Reader State Model by The Atlantic
Overall pulse to understand the health of subscriber engagement and measuring the impact of new features.
Reader State, 30-day visit frequency
Dormant: 0 days
Fly-by: 1 day
Casual: 2–3 days
Engaged: 4–8 days
Core: 9+ days
“If readers sit in those more engaged or core states, the data shows they’re far more likely to be retained a year later. Every onboarding flow is built to push newcomers up that ladder.”Mariah Craddick, The Atlantic
The Atlantic’s product and data-science teams needed a simple, newsroom-friendly way to assess whether readers were developing a genuine habit with the brand. While daily-news publishers often prioritize daily active users, long-form, magazine-style outlets like The Atlantic publish fewer stories, making a daily visit an ambitious goal. In the short term, The Atlantic aims to encourage readers to return at least weekly, even as it works toward becoming a daily habit.
The team set out to learn how often a subscriber needs to return before renewal rates improve. By analyzing 12-month cohorts and grouping users by distinct visit days—site visits, app sessions, or newsletter opens—in a 30-day window, they identified five “reader states” that now guide engagement strategies for all users. This clustering produced five distinct “reader states,” now used to guide engagement strategies for all users, whether they are subscribers or not.
Primary use cases of Reader State at The Atlantic
Overall pulse on the health of subscriber engagement
Example I: The Atlantic uses Reader State to monitor the percentage of subscribers in each state, aiming to move more users from fly-by and casual into engaged and core. They aim to keep the dormant segment low, knowing that once a subscriber goes dormant, re-engagement becomes much harder—so the priority is preventing churn before it starts.
Example II: If data indicates a subscriber is likely to churn, using a churn model that includes Reader State as one factor among several, The Atlantic may offer a renewal rate close to their introductory price. Conversely, subscribers who appear highly engaged may receive offers at higher-tier renewal rates.
Measuring the impact of features on engagement
Example: After a feature is launched, the product team works with the data team to understand if those who engaged with said feature became more engaged overall or not. So were they in a fly-by or casual reader state and then after engaging with the feature move into engaged or core.
We did this recently to understand if interacting with the audio player in app made someone more engaged or not, it does! And to understand if interacting with onsite onboarding made those subscribers more engaged, it also does!
Mariah Craddick, The Atlantic