companies, using AI and data.

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