Journalists, by nature, love data. They are expert synthesisers of data, capable of digging into the gritty details and distilling them down in an impactful way for the reader. When building our Analytics Hub — a set of data-centric dashboards, reports, and tools — for The Seattle Times newsroom, we had a similar goal in mind.

During a hack week (also known as a “hackathon” which is a five-day sprint of rapid prototyping in which teams are challenged to bring a product idea to life) in the summer of 2017, Digital Audience Editor Nick Eaton pitched the product team on an easy way to bring all of the various data streams consumed by the newsroom into one place.

To really get a sense of how a story was doing, a reporter, editor, or producer would likely need to keep their eyes on a few open tabs and the overhead monitors that flash up-to-the-minute data. Nick’s idea was to bring all of that together in one place.

For that week, we settled on a simple story report: put in the URL of an article, get back real-time users from Chartbeat, likes and shares from Facebook, and historic pageviews from Google Analytics.

The Seattle Times Analytics Hub makes it easy for staff members to see at a glance how their content is performing over time.
The Seattle Times Analytics Hub makes it easy for staff members to see at a glance how their content is performing over time.

Two years later, this idea evolved into the Analytics Hub, and it is the home of multiple reports, dashboards, and tools used in the newsroom and around the company to gauge the performance and efficacy of our content. The Analytics Hub has received a facelift or two since, and our business intelligence team is hard at work on bringing all of our data together for quicker and easier access.

Early on, we knew we wanted to focus more on the readers, rather than faceless metrics like pageviews, which when set as a goal often leaves a sour taste after the last couple decades of growing pains in digital journalism. In iterating on the initial prototype of the Analytics Hub, we tried to put more emphasis on readers and who they are, adding charts that show the percentage of local readers and subscribers that read a story. At the top of every story report, we list some highlights for that story like, “This story was read by 12% of subscribers!”

In deciding how to bake subscription metrics into our analytics pages, we wanted to avoid a singular attribution metric. While the idea of a single, composite metric is appealing, past experience has shown this to invite skepticism and demands for a breakdown of the constituent bits of the algorithm.

We also wanted to make sure that stories beyond the ones read directly before a transaction event were given fair credit as part of the reader’s journey toward becoming a subscriber — a sense of trust and value doesn’t happen all at once, but over time.

In the end, and after much debate and whiteboarding, we settled on a tiered approach, which gives some value to all the stories a user reads during the month before subscribing. On all subscription reports, we display tallies for “Influenced,” “Highly Influenced,” and “Directly Influenced,” based on how closely a story was read before the eventual subscription event.

Editors around the newsroom have been able to use this subscription data, along with other metrics like in-market pageviews and subscriber reach, to help guide coverage.

“It enables us to take the temperature of what does and does not drive subscriptions, which we hope is the business model of the future,” says Features Editor Stefanie Loh.

Individual key coverage areas in the newsroom — real estate, the Seattle Mariners baseball team, food and drink, etc. — have been designated as “mini publishers” and are empowered to set their own goals and make data-driven coverage decisions to meet them. Staff covering the Seahawks football team quickly realised mailbags were a simple thing to produce that really resonated with readers, a strategy that has now been extended across several teams, even finding a way into our recent snow coverage.

We built the Analytics Hub around the question, “How’s my story doing?” Looking to the future, we want to be smarter about how we answer that question, starting to get at why as much as how. If a story did well, was it because of or in spite of its curation, timing, and promotion? That will be the next of many steps toward more insightful, impactful data.