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3 lessons you can learn from your Analytics by digging deeper

Apr 07, 2016
Knowledge Base

By Jean Hodges, Gatehouse Newsroom

Newsrooms have been staring at their analytics and sharing them for years, right? What else could we possibly learn from these numbers?

I hear editors say analytics mostly confirm what they already know. Crime, crime, crime … it’s all people want to read online.

In other newsrooms, I hear people talking about analytics and how to use them to focus on the content that really counts for readers. Here are some tips to find out how to learn from the information that comes from analytics to ensure your coverage is deep and meaningful in ways your readers will appreciate.

1. SHARE WITH EVERYONE: Editors aren’t the only ones who benefit from studying analytics. Reporters can learn from their own analytics, too.

Now: How do reporters learn about analytics? Some newsrooms have screens up in the newsroom to show real-time analytics. Reporters will stop by and take a look at the screens occasionally. And many newsrooms share a list of the most popular content on the website each day with the whole staff.

Dig deeper: Give the keys to your analytics to everyone. Hold a brown bag lunch session in which your analytics guru gives basic pointers on analytics to the whole staff. So, give everyone access and teach them how to use your analytics tools. In many GateHouse Media newsrooms, we use, which allows reporters to find out exactly how their own content is doing. Just think, your education reporter may discover that readers gravitate toward stories about standardized testing, prompting a deeper dive into those tests in schools in your area. If reporters only saw those lists of the most popular content, they might not have seen the audience trend on school testing. Some editors push back, saying some folks might be depressed to see how little time people spend with their content, but this is no reason to keep the information from your staff. Great leaders understand that information is important for growth, and they will look for ways to help struggling staff members learn from their own metrics and produce content that resonates with readers. Keep reading

Data-driven product pricing for digital subscriptions

Mar 23, 2016

This is another article from Piano’s Lead Data Scientist Roman Gavuliak written last year and re-posted here for the edification of our faithful readers. To read all of Roman’s articles please click here.

Digital pricing is not usually coupled to the cost of production. In fact, digital pricing, like that of luxury brands, is based upon perceived value. Why would people pay for the New York Times online when news and information is freely available on the Internet? Because people believe when they purchase a NYT subscription they are getting a better product, with unique, distinctive content plus they value the paper’s editorial curation. So how does this apply to online publishers who are not the New York Times?

Two words: Quality Content

Keep reading

Lessons in paid content III: Don’t just slap a meter on it

Feb 16, 2016

This is another in a series of articles from Piano’s Lead Data Scientist Roman Gavuliak that were written last year and are being re-posted here for the edification of our faithful readers.

Lessons in Paid Content I: Not all content is created equal
Lessons in Paid Content II: The size of your audience
To read all of Roman’s articles please click here.

Our last post looked at the size of a target audience for a typical publishing site with 2 million monthly unique users as reported by Google Analytics. After accounting for Google’s user overestimation and users that only skim headlines, there were between 0.5M – 0.7M monthly readers left. Now, assume the example publisher’s online team decides to implement a meter limit of 10 articles per month; one that mirrors the current New York Times limit. What follows is the monthly content consumption for users in our example (based on similar real newspapers):

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The graph shows that the share of users reading more than 10 articles (10+ category) is below 10%. This is not uncommon for many titles and means a metered paywall with a setting of 10 free articles per month affects (building on the example) between 40k – 60k users, users who can be effectively monetized. This doesn’t mean a metered paywall makes no sense, it means there must be realistic expectations about its impact.

This curve allows for optimizing paywall impact, regrettably it lacks a sweet spot where there is an optimal trade-off between pageview and user impact. However, the sweet spot doesn’t exist because article pageviews follow roughly a 80/20 rule (80% of article pageviews are done by 20% of users) and, changes impacting pageviews are really much smaller when compared to changes in terms of affected audience. Therefore there is an inherent risk associated with implementing a metered paywall with any setting.

However, there is a big upside – a more aggressive approach brings more opportunity. To wit: numbers from metered paywall estimation are presented in the following table:

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The conclusion here is: don’t be scared!

The percentage of affected pageviews is only on articles. A metered paywall leaves homepages and section fronts freely accessible. The resulting percentage loss of pageviews from all pageviews is lower and decreases further based on the conversion rate (since the pageviews of paying users are retained and might even grow as these users now attribute a monetary value to the content they read).

Back to the example; lowering the meter setting from 10 to 4 free articles per month means increasing the absolute percentage pageview loss risk by 5.6% while almost doubling the absolute share of users affected (on all users reading articles). Keep in mind too, that people reading over 10 articles per month are more likely to pay than those reading only five articles per month.

So what is the takeaway here? Ten free articles per month might sound like a nice round number and it works for New York Times; but while user distribution is based on their reading level and follows a similar mathematical function, it differs in parameters. These differences are illustrated in the graph below. One daily Estonian title has roughly 1M monthly unique visitors while a Slovak daily has 2M monthly visits and two German titles have 1.9M and 2.7M respectively.


Despite having the smallest audience, a limit of 10 free articles a month would impact the greatest share of users at the Estonian daily newspaper.

A metered paywall then, is not about the size of your site, but about understanding your audience and putting this knowledge into action.

Lessons in paid content II: The size of your audience

Feb 09, 2016

This is another in a series of articles from Piano’s Lead Data Scientist Roman Gavuliak that were written last year and are being re-posted here for the edification of our faithful readers.

Lessons in Paid Content I: Not all content is created equal
Lessons in Paid Content III: Don’t just slap a meter on it
To read all of Roman’s articles please click here.

If you ever look at your Google Analytics or get a report from a colleague, you might be familiar with the number of monthly unique users your title has. If you decide to monetize content on your site with something other than just plain old advertising, you might wonder how many users a paywall would reach. While there are users who pay for the convenience of never having to encounter a paywall, most people will have to be prompted to pay.

Imagine then your Google Analytics shows 2M monthly unique visitors; how many of them can you monetize? First though, factor in Google’s routine overestimation of unique visitors. Why? Because Google Analytics is oriented around visits and cookies for unique user identification, they do not identify unique browsers. Google isn’t really telling you the truth about unique users visiting your site because one user can visit on a variety of different browsers or devices. What this really means is Google Analytics is overestimating site visitors by up to 50%! Here’s an illustration:

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After correcting this overestimation, there are still at least 1M unique visitors, which is not that bad, right? Naturally it seems perfectly logical that for paid content to work, only those users who actually read articles are going to pay, right? No surprises here, but – make sure you are sitting down and take a deep breath because here it comes:

For most media, at least 50% of their users do not read any articles weekly or monthly! Keep reading

Algorithms, automated content optimisation are key to publisher competitiveness

Feb 08, 2016

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Chuck Blevins, the manager of new platform development and technology for the audience department at the Atlanta Journal-Constitution in Atlanta writes about the importance of hitting your audience at the right time with the right content. Piano VX software has an algorithmic paywall function that helps monetize your most popular content at the right time, turning users into subscribers. Get in touch with us if you want to learn more:

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