Trust is complex. Using data, how can a digital media company understand the levers to be used to grow trust in uncertain times?

Trust is essential to The Wall Street Journal, and they have successfully earned and grown that trust through their 133-year history. In today’s media landscape, the challenge is to continue to build trust that enables growth by making trust actionable, given that it is extremely complex, personal, and defined in many ways. Through this pilot, WSJ wanted to understand the why behind trust, and how to best harness those findings through data-driven technology.

With the trust data and experimental design, a series of targeted campaigns were deployed, and we scaled the solutions that worked in the pilot for all customers with similar trust scores. WSJ used these algorithms to target potential readers with content designed to reach customers based on which aspect of trust they prioritized most. To increase the transparency trust signal, for example, WSJ ran a series of digital campaigns reminding about the breadth of services available to subscribers, including content sharing with friends and family, podcasts, additional products like crossword puzzles and more. To increase the humanity trust signal, WSJ focused on ensuring targeted readers felt included, promoting existing content to ensure that readers themselves were reflected in the breadth of coverage. For some, this meant sharing a recent special report on climate change, while for others, this included sharing a column by a current family and technology columnist.

First, a four-question trust survey was completed by 16,000 current, former, and prospective WSJ customers to gauge beliefs regarding these four signals of trust: capability, reliability, transparency, and humanity. This identified areas of trust strength as well as opportunities to bolster trust. This information was combined with WSJ’s own behavioral data—for example, what customers read and how frequently they visited the company’s website. By evaluating thousands of potential variables, a dynamic data-driven model of customer trust was built. This dynamic model could be applied to measure changes in trust scores over time. This meant that by surveying a very small subset of the company’s customers, we could predict individualized trust scores across millions of current and potential customers.

Having established this trust model, a series of targeted content pilots were deployed to determine which actions would increase trust scores. These were designed as experiments, separated into test vs. control groups.

By deploying campaigns targeted toward customers based on which of the four dimensions of trust they prioritized most, the TrustID technology demonstrated that WSJ could grow their targets’ trust with content that WSJ already had by delivering it to the right people at the right times. The TrustID algorithm was three times more effective at targeting customers based on their trust scores.

These efforts improved more factors than trust alone. There was a notable impact on other important business measures, such as:

? 33% growth in targeted trust scores as predicted by the model

? 14% increase in web views (versus a control group).

? 5% decrease in predicted subscriber churn.

For a subscription-based media company, keeping current customers is far more efficient than spending to acquire new ones. TrustID created a successful targeting framework to harness and continue to build trust, drive web traffic, and reduce subscriber churn.