Ditch the Dashboard: How to Find Deeper Meaning in Data
Data measurement today is key. Go beyond the page view to make better business decisions.
We’re living and working in a dashboard-crazed culture. Today, publishers and content teams have an enormous wealth of data at their disposal. With the right tools, we can instantly see how many people are reading our article, streaming a video, downloading our podcasts, and liking and sharing content on social media. Yet, newsrooms have long struggled with how to weave this wealth of information into their work.
Too many organizations obsess over the numbers without assessing how these metrics inform their strategy. They launch initiatives and hope for the best, measure retroactively, or have simplistic goals of surface-level KPIs in mind. Seventy-three percent of newsrooms focus attention on page views rather than how many readers are actually engaging with content (Reuters Institute for the Study of Journalism).
Others take a fragmented approach, defining success differently across teams within their organization. Writers and editors often fixate on page views, video teams focus on video views, while the social team is constantly checking the number of likes and tweets.
Data has great promise: to give audiences a stronger voice than ever, shifting the power balance from editors to the people actually consuming the content. Yet, too many organizations create monthly analytics reports without first considering what the data means, what their audiences actually want, and how to use data to drive better decisions. This hinders their ability to pursue the stories their audiences are hungry for.
Modern newsrooms think differently about data. Here, we distill some of their best practices for your teams to put to use.
Start with a question.
Instead of measuring retroactively, top newsrooms bring together analysts, writers, designers, and developers to establish the big questions that underpin their effort.
We asked Joshua Lasky, The Atlantic’s senior manager of data and analytics, what those meetings are like. He told us: “Together, we come up with questions like: Who are we targeting? How do we think this initiative will affect user behavior? Are we tracking dimensions and metrics that we need to understand changes to their behavior?” Looking at these deeper strategic questions is critical to keeping the big picture in mind. Every initiative at its most basic level should be geared toward developing a deeper and clearer understanding of your audiences.
Don’t accidentally discount your loyal core.
Analytics and content teams have a tendency to look mainly at their top-performing content. This inclination is understandable: If you know the secrets behind your greatest hits, perhaps you can replicate them again and again. The truth is, these top performers can be fickle or misleading. They are inflated by passersby — those who may be attracted to a story but may only be single-serving visitors.
If you pay attention to only your most popular stories, you get a shortlist of your biggest successes. But if you look at top-performing data alone, you end up prioritizing large platforms on big pieces rather than understanding the loyalty of audiences to a certain kind of content or platform.
“Don’t get distracted by the shiny viral hit. Pay attention to what your most loyal audiences come back to again and again,” said Lasky. Use their preferences and habits as a gut-check when evaluating other initiatives you pursue on the website, for example, or products that are otherwise new. If the loyal audience responds well to new features, then you know they will build that loyalty over time.
Understand the nuances of your loyalists.
Look at your audiences — even your loyalists — as more nuanced than just one homogenous group. Treat each new initiative as an opportunity to define their differences and create segments that help you better understand them and how much they value your content.
CityLab takes this approach to understand how their target audiences respond to content. Using Google Analytics, they look at demographics data to better understand key trends. They segment their audiences by interest — construction and planning, environment, and transportation, for example — and look at how each interest group engages with articles.
Constantly question your assumptions.
Working with analytics comes down to constantly questioning your assumptions and challenging them. “Just because you have defined the way you think about a specific success metric doesn’t mean those assumptions will be true in six months or a year,” said Lasky. If you’re working with digital analytics, you need to constantly reevaluate.
Working with digital analytics doesn’t mean you need to have a dedicated team of people behind the effort. If you don’t have an analytics team, find those interested in data at your organization, and bring them together to answer specific questions. If you hold one monthly meeting focused on answering one big question, by the end of the year you will have answered 12 big questions about your business with data. This has huge implications for how your organization can make better decisions with minimal analytics investment.
It’s essential to have basic data literacy throughout your organization at every level. With basic data literacy, employees in every kind of work can explore, chase down trends, and challenge assumptions that impact the business as a whole.