Fully Dynamic, Personalised, Real-Time Paywall
Media associated with this campaign
Overview of this campaign
The Globe and Mail launched a hybrid paywall in 2012 – applying a simple meter for most articles, with editors deciding to place about 10% of articles behind a hard paywall. Like most news organisations, we were heavily reliant on advertising revenue at the time and did not want to risk killing the golden goose. However, we knew that the future of a sustainable journalism business depended on how aggressively we could acquire and retain subscribers.
We rapidly started to see a bottleneck for both new subscription growth and registration growth from our static paywall. We tried to tighten the paywall by increasing the total number of hard-paywalled articles and experimenting with the number of articles one could read via the meter – but that either depressed user engagement significantly or we missed out on potential subscriptions because we asked the wrong people at the wrong time.
In order to break through and create sustainable growth, we needed to redefine how we decided when to present a paywall or a registration wall, and when to let the reader keep reading uninterrupted. In other words, a fully dynamic paywall that offers one-to-one personalisation in real time in order to ensure we were not leaving any money on the table.
We were finally in a position to undertake this ambitious project. We had one data scientist in 2012; the team of data scientists and engineers at the Globe now numbers close to 50.
It is also worth mentioning that during our transition to the new paywall, we encountered COVID. We needed to balance competing needs – driving subscriptions while ensuring that we fulfilled our mission in providing the public with vital information on a poorly understood, swiftly spreading disease. So, we implemented various constraints within our dynamic paywall, which actually diminished its performance in favour of ensuring that Canadians understood how to stay safe and healthy in unprecedented times.
Results for this campaign
Our goals were to increase the subscription and registration conversion rates without diminishing reader engagement. Our cutting-edge machine-learning models today use Reinforcement Learning with Long Short-Term Memory to generate millions of dollars in revenue for us.
- Subscription revenue gained was 10x the advertising revenue given up
- Our revenue attributable to the paywall alone increased to $10 million annually from $2 million earlier with our old paywall.
- 51% increase in subscriptions (vs our old paywall, which we were A/B testing against simultaneously)
- More than 100% increase in registrations (vs our old paywall, which we were A/B testing against simultaneously)
- 22% increase in registered visitor engagement (vs our old paywall, which we were A/B testing against simultaneously). We were surprised to see that engagement actually increased – but that is because our paywall understands when to leave which readers alone and when to ask for either an email address or for money.
- Another unexpected success: We typically run a few “flash sales” a year and usually see a drop in subscription gains soon after each one. But with the fully dynamic paywall algorithm, the diminishing subscription trend that followed flash sales has disappeared.