Creating a subscriptions model, from no digital presence to over a million subscribers
In 2009, the Financial Times had no digital subscribers.
By 2019, it had created a digital subscriptions model with over 1 million paid subscribers, and generated significant new sources of revenue, future-proofing its 130 year old brand. This shift towards digital proved particularly effective during the Covid-19 lockdowns. During the first UK lockdown, weekly page views went up 97% on last year and it achieved its entire subscriptions goals for the year in just three months. One of the major reasons the Financial Times achieved these results was because, from the outset, it focused on a single metric that drove its strategy long-term.
The subscriptions model
For most of the Financial Times’s 130 year history, the primary focus of the business was to produce a best in class product: the daily newspaper. Until 1999, the distribution of the paper was controlled by intermediaries (newsagents), creating a distance between the people making the product (the newsroom & wider business) and the end customer (the reader).
The introduction of FT.com in 1999 changed this paradigm. Instead of waiting weeks for newspaper sales data, editorial and commercial teams within the FT could suddenly get access to live, in-depth metrics from FT.com. Initially these metrics were product-centric, such as page views of articles or conversion rates of landing pages. Whilst these metrics allowed teams within the FT to optimise digital assets and content, they missed out a key perspective: the customer. As FT.com grew in maturity, the data team started to identify key customer-centric engagement measures to replace the product-centric metrics. Tom Betts, Chief Data Officer at the Financial Times, says:
We began to analyse the data we had access to within the organisation. As we built models - whether they were to optimise customer acquisition or retention - one driver was consistently more significant than any other: usage.Chief Data Officer, Financial Times
“We began to analyse the data we had access to within the organisation. As we built models - whether they were to optimise customer acquisition or retention - one driver was consistently more significant than any other: usage. Once we discovered this relationship it seemed obvious. Usage directly correlates with customer value and is a great predictor of subscription renewal. This created a great story for our editorial teams too: consumption of journalism directly related to business success."
In addition to subscriber retention, the strength of the relationship between usage and customer value also helped the Financial Times understand who would be more likely to convert from a trial subscription to a paid one. Understanding measures of usage quickly became essential to the FT’s decision making, and evolved in sophistication into what we now refer to as ‘engagement’. Key to this development was the ability to segment customers, allowing the FT to better understand their needs and offer them the most relevant experiences.
This initial measurement of usage evolved into a multi-dimensional engagement score: RFV. The RFV formula had been used widely to segment customers in retail, but wasn’t commonly used in other sectors, particularly media & publishing. The RFV formula is made up of three components:
- Recency - How recently the customer has consumed content
- Frequency - How often the customer consumes content
- Volume - How much content the customer consumes
RFV is fundamentally a measure of how engaged each individual FT subscriber is, and it strongly correlates with revenue-critical metrics like subscriber renewal, churn rate and customer satisfaction. This metric formed the basis of an overarching, customer-centric north star goal: to maximise RFV where possible. A deep understanding of customer needs and the alignment of teams around this common goal have been key contributors to the FT’s rapid and successful digital growth.
Teams at the FT continue to experiment by deconstructing this north star further, in order to bring the customer into every metric that the business uses. In recent years this has been extended to enhance granular editorial measures. For years, the typical metric used to measure the success of an article was page views - a product based metric. The problem with this measure is that page views doesn’t give a real indication of the consumption of an article. It also undervalues articles that aren’t going to bring in millions of readers, but are nonetheless providing quality content to customers. McKinley Hyden, the Financial Times’s Head of Insights, explains:
The number one question that journalists wanted answering...was: what do subscribers actually think, and are they really reading?Head of Insights, Financial Times
“The number one question that journalists wanted answering after our North Star goal of engagement was introduced, which page views couldn’t answer, was: what do subscribers actually think, and are they really reading? Over the course of a few years, the insights team created a ‘Quality Reads’ metric, designed to work in tandem with page views. ‘Quality Reads’ was a consumption- based metric: how much of an article has someone actually read? This metric was designed alongside a number of different teams: internal products, multiple data teams and technology teams. This metric is now used all over the business, and is constantly being updated and improved."
With this metric, editorial could make a correlation between articles that had high page views but low Quality Reads, and vice versa, helping them make informed decisions about content and customer engagement. Marketing also use this metric to correlate between Quality Reads and Quality Visits (defined as a customer returning to the FT within 7 days). As expected, they found that customers that have a quality visit and a quality read are disproportionately more likely to convert.