5 mistakes publishers make with their data
This post was written by Parse.ly CEO Sachin Kamdar
What do most people think of when they think of analytics? Metrics and measurement, mostly. Is this wrong? No. Analytics starts with data that can be counted, sorted and arranged into recognizable patterns.
But limiting analytics to just metrics and measurement limits what you can do; the power in analytics is in the translation from metrics to action. It’s the difference between knowing you have one million readers and having one million readers interact with you — in a way you strategized for.
How does a publisher make the leap from metrics to action? By powering every decision through a keen understanding and application of data. It takes leadership to steer a team beyond staring at numbers on a screen and inspiring them to act on those numbers. We’ve all seen some great companies do it, and we want to make sure all publishers start taking the steps they need to get in the game.
Are you making any of these five mistakes?
You get a monthly report of the top 10 articles.
Seeing the most (and least!) popular articles can be very helpful — but only when you know why they were popular (or not). What’s the best way to do that? By examining their details. For example, compare the most popular articles to their less popular counterparts; consider where the traffic came from; and pay attention to which articles are read afterwards.
We encourage editors to useweekly digest reports about an author. They include the number of stories created that week, comparative metrics to the week before, referral and traffic sources, and top articles as a conversation starter. Using these, the person most familiar with the topic and audience can dig into the details and find information that she can incorporate into future work.
You have an analyst pulling those top 10 article reports.
Do you have an analyst who’s sitting in a complicated analytics program, pulling out the top ten urls from the past month for each author, editor or section, and creating that Excel file? Great concept, bad execution.
An analyst’s time is better spent on data interpretation, not data gathering. Organizations that find ways to automate basic analysis have teams that can spend more time on game-changing work — like creating experiments to drive more readers and revenue.
You look at your audience as one unified number: X per month.
Not every reader who comes to your site is the same, and they shouldn’t be treated that way. Are they a frequent visitor? Consider personalized recommendations. But don’t be limited to product decisions: Editorial should also parse out to readers. Are your twitter referrals more likely to read about the FCC than Apple? Make sure your social team knows that.
If you aren’t looking at your audience in segments (each with its own preferences, behaviors and needs), you may only satisfying be a small percentage of your overall readership.
You’re looking at real-time analytics.
Okay, this was a trick; We actually support this one. The mistake here is limiting your real-time data to only this information, without longer-term context.
Seeing things happen in real time is critical these days, especially when you’re able to jump on trends as they happen. But those are often short-term gains. To make better, strategic editorial decisions, you need historical analytics that put the real-time losses and gains into context.
You use data to make product decisions for your external systems, but not your internal ones.
Ask any manager: People are more likely to do something when you make it easy for them. Using all the data in the world won’t help your readers if the newsroom or editorial teams can’t make sense of it or fit it into their already overburdened work day.
While training is certainly an option, designing data and analytics into the newsroom can also help all these solutions come to life. This might be a CMS integration that shows contextually related stories, or it may be easy reporting creation so your ad teams can spend more time selling.