Five mistakes publishers make with data

Original author: Sachin Kamdar
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What do most people associate with analytics? Mostly metrics and measurements. Is this true? Yes, analytics begins with data that can be collected, ordered, and compiled into recognizable models.

However, to be limited only to metrics and measurements in analytics means to limit your capabilities. After all, the most important thing is the transition from metrics to action. This is where the difference lies between a million readers and a million readers interacting with you as you would like .

How do publishers move from dimensions to actions? Through a clear understanding of data and experiments on it. You need to use effort to distract the team from looking at the numbers on the screen and focus on actions. We met several cool companies that started doing this, and we want others to start using their experience.

So, let's start our list of errors.

Monthly report on 10 most popular articles

A list of the most (and least!) Popular articles of 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 delve into the details. Compare the features of the most popular articles with each other, 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 graphs for 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 at some scary graphs, then pulls out the most popular URLs from the heap over the past month and inserts it into an Excel file? Great idea, that's just the implementation so-so.

Use the analyst’s time to interpret the data, not collect it. Organizations that automate the process allow the team to spend time saved on experiments to increase audience and profit.

Assessment of the audience as a total: “X per month”

Not all of your readers are the same, and you need to treat them differently. Regular reader? Give him relevant recommendations (and not only automatic, but also editorial). Twitter visitors read more about Navalny than about Apple? Make sure people in charge of planning know this.

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

Real-time analytics

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

It’s very useful to see how things are going in real time these days, especially when you need to be in a trend and react to events as they arrive. True, the benefits of this are usually short-term. For more correct strategic actions, you need a historical analysis that puts your current ups and downs in context.

The use of analytics only for external processes, but not internal

Obviously, the simpler you make the thing, the more likely people will use it. And even if you collect all the data in the world, it will not bring any benefit to readers if your editors and authors do not understand it, or cannot squeeze it into your busy schedule.

Although learning how to use data plays a huge role, directly integrating analytics into the newsroom workflow will also help bring ideas to life. It can be either modules built into the CMS that display the necessary information in the current work of journalists, or regular automatic sales reports.

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