Transforming a Media Organisation with
Big Data
Robin Goad, Head of Customer Analytics, Financial Times
March 2016
1
Agenda
A brief history of the FT
What does Big Data mean to the FT?
The benefits of Big Data
How we do it
What’s next?
2...
A brief history of the FT
128 years of innovation
What does Big Data mean to the FT?
The data that matters
User
• Identity
• Contact
• Subscription
• Demographics
• Devices
• Payment
• Permissions
Behavioura...
“80% of the FT’s revenue
would be at risk if we lost
our First Party Data”
Internal analysis to determine the value of the...
The benefits of Big Data
A data driven strategy
Measuring Reader Engagement
We look at reader behaviour over
the last 90 days:
• Recency – when did they last
visit?
• Fre...
Segmenting users based on behaviour
Personalisation via data
myFT – peronalised
content on- and off-site
API – feed data to
where people need it Editorial aut...
Data driven innovation
How we do it
Team and organisational structure
Chief Data Officer
Analytics
Reporting
Data
Intelligence
Data
Science
Vertical
Specialis...
“The analytics team (with support
from tech, commercial and third
parties) will explore ways of finding
value as a prerequ...
What’s next?
What are we planning for 2016?
Data democratisation Distributed content
Test, test, test…
Plus…
• New data sources
• Focus...
Questions?
Presentation Financial Times Big Data at EBU Big Data Conference
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Presentation Financial Times Big Data at EBU Big Data Conference

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Those are the slides of the presentation given by Robin Goad at the EBU Big Data conference in Geneva on March 21st, 2016.
We analyse this presentation and some of its key findings on our blog at http://www.intotheminds.com/blog/en

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  • Emphasise the importance of collecting and storing data
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    Use of lionel on barriers etc – wins on A/B tests
  • Presentation Financial Times Big Data at EBU Big Data Conference

    1. 1. Transforming a Media Organisation with Big Data Robin Goad, Head of Customer Analytics, Financial Times March 2016
    2. 2. 1 Agenda A brief history of the FT What does Big Data mean to the FT? The benefits of Big Data How we do it What’s next? 2 3 4 5
    3. 3. A brief history of the FT
    4. 4. 128 years of innovation
    5. 5. What does Big Data mean to the FT?
    6. 6. The data that matters User • Identity • Contact • Subscription • Demographics • Devices • Payment • Permissions Behavioural • What is read? • How is it read? • Where is it read? • How is it found? • Why is it read? • What about stuff that isn’t read? Meta • What is the story about? • Who wrote it? • Where does it belong? • Who can see it? • When, where and why was it published?
    7. 7. “80% of the FT’s revenue would be at risk if we lost our First Party Data” Internal analysis to determine the value of the FT’s First Party Data
    8. 8. The benefits of Big Data
    9. 9. A data driven strategy
    10. 10. Measuring Reader Engagement We look at reader behaviour over the last 90 days: • Recency – when did they last visit? • Frequency – how often do they visit? • Volume – how many articles have they read? Engagement score Cancellationrate More engaged readers are less likely to cancel
    11. 11. Segmenting users based on behaviour
    12. 12. Personalisation via data myFT – peronalised content on- and off-site API – feed data to where people need it Editorial authority
    13. 13. Data driven innovation
    14. 14. How we do it
    15. 15. Team and organisational structure Chief Data Officer Analytics Reporting Data Intelligence Data Science Vertical Specialists Campaign Management Data Strategy Technology Product Research 3rd parties Key supporting functions: Customers of Data and Analytics B2C and B2B Editorial Product Finance Advertising Board & Strategy
    16. 16. “The analytics team (with support from tech, commercial and third parties) will explore ways of finding value as a prerequisite to building in new capability” The FT’s “Analytics First” approach to Big Data
    17. 17. What’s next?
    18. 18. What are we planning for 2016? Data democratisation Distributed content Test, test, test… Plus… • New data sources • Focus on data quality • Answer questions quicker • Develop new skills • Grow team • More stakeholders • Academic partnerships • More innovation…
    19. 19. Questions?

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