Habit formation: How The Wall Street Journal turned user-level data into a strategy to keep subscribers coming back The Journal went on a quest to identify the user actions — an app download, an article share, repeat reading of a particular reporter’s stories — that can turn a new subscriber into a loyal one. Then it turned that knowledge into churn-reducing action. By Anne Powell, John Wiley, and Peter Gray June 27, 2019, 6 a.m. With more than 2.5 million paid members, The Wall Street Journal is the biggest it has ever been. While we will continue to sharpen and evolve our member acquisition strategy, we believe that the next chapter of growth will be fueled by retention. Data clearly shows that the best way to reduce churn is to increase engagement — but the path to driving product use and building loyalty amongst members has not always been as obvious. Over the past year, a cross-functional group here at the Journal has worked together to identify retention-driving actions and reinvent the way we promote those habits to our member base. We call it Project Habit. We’ve known for some time that if a member downloads our mobile app or signs up for an email newsletter, they’re more likely to stay with the Journal. Project Habit began when someone said: “There must be more.” Members of our data analytics, optimization, and membership teams set out to confirm that assertion. Our first step was to make an exhaustive list of all the things a member could do on our site. The list was quite long, including actions from Email Article and Play Puzzle to Build Watchlist and Comment. Next, we built a model to ingest all of these actions or “habits.” We decided to borrow techniques commonly used in medicine by applying the Kaplan-Meier estimator to member retention. Essentially, the model compares the “survival rate” of members who have taken a particular action against the rate of those who did not. It looked at the impact of performing these actions within the first 100 days of membership and how it affected retention in 30-day increments over the course of their first year of tenure. Why did we only look at actions taken within the first 100 days? To try to eliminate the causation vs. correlation question. Focusing on our newest members ensures we’re looking at those who aren’t yet habituated. Any behavior they display marks the emergence of a new pattern rather than the presence of an existing one. The trends we observed held true for members of all tenures and pack types. Our big finding: There are many more actions beyond app downloading and newsletter subscribing that drove retention amongst members. We saw, in fact, that there are dozens of habits that clearly reduce churn. We plotted the actions based on how dramatically they affect retention and their current adoption rate amongst all members. Here are some of the top line trends we found: https://www.niemanlab.org/2019/06/h...o-a-strategy-to-keep-subscribers-coming-back/