The art of listening: lessons in analytics at Wall Street Journal

By John Wiley

The Wall Street Journal

New York


If the last decade of digital disruption was dominated by masters of the megaphone — loud, upstart media brands using platforms like Facebook to artfully amplify their message — I predict the next decade belongs to publishers who are experts at listening.

At The Wall Street Journal, building listening into our DNA has been paramount in building our membership business, specifically in the construction of our paywall and putting a greater focus on the customer.

Using customer data, the Wall Street Journal was able to analyse and personalise the paywall model.
Using customer data, the Wall Street Journal was able to analyse and personalise the paywall model.

Among those in the publishing industry, the rising consensus is growth in digital subscriptions is an economic imperative, and the enlightened use of readership data and technology is a critical part of that growth.

There’s opportunity for publishers to heed the example of companies like Netflix, where data scientists analyse every micro-pattern of user behaviour. This means carefully monitoring a user’s path of content consumption from day one in the product and creating descriptive models giving the business a colourful portrait of that user’s engagement.

When we set out to reinvent our paywall about four years ago, we were initially drawn in by complexity of the problem. We knew the publishing ecosystem to be increasingly complex, a vast landscape seemingly built atop a fault line. For a decade, we’ve felt the earth shake beneath us, observing how search and social audiences play an outsized role in informing publishers’ strategies for monetising content.

What we eventually learned was a simple understanding of readers was the key to reclaiming autonomy over our paywall and growing our member base.

The initial vision for the Journal’s new paywall was a beast with many arms, deploying a mix of tactics adaptive to platform and referrer. We rapidly spun up heavily segmented marketing messages, mapped to several key segments manually devised using our data management platform (DMP). We saw some success in these optimisations. With time, we began to look for a more efficient method of personalisation, one that would apply a science to identifying our “high propensity” segment.

What we did notice working almost immediately was another component of our testing: the simplification of our rules around just what content was paywalled. In rewriting the paywall logic, we also challenged a long-term assumption that only business-focused coverage could command a premium audience. The Journal moved away from a system of manually picking which stories to paywall and immediately found a willingness to pay for quality reporting across previously overlooked verticals.

This provided an immediate boost in subscriptions and also gave direction to our testing. Our opportunity would be in building a new understanding of the WSJ reader, one unburdened by our accumulated assumptions.

Taking some cues from behavioural economics, we decided conquering personalisation would demand a system that could further eliminate our biases. Using a series of algorithms, we began optimising paywall performance around both macro trends (i.e. shifts in willingness to convert by time of day) and micro patterns (i.e. a person’s unique pattern of readership).

We devised a new paywall that could function like a central nervous system, customising experiences on to the path of an individual reader.

Utilising a “credit score” of sorts for reader behaviour, we could remove some of the complexity demanded by DMPs. Readership patterns would be distilled into straightforward, actionable indicators of which readers were most likely to subscribe, with machine-learning techniques in the background, properly weighing factors like frequency of visitation, content preference, and length of engagement. These scores would then be used to test different “paths” for readers to subscription.

This meant altering messaging and access in such a way to incentivise engagement among low propensity readers, keep the paywall tight for those ready to subscribe, and nudge readers along that path to subscription. This is what we would come to know as our dynamic paywall.

The new paywall has been a critical piece of The Wall Street Journal’s growth story. With parent company Dow Jones now celebrating 3 million subscribers, the Journal has more members than ever before. It helped us come together around a new vision of what a membership business looks like, applying intelligent modeling around engagement to all stages of the customer lifecycle.

And while it’s only one piece of The Wall Street Journal’s expansive membership strategy, the applied use of readership data has allowed us to become better listeners and center our conversations on understanding the readers rather than simply selling to them.

About John Wiley

By continuing to browse or by clicking “ACCEPT,” you agree to the storing of cookies on your device to enhance your site experience. To learn more about how we use cookies, please see our privacy policy.