Content Analytics for Publishers

The Publisher’s Guide to Content Analytics

How to use content analytics to grow your audience, boost revenue, and make your website more valuable to readers.

Terms like news value and editorial judgment get tossed around newsrooms each day, but increasingly, the real decisions at media publications are being made based on content analytics.

Content analytics platforms, like Google Analytics, Chartbeat, Parse.ly, and others, are providing publishers with the data they need to make smarter editorial and business decisions. Like what, you might ask?

Using content analytics platforms, digital publishers are determining which writers are generating the most page views, which topics are of the most interest to readers, and which stories are doing best on social media platforms, like Facebook and Twitter.

To better understand how digital publishers are using content analytics to inform their editorial strategies and refine their business practices right now, we put together this guide to content analytics for digital publishers.

How to Use Content Analytics – Top Strategies for Success

Strategy #1: Conduct internal benchmarking by audience.

Older readers react differently to content than younger readers, and engaged audiences care more about certain topics than drive-by website visitors. Digital publishers who are interested in reaching certain target demographics can use content analytics to pinpoint what those audiences are most interested in and then push that content to the max. This doesn’t just have to do with general article topics, either. Once they’ve targeted the demographic they’re trying to reach, publishes can hone in on the specific authors and types of content (videos, articles, podcasts, etc.) that certain readers are most likely to engage with.

Bringing targeted audiences back to the website isn’t just about creating more engaged readers. It’s also about demonstrating to advertisers the value that the publication provides and its ability to reach the audiences that advertisers are interested in selling to.

With content analytics, publishers can dig deep into the interests of certain audiences and then continue working to beat their personal bests, in terms of page views and engagement levels.

Strategy #2: Put conversions at the top.

Page views and average session duration are important metrics for digital publishers; there’s no doubt. But the most advanced content analytics platforms give publishers a way to associate conversions with content, as well. How effective was a native advertising campaign or a sponsored article at driving people to the advertiser’s website or the product being sold? With native app integrations, publishers can compare engagement and loyalty metrics for both native and web audiences. They can also evaluate the success of advertiser campaigns over a certain period of time, comparing total engaged minutes to page views and average engaged time for any number of websites or articles.

Strategy #3: Grouping websites together for comparison.

As the digital publishing ecosystem evolves, consolidation is occurring. It’s not uncommon now for local publishers to have not just one, but dozens—if not hundreds—of local news websites under their umbrellas.

Publishers who fit this description can use content analytics platforms to see how stories, writers, and referrers are performing across their entire network of websites. They can also compare metrics between websites in a network, or just in certain verticals. This strategy is particularly helpful as publishers look to measure the performance of smaller websites in their portfolios.

And what about publishers who don’t own a dozen or more online properties? Consolidation using content analytics can help them, too. These same techniques can be used by publishers as they compare traffic in certain sections of their websites. For example, how are the articles in the Sports section performing relative to the articles in the Arts & Entertainment section of a news website?

Strategy #4: Maintaining an analytics-informed approach, versus analytics-driven.

Publishers who focus too singularly on page views and website traffic are missing the point of content analytics. In an editorial setting, analytics let publishers know how different segments of their audience are interacting with articles and other website content.

The distinction here has to do with audience segment versus audience size. Experienced publishers know that there is more to growing a digital news publication than just attracting the greatest number of eyeballs. Publishers who want to reach out to certain segments of their audiences—for example, the most dedicated and engaged readers—will sometimes run stories that are of interest to those readers specifically, even if the public at large could care less. With the right content analytics platform in place, publishers can make sure their targeted content is reaching the right people and having the desired effect.

Have you tried using content analytics? If you’re not sure how to integrate this type of platform into your website, then now is the time to change that. We can help with everything you need to increase conversions and establish new revenue channels as a digital publisher.

Metrics to Achieve Long-Term Revenue Growth

This Retail Metric Is Helping Publishers Achieve Long-Term Revenue Growth

Online retailers have been using the customer lifetime value metric to gauge the success of their retention strategies for years. Now, savvy digital publishers are getting on board with the same technique as they search for new ways to achieve long-term revenue growth.

The customer lifetime value (LTV) metric is a new one for many in the digital publishing industry. The metric is designed to measure the value of a customer to a business over the course of the relationship.

Rather than looking at customer interactions and purchases in a silo, ecommerce retailers figured out years ago that they would be able to see a more complete picture, and more easily achieve long-term revenue growth, if they looked at the value of a customer over the complete course of that customer’s relationship with the company.

Tracking the LTV of customers often results in retailers paying more attention to customers who buy a few items a month over a long period of time than customers who purchase just one item and then never return to the retailer’s online store. In the long run, businesses generate the most revenue from loyal customers who incrementally spend money over a period of years.

What does the LTV metric have to do with digital publishers looking to achieve long-term revenue growth?

As more publishers adjust their business strategies to include subscriptions and memberships programs, interest in tracking the long-term value of individual readers has grown.

A publication’s most loyal readers are also its most valuable. The longer that relationship continues, the less costly it becomes to serve that reader. Over time, loyal readers can also bring in new subscribers via referrals and social media “shares,” increasing the lifetime value that much more.

The push toward using LTV as a metric comes as publishers place less and less importance on page views. Page views were once thought to be one of the most important metrics for a publisher to track. But really, all a page view tells a publisher is how many people clicked on a particular article. A page view doesn’t offer any insights into how many people actually read the article, or where they came from, or whether they decided to subscribe after viewing a particular piece of content.

How Publishers Calculate LTV

How a publisher calculates the LTV metric depends on which channels or strategies he is using to achieve long-term revenue growth. The mix of subscriptions, memberships, types of advertising, and product sales (for example, selling images, videos, branded swag, or tickets to live events) will influence which key metrics make up the LTV.

In general terms, we like to see publishers combine the eCPM of each page type with the number of pages per visit, the amount of time spent on each page, and the number of repeat visits. When combined, these metrics should paint a clear picture of the true value of each visitor, giving publishers a way to identify which topics, themes, and features are bringing readers back and causing them to subscribe.

The New York Times employs an entire data team dedicated to understanding what makes casual readers decide to subscribe, and why long-time readers decide to cancel their subscriptions. The team looks at the behaviors leading up to conversions (when readers decide to subscribe). They have found that frequency breadth and depth are good signals that can predict when a reader will subscribe, helping the media organization achieve long-term revenue growth.

Knowing that the more frequently a visitor comes to the NYTimes.com website the more likely he is to subscribe, the publisher has boosted its tactics for bringing casual readers back for more. One of the ways The New York Times does this is through email newsletters, which the publisher has found to be a highly successful in converting casual readers into subscribers.

A decrease in website visits and engagement can also be viewed as a warning sign that a reader is about the cancel his subscription. For example, someone who used to read seven articles a week and now reads just one or two is at a high risk of cancelling.

If you’d like to learn even more about how to convert readers into subscribers to achieve long-term revenue growth for your publication, then let’s chat.

audience engagement metrics

What Matters More, Audience Engagement or Page Views?

At this point, most digital publishers understand the importance of web analytics. While page views will always have a role here, audience engagement is taking the lead for publishers interested in measuring the success of their editorial content.

A decade ago, it was expected that publishers would look at page views and unique visitors as they evaluated the success of certain articles or sections on their websites. Most publishers were under the assumption that the more page views an article amassed, the better the article was. Visitors who enjoyed an article would have a reason to stick around the website, either clicking through other pages or returning the next day to see if more content had been posted about the same topic.

To a certain extent, publishers’ initial focus on page views makes sense. In addition to promoting loyalty, page views were also an indication of how much display advertising revenue the publication could expect to bring in.

In the years since news publications moved online, however, there’s been an industry-wide shift away from looking at page views as a key performance indicator.

For starters, page views alone are not enough to indicate whether a visitor enjoyed a particular article, or whether the reader even finished the article in its entirety. Page view metrics also do a poor job of measuring what kind of opinion readers have of the publication overall and whether they plan on returning in the future or becoming paying subscribers. These are just a few of the reasons why, as an industry, news publishers have transitioned away from page views and started looking more closely at audience engagement metrics.

Audience Engagement Metrics

Audience engagement has become increasingly important for publishers who want to boost their CPMs for display advertising revenue and convert first-time readers into paying subscribers.

Whereas page views measure the specific number of visitors clicking on a website, audience engagement metrics are much broader in scope. When most people talk about audience engagement, they’re talking about the extent to which visitors are interested in or involved in the content on a website. Shares, comments, time on the website, and offline impact can all represent audience engagement. Many publishers combine two or more of these metrics—for example, shares and comments—to determine audience engagement.

What publishers are discovering by tracking audience engagement is that audiences enjoy reading articles about certain topics more than others, and that certain kinds of stories do a better job of converting first-time readers into paying subscribers. Tracking audience engagement gives publishers a way to hone in on these topics.

For publishers who frequently post video, video completion rates are almost always a part of the equation here. Knowing the number of people who clicked on a webpage with a video (page views) is less useful than knowing the number of visitors who watched the video in its entirety. Knowing the engagement metrics for their video content, publishers should have a clearer understanding of how the length of content impacts completion rates. They can also compare completion rates for videos posted on different platforms and users on different devices.

Paywalls can make it harder to track the impact of page views, as well, since articles behind a paywall will always generate fewer page views, even if engagement with those articles is much higher. This is one of the reasons why many digital publishers stop using page view growth as a key performance indicator after putting up metered paywalls.

Analytics platforms today can get incredibly granular in the way they track audience engagement metrics, segmenting users by loyalty and demographics. A few questions that publishers can answer by looking at their audience engagement metrics are:

  • Do readers coming from Facebook stay longer than readers coming from Twitter?
  • How long does the average reader from Google stay on the site before leaving?
  • Are readers living in certain areas staying on the website for longer?

Publishers aren’t the only ones interested in audience engagement metrics. Advertisers are interested in the information, as well.

Page views and impressions can only tell an advertiser so much. For a clearer picture of what they’re buying, advertisers will often ask for audience engagement metrics. Advertisers want to know that readers are engaging with the content on a publisher’s website and that the display ads they purchase will result in clicks and sales. In this way, tracking audience engagement metrics makes publications more attractive to potential advertisers, upping the CPM rates that publishers can charge.

directory publishers analytics metrics

The Most Important Analytics Metrics for Directory Publishers

Important decisions shouldn’t be left up to gut feelings. Using analytics metrics, directory publishers can get a big picture view of how their websites are performing and where areas for new opportunities exist.

Directory publishers don’t just have to worry about search engine traffic and visitor engagement, although those are powerful factors that can play a major role in impacting the bottom line. They also have to think about advertisers and the businesses signing up for paid listings. The latest analytics metrics give directory publishers insights into how visitors and advertisers are finding their websites and what makes them convert.

The goal here is twofold. Directory publishers want to use analytics metrics to make smarter business decisions, and they want to gain a deeper understanding of how visitors and paying advertisers are using their directory websites. Let’s take a closer look at what that means.

1. Top Keywords

How are people finding your directory? The answer may not be what you think. Using Google Webmaster Tools, directory publishers can find out what keywords are driving the most traffic to their sites. Navigate to Search Traffic, then Search Queries to see a list of the keywords driving traffic to your directory. You should see the click-through rate for each of these top keywords, letting you know how often someone clicked on your directory over another Google listing. Another option here is to use Google Analytics. Click over to Acquisition, then All Traffic, then Channels, then Organic Search.

Most directory publishers see 75% to 90% of their search volume coming through the top 200 phrases. For example, publishers with restaurant directories may find that most people are landing on their sites after typing Top [City] Restaurants or the name of a specific restaurant with a listing on the directory.

Regardless of what you discover through keyword analytics, you’ll want to use the information to optimize your content and take advantage of the keywords people are using.

2. Visitor Engagement

Clicks, shares, and time on page are all trackable metrics that directory publishers can look at as they gauge visitor engagement on their websites.

While engagement is often confused with reach, particularly when it comes to analytics metrics for online directories, they actually tell us two very different things. A directory’s reach is determined based on the number of people who see it, even if they only see it for a moment. Publishers can boost their reach by using clickbait headlines or landing pages that are only minimally related to the content in their directories. Are those stunts worthwhile in the long run? Probably not. Visitors who arrive at a directory under false pretenses—for example, thinking they are getting restaurant coupons when they are actually just seeing business listings—are likely to leave quickly and not return.

Engagement is something else entirely, and there’s a reason why we encourage directory publishers to focus on engagement over reach. Tracking engagement means looking at how involved visitors are with the content in a directory. There’s a number of ways to measure that. One idea is to track comments and shares. People don’t usually leave comments unless they are legitimately interested in the content. Tracking how commenting ebbs and flows over time, and which directory pages are receiving the most comments, can provide you with insight into how you should format landing pages or promote your most popular directory listings.

Another option here is to track scroll depth. Scroll depth means how far down a webpage a visitor scrolls. If a visitor is scrolling down to the bottom of a “Best Of” list or a directory listing, there is a good chance he is engaged with the content.

3. Email Capture Rates

Many directory publishers use email marketing to bring visitors, and advertisers, back to their websites. For these publishers, website email capture rates show how what percentage of website visitors are subscribing.

Determining a website email capture rate is fairly straightforward. Just divide the number of new email subscribers acquired via the directory website over a period of time (one week or one month) by the total number of unique visitors during the same time period.

Let’s say that through this process, a publisher learns that .1% of the visitors coming to his business directory are signing up to receive a monthly email newsletter. The next question is, how do you increase web-to-email conversion rates? A little bit of A/B testing can help determine whether simple changes to capture forms or landing pages could be enough to see major improvements.

What metrics do you analyze, and how could a deeper analysis of the trends lead to greater revenue on your directory? We’d love to learn more about what you’re doing and how we could help take your online directory to the next level.

Data Journalism Tools

The Small Publisher’s Guide to Editorial Analytics

Numbers don’t lie. For local publishers looking for new ways to boost traffic and click-through rates, editorial analytics can serve as a roadmap to success.

Rather than polling readers or simply guessing which articles will be most popular, more publishers are now relying on audience metrics and editorial analytics to inform their newsroom decisions.

Editorial analytics platforms can be setup to measure visitor activity on a publisher’s site. With popular platforms like Chartbeat and Parse.ly, publishers have the ability to track readers on their websites in real-time. Analytics platforms also track whether site visitors are actively reading, or whether they are just skimming content and saving articles to read later.

With this data in hand, publishers can make better decisions about which topics or stories to cover and how prominently certain articles should be promoted on their sites.

Three examples of how analytics can be used to make newsroom decisions include:

  1. People in the community may say they love reading stories about the public library, while editorial analytics suggest that sensational crime stories are actually driving the greatest engagement.
  2. Editors can track how small changes to published articles, such as changes to headlines or additional links to outside sources, impact how readers engage.
  3. When doling out annual bonuses and selecting candidates for promotion, publishers can look at reporters-specific metrics to determine which staff members are bringing the most value to the organization.

Should editorial analytics always be used to determine which topics get covered in a local publication? The answer to that is tricky. Just because a certain topic doesn’t generate traffic doesn’t always mean it’s not a topic worth covering. These are difficult questions that journalism ethicists have been debating for years.

In the years since digital-first publications like The Huffington Post and Gawker first started using analytics to make editorial decisions, the practice has gone mainstream. Many of the most popular tools for collecting this data at large media outlets have since been adapted for smaller digital publishers.

As a best practice, editors should consider asking themselves these questions when deciding the best ways to utilize editorial analytics in the newsroom:

  • Which readers are we trying to reach?
  • What types of reader behaviors do we want to cultivate or encourage?
  • Which metrics are we using as benchmarks for success?

In a survey of news editors, CEOs, and “digital leaders” conducted by Reuters Institute, 76% said improving the way newsrooms use data to understand and target audiences is going to be “very important” for their organizations.

Larger newsrooms have added analytics teams to the mix at a furious pace. Audience development editors and data analysts pour over the data to uncover new areas for opportunity. In smaller newsrooms, journalists themselves have access to analytics tools and metrics for their own published stories.

For publishers who’ve decided to start using editorial analytics to make strategic newsroom decisions, the next question is which platforms or tools to use. We’ve put together a list of some of the top choices for small and mid-size publishers who run their websites on WordPress.

Top WordPress Plugins for Editorial Analytics

  1. Chartbeat: For existing Chartbeat users, this plugin makes it easy to install Chartbeat’s code and start tracking website traffic and audience behaviors.
  2. Google Analytics: The Google Analytics plugin for WordPress connects publishers to Google Analytics and lets them see how visitors are finding and using their websites.
  3. Parse.ly: Designed for writers, editors, and website managers, Parse.ly helps publishers understand how audiences are connecting with the content they publish.
  4. Google Analytics Post Pageviews: This plugin links to a publisher’s Google Analytics account to retrieve the pageviews for individual articles or posts.
  5. Clicky by Yoast: Publishers who use this plugin can track individual posts and pages as goals and also assign revenue to specific pages or posts.
  6. Crazy Egg: With Crazy Egg, publishers can see exactly what visitors are doing on their websites and where they are clicking. They can also see where visitors are coming from and what types of content are bringing people back.
Data Journalism Tools

How Local Publishers Use Analytics to Make Editorial Decisions

In digital newsrooms across the country, editorial judgment is being replaced by web analytics. Which news events should a hyperlocal publication cover, how much coverage should a particular story get, and what sort of resources should be thrown at it? Those are all measurable questions that can be answered by looking at a publication’s web analytics report.

According to a study by the Donald J. Reynolds Journalism Institute, editors of mid-size community newspapers are more likely to base editorial decisions in part on web analytics. Ninety-percent of editors receive web analytics reports that show page views, length of visit, and traffic on their websites, and 49% make decisions about which topics to cover based on those web analytics reports.

Google Analytics and Chartbeat are two of the most popular tools for tracking publishing metrics, like time-on-site, time-on-page, and engaged-time-on-page, in real-time. Sometimes these sources can give conflicting answers about the success or failure of a particular article. That’s because Google Analytics and Chartbeat each have their own way to count visitors. But larger trends should still help guide hyperlocal newsrooms in their editorial decisions.

Just as no two publications are exactly alike, no two editors measure content performance in exactly the same way. At some local publications, analytics are used to make day-to-day decisions that optimize traffic and reach, while other publishers utilize the same data to form longer-term business strategies. There is no right way or wrong way to use web analytics to make editorial decisions. Context, priorities, goals, and expertise all go a long way in determining how digital publishers use the web analytics they collect.

Understanding the Audience

Web analytics make it possible for local publishers to get a clear view of who their audience really is. It’s easy to assume that readers engaging with hyperlocal content must live in the surrounding community, but is that really the case? Without analytics to back up their assertions, publishers are essentially flying blind.

A few questions that publishers can answer with basic analytics tools include:

  • Who is visiting the website, including location, age, gender, and income
  • How are readers interacting with content
  • What types of content are readers engaging with the most
  • Where are readers arriving from
  • Which segments of readers are most likely to share content
  • Where do readers go after they leave the publication

Gauging Reader Interest

Publications like the Wall Street Journal, and many others, rely on algorithms and web analytics reports to gauge which topics and stories readers are most interested in learning about.

Page views can help determine which topics readers are interested in, but when they’re used in a generic fashion, they can also send local publishers down the rabbit hole chasing celebrity slide shows and other content that’s irrelevant to their niche in the market.

One solution that many hyperlocal publishers have settled on is to keep a closer eye on engagement metrics, like time-on-page, which offer more insight into how the audience is receiving a particular piece of content.

Improving Headlines

In addition to influencing the coverage that certain topics receive, web analytics can be used to improve the quality of headlines. Publishers can test different headlines to see which keywords attract the most attention or generate the greatest click-through rates.
Intrinsic factors that made print headlines meaningful are lost in the digital world. A successful headline for an online publication is one that can stand on its own in a social media post and also inspire people to click on a link. “Descriptive and direct” is the definition of a great headline most often used in digital newsrooms today.

Keyword selection is incredibly important, as well. Headlines filled with puns, but missing keywords that describe what the article is actually about, are duds in an online world. Editors can improve an article’s traffic by using descriptive keywords and short, punchy headlines.

Of course, data rarely tells the complete story. In an ideal world, editors would be combining qualitative judgment with their own journalism expertise to make important decisions about what topics their reporters will cover.

If you’d like more guidance on how to use web analytics to improve your own editorial workflow and cultivate a more engaged readership, let’s connect.