How to Build Data Literacy in Your Media Organization

Media organizations are increasingly using analytics to drive editorial strategy, and most acknowledge the need for cross-departmental access to content performance and audience data. But having access to data is not enough. A tool is only as good as its user, so knowing how to interpret the data and act on it remains the crucial piece of the puzzle. Over the past months and years, we at Content Insights have been talking about this with clients such as Süddeutsche Zeitung, De Persgroep, and IDN Times. Together we’ve explored how they manage change, improve data literacy, and incorporate content analytics into their newsroom or media organization. We’ve found that success lies in the proper education of all members of your organization, and in a willingness to nurture a culture of knowledge sharing. Building data literacy and transparency takes time and effort, but it is doable. Here’s how to lay the foundation. Understanding the Challenge of Change Management Achieving data literacy requires organizational, technical, and cultural change. This means you need to have the right data tool (or set of tools), an organizational structure with the expertise (or the capacity to build expertise) to use those tools, and a culture that is open to embracing a more data-informed approach. On paper, it looks pretty straightforward. In reality, users are often very resistant to change, especially when it comes to adopting technologies. Employees may shy away from new approaches if the learning curve is slightly steeper than they initially expected. Editors and reporters may want to dismiss data and trust their editorial gut, while data analysts may feel threatened by user-friendly analytics solutions that they believe will become obsolete. Directing your employees’ attention to the benefits of understanding and strategically using data is the first step. Inviting Everyone to the Data Table According to a Reuters Institute Report, many newsrooms are still taking a rudimentary approach to analytics and using off-the-shelf tools like Google Analytics and Facebook Insights, relying on advertising-oriented metrics that are insufficient for measuring content performance. With this approach, the focus is often on traffic and short-term commercial goals. The crucial systematic link to decision-making is missing, preventing publishers from extracting value from data and defining editorial priorities. Identifying the type of data you need – and how it can be used to transform your business for the better – is the key to becoming data literate. The best way to get this data culture flowing through the newsroom is to get everybody involved. Süddeutsche Zeitung (SZ) has managed to incorporate editorial analytics into their day-to-day routine by defining processes and establishing open channels of communication between the analytics department and the newsroom. “Demonstrating how an article harnesses engagement and loyalty is great for the editorial team and helps everyone improve their output,” says Philipp Bojen, SZ’s head of SEO and analytics. Report distribution is also a key part of SZ’s data literacy strategy. Customizable, easy-to-understand reports are automated and sent directly to their inboxes so everyone is armed with data for weekly or daily editorial meetings. There is also a dedicated Slack channel via which authors receive daily reports about their article performance. Roy Wassink, De Persgroep’s insights manager for the Netherlands, says reports are the ultimate newbie-friendly way to promote analytics use among members of an organization. There are 70 different reports circulating within the De Persgroep newsrooms in the Netherlands. “It’s all about a cultural shift,” says Wassink. “The use of data might not be something that comes naturally to journalists and editors, and although vanity metrics are certainly useful in bringing data to the newsroom, they are not what it should be about.” So, which metrics should writers and editors look at? It depends on the publication’s business model and content goals. Focusing on engagement metrics is something that works in most cases, so let’s take a closer look. Users Need to Be Data-Informed, Not Data-Blinded Incorporating analytics into your editorial workflow has to go beyond just hanging a big screen TV in your newsroom which shows only real-time numbers going up and down. Just because data is right there doesn’t mean people will understand it, care about it, or even look at it. Users need to be enabled and allowed to experiment, explore data, ask questions, draw conclusions, and make their own hypotheses. They need to learn through trial and error and understand they are in control of their output, with data on their side. Behavioral metrics such as Attention Time, Article Reads, Read Depth, and Page Depth serve as good indicators of content performance. To quantify reader behavior and make it simple for authors to understand, Content Insights developed a Content Performance Indicator (CPI) algorithm that measures audience exposure, engagement, and loyalty. Through an app, users can access their analytics dashboard, choose a time period, and evaluate their content performance by the three different behavioral models recognized by CPI, mentioned above. This way they can analyze their data, identify successful pieces that could use a promotional boost, or pinpoint topics that are underperforming. Yogie Fadila, head of editorial at Indonesia’s IDN Times, shares their practice: “Every Monday morning the editorial team presents the data from the previous week and we look at the areas which have improved, and also – importantly – the areas that haven’t. We can use these analytics to see how we can fix those issues, so it’s like seeing a mirror of yourself, but you can photoshop it.” When users see that strategic use of data can improve results, they naturally become more open to learning how to use data tools. The Future of Data in Media All members of an organization can benefit from becoming data literate. C-level executives need to be able to "speak data" so they can set an example and make strategic decisions, while internal training can equip employees across departments with the understanding of data they need to drive results. Thankfully, many analytics solutions today are very intuitive, so the learning curve is mild. Users can now take advantage of their chosen analytics solution’s customer support. In the analytics world, the customer support department has evolved into something we prefer to call “client success,” which means users get not only technical issue support, but also additional consultant services and proper onboarding. Content analytics tools need to support both short-term decisions and long-term strategy. Good analytics solutions offer data and insights relevant to your business goals and don’t impose “the tyranny of numbers.” They should offer specific perspectives for different professional roles, and reports which are easy to understand. This is how resistance towards data gets replaced with curiosity. Having access to content performance data is crucial for publishers that plan on shifting to a reader revenue model. Data can clarify how people consume content, what keeps them engaged and encourages loyalty, and what drives them to subscribe (or unsubscribe). Knowing more about your audience fuels the right strategic decisions, from picking a paywall to choosing free versus premium content. As analytics solutions evolve and the available data becomes more rich and precise, the media organizations that invest in data literacy will thrive and serve their audiences better.

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How to Build Data Literacy in Your Media Organization

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