Five mistakes publishers make with data

What do most people associate with analytics? Basically metrics and measurements. Is this true? Yes, analytics begin with data that can be collected, ordered, and compiled into recognizable models.

However, to be limited only to metrics and measurements in analytics is to limit one’s possibilities. After all, the most important thing is the transition from metrics to action. This is where the difference lies between the millions of readers, and the millions of readers interacting with you the way you would like .

How do publishers move from measurement to action? Through a clear understanding of the data and experiments on them. It is necessary to apply an effort to distract the team from viewing tsiferok on the screen and focus on action. We met a few cool companies that started doing this, and we want others to use their experience.

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So let's start our list of errors.

Monthly report on the 10 most popular articles

The list of the most (and least!) Popular articles for the month is undoubtedly a very useful tool. But only when you know why they are popular (or unpopular). The most correct way is to go into details. Compare the features of the most popular articles among themselves, as well as with the least popular. Look at the sources of traffic in these articles, their length, style, which articles are then read later, and which are not.

We help editors do this with weekly reports. They include not only general charts on views, comments, number of articles and changes from previous weeks, but also summary information on top and outsider articles. Using this information, the editor can delve into the details and use them in subsequent work.

Special person choosing these 10 articles

Do you have an analyst who looks into some scary graphs, then pulls out the most popular URLs from the pile of URLs over the last month and inserts them into an Excel file? Great idea, only implementation is so-so.

Use analytics time to interpret the data, rather than collecting them. Organizations that automate the process, give the team to spend the time saved on experiments to increase the audience and profits.

Evaluation of the audience as a total: "X per month"

Not all of your readers are the same, and they also need to be treated differently. Regular reader? Give him relevant recommendations (and not only automatic, but also editorial). Visitors from Twitter more often read about Navalny than about Apple? Make sure the planners know about this.

If you do not break the audience into parts (each with its own interests, preferences and behavior), you satisfy only a small part of your readership.

Real Time Analytics

Well, okay, in fact, we are for analytics in real time. The real mistake here is to use current information without long-term context.

Seeing how things are going in real time is very useful in our time, especially when you need to be in trend and react to events as they come. True, the benefits of this are usually short-term. For more accurate strategic actions, you need a historical analysis that puts your current ups and downs in context.

Application of analytics only for external processes, but not internal

Obviously, the easier you make the thing, the more likely people will use it. And even if you collect all the data in the world, they will not bring any benefit to the readers if your editors and authors will not understand it, or will not be able to squeeze it into their overloaded schedule.

Although learning how to use data plays a huge role, directly integrating analytics into the newsroom’s workflow will also help to translate ideas into action. These can be either modules embedded in a CMS, displaying the necessary information in the current work of journalists, or regular automatic sales reports.

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