BDW17 London - Tom Woolrich, Financial Times - What Does Big Data Mean for the FT and How Do We Use It
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Content: 1. A brief history of the FT 2. What does Big Data mean to the FT? 3. The benefits of Big Data & how we use it 4. How we do it 5. What’s next for us?
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BDW17 London - Tom Woolrich, Financial Times - What Does Big Data Mean for the FT and How Do We Use It
- 1. What does big data mean for the FT and how do we use it Tom Woolrich, London Big Data Week
- 2. 1 Agenda A brief history of the FT What does Big Data mean to the FT? The benefits of Big Data and how we use it How we do it What’s next? 2 3 4 5
- 3. Data is central to our business model
- 4. What does big data mean to the FT?
- 6. “80% of the FT’s revenue would be at risk if we lost our First Party Data”
- 7. The benefits of Big Data
- 8. A data driven strategy
- 9. 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 scoreCancellationrate More engaged readers are less likely to cancel
- 10. Segmenting users based on their behaviour
- 11. Personalisation via data myFT – personalised content on- and off-site Feed data to where people and systems need it Editorial authority
- 13. How we do it
- 14. “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
- 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. What’s next?
- 17. What are we planning for 2018? • In house attribution modelling • Linking customers across all of our publications (FT specialist) • Next level ads – more than just impressions, outcome based values • More self-service – double usage levels; expose models to users. • GDPR • Expand KPIs to be more team focused goals: product specific goals and targets, interaction level engagem ent. • Dynamic pricing, revenue optimisation, yield management, better view of lifetime value (LTV). • More segmentation of our audiences across the board: prospects, subscribers, advertising audiences, unm asking the anonymous • Do more cool stuff with data – quarterly innovation month.
- 18. Thank you
Editor's Notes
- smile
- Start off with a brief history of the FT – not everyone knows who we are, or might have a limited perception of what we do Then talk about what big data means to us at the FT – what do we really mean by big data, and which of it is most useful Next talk about the benefits we have derived from Big Data – real, tangible benefits Then talk about how we do it – the structure of the team and the importance of cultural change and support Finally what’s next – what are the challenges and opportunities? And we’ll have time at the end for questions, or course
- Most famous as a leading ‘financial newspaper’ – printed on pink news print ‘the pinkun’ Founded in 1888 – coloured pink to distinguish us from a key competitor 1995 – launched the website 2007 metered access model – view a certain number of articles for free (registration) + then subscription It was very important we didn’t start giving our journalism away for free Other benefit of registration is fantastic user level data – we could see usage across multiple devices. KEY! Also launched our own web app, bypassing the app store? Why? Collecting our own data was key = we’ll talk about this at the end of the preso (distributed content). Last year we shifted away from metered model to trials model – this decision was 100% data driven (talk about later). We now have the highest number of paid for readers in our history – 770k across print, digital and B2B. Data is the core for us – we now have chief data officer
- There are 3 key types of data for us: User demographics; we want to know this for Ads, marketing, billing, etc.. Is key – and if we don’t have it we, use data science to work it out (e.g. Gender data) Behavioural – real benefit of the new world, so much is based on that and it often delivers the best results – e.g. marketing stuff. Meta data – how do we explain what it all means? Historically we have not been great at this and it’s a focus. Very important as we take it to the next level – both for models, but also for the website, etc.. And of course, bringing this data together is vital – how to store for usage both for analysts and systems. Mention Redshift and NGDA (collection as well as storage) – NGDA powers analysts, Chart.io and Lantern Issue when we don’t have full control – e.g. Ads, takes ages to get stuff, and no historical data
- All of this we could call first party data We did some analysis last year and concluded that 80% of revenues in some way relay on this data. We think of our data as a key asset – up their with the quality of our journalism and the brand
- So here is a very clear example of how important data is to the FT! Our CEO has set a very simple – but not easy!! – target of reaching 1 million paying subscribers (remember – we are not at c780k.) We’ve got to where we are now off the back a strong business model, fantastic journalism and a volatile economic environment But the next 220k will be trickier – print will decline, so digital needs to grow more than 220k To do this we need to achieve 2 key things: engage existing customers in order to retain them: RFV And find new audiences to sell to: Reach / QV All investment now seen through this prism, data model driven by analytics, finance, strategy & the board
- Here’s our very successful R / F / V model – stolen from retail, TBH It works because we correlated it to canx We have an important threshold – This stops focus on people it’s easy to win over. After 200 is diminishing returns Moving from unengaged to engaged is worth £108 in lifetime revenue
- RFV also allows us to describe customers a lot more easily Scale from engaged to unengaged FT Fans vs. EFF How to use this? Alert for people moving from 1 group to another – DS model Power marketing campaings Measure next: key metric to track success and migration
- RFV etc is very much as quite a high level, but what about more day to day Data is a big way of powering personalisation on site. We have been a bit behind on this, but myFT is very much about using opted in and derived data to power that. Collecting data is key – preference centre, yes, but also collect data in context (B2B 25%, B2C 43%) APIs = talk about HUI, taking what we know and feeding it to other systems Editorial authority v important – majority of our subscriber journesy start on the homepage / 5% of digital traffic is ePaper Use of lionel on barriers etc – wins on A/B tests, so editoirial authority also help us sel!!
- Having the data, it all in one place and team of smart people – means you can innovate E.g. business travellers segment came from request from Ads HUI API stuff Allows us to be at the forefront of discussions with Google, Facebook, Apple… etc..!
- Meta data stuff Risk of letting developers create stuff that isn’t joined up – it can be perfect but expensive Analytics Frist focus helps us to best opportunities Having analysts with commercial savvy, comms skills, etc.. Is VERY important. A good lead data scientist does not sit in the corner just being clever,
- Importance of Chief Data Office – highlights how important it is to the board Talk through team CDO role is as much about strategy and mind-set as direct reports Technology is vital to support Range of stakeholders – EVERY key department Decent size team – 15 analysts + as many again supporting in tech, research, product etc…. 30 people (1.5% of FT total)
- What’s next? Data democratisation – push culture through org, unified set of data to support everything. Screen around office etc – always in people’s faces How do we collect data from distributed content – very important Testing – building in house capability, key driver for trials work, big cultural issue to organise it properly What else – see list