George Montagu

Head of Insights

FT Strategies

Trendlines & Headlines is a monthly newsletter from FT Strategies' Data & Insights Practice.

This month, I want to take things back to basics. What chart does every publishing or news organisation need at its fingertips?

As a starting point, I would suggest a basic funnel chart - how many people (or what percentage of users) fall into each of your user types on any given month. Each organisation will have its own user types (much to the dismay of our data analysts), but a good place to start is the following:

• Anonymous: users with no ID who have not consented to cookies / web analytics
• Unknown: users with no ID who have consented to cookies / web analytics
• Registered: users with an ID (typically email address)
• Entry-level subscribers: users who are paying for an entry-level product
• Premium subscribers: users who are paying for a premium product (e.g. bundle)

What you are likely to see is a concerning picture (see example data below based on a sample of our clients) - 85 to 95% of your audiences belong to the anonymous or unknown group, 5 to 10% are registered and a tiny minority pay for access. Don’t worry, you are not alone.

Now, let’s get a bit more exciting. The next chart we typically build with clients is a consolidated view of expected annual ARPU (average revenue per user) at a user type level. What makes it “consolidated” is the combination of advertising and subscription revenue. From our experience, subscription ARPU is often available (but is exclusively based on price and not subscriber tenure) and advertising teams don’t calculate ARPU at a user type level. We have our own proprietary methodology of how we calculate annual ARPU for subscriptions and advertising (reach out if you want to learn more).

When we show this to our clients for the first time, they can’t hide their enthusiasm. For the first time, they have a consolidated financial value for each user type. That allows them (or us) to make financial projections based on moving users from one group to another. For example, transitioning an unknown user to a registered user may translate to a 8-12x increase in advertising value (from £0.50 to £5) as a result of an uplift in page views and higher CPMs for demographic targeting. These projections help to project or quantify the value of marketing and product initiatives, helping to support investment decision-making. 

Although these charts look basic, the most valuable pieces of data analysis often are. If you’d like to recreate these charts in your organisation, please do reach out for advice or share your results with us for some free benchmarking.