Hi! This is Smart Data, a new newsletter for INMA members on creating value with data analytics for media companies and incorporating a data-positive culture.
I am INMA’s researcher-in-residence at INMA. E-mail me at: firstname.lastname@example.org.
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From the editor: Welcome to the data-positive world!
With the new Smart Data Initiative, INMA aims to help transform the online news business and make journalism sustainable.
In this newsletter, I will be sharing insights and best practices in democratising data and tying it to editorial and business objectives.
I address this newsletter to decision-makers, or simply data insights users, in the boards, newsrooms, marketing and sales, product, technology, research and — surely — data analytics departments themselves.
Sign up to this newsletter today and tell your colleagues about this new resource.
CONTENT ANALYTICS. Benefits and risks of ditching pageviews for time as a key engagement metric
Mediahuis in Belgium and some other leading publishers use attention time as their North Star engagement metric, as opposed to pageviews. Should you join them?
At the INMA Smart Data Initiative’s first meet-up in February, Yves Van Dooren of Mediahuis revealed its Belgian newsrooms turned to measuring and reporting aggregated time spent on articles by readers, as this metric was universally understood by editors and well accepted.
In the follow-up call, Van Dooren explained that focusing on time instead of pageviews might help in reducing click-bait, while aggregating time spent by all readers on individual articles captured popularity of the articles, too.
Mediahuis also found aggregated time spent on the site by individual readers correlated with the likelihood they converted to paid subscribers and renewed.
Benefits of measuring time: Research by news publishers, such as this Deep.BI modelling, confirms time is predictive for a reader subscribing or renewing. In comparison to other signals, time is more predictive than the number of pageviews but slightly less than, for example, visit frequency.
Academic research, in general, confirms benefits of measuring online engagement with time:
- Scientists at Yahoo Labs found it a good indicator for user interest.
- Researchers at Tsinghua University saw it as a proxy to for content quality.
Time is also widely used in digital media beyond news. For example:
- At Facebook, time spent interacting with posts helps rank the News Feed.
- At Google, time spent on sites informs its search results.
- At Netflix and Spotify, play time guides content, product and marketing decisions. For example, a study by Caitlin Smallwood, vice-president/data science at Netflix, found the total hours spent watching was the most predictive for member retention, well ahead of movie or show ratings.
Risks of measuring time: “Although time is a popular measure,” admitted Mounia Lalmas-Roelleke, head of tech research at Spotify, in her seminal book “Measuring user engagement,” “there are significant drawbacks in using it.”
- Tracking challenge: Popular software, such as Google Analytics, tracks reader behaviours on the browser side and that method has limits. For example, it doesn’t measure the time a reader spent viewing the last page of the visit. So, if the reader views just one page, the time reported is zero. It matters because on average, 67% of all visitors to news sites view just one page in a month.
- Observation challenge: Readers often open multiple articles simultaneously in their browsers and switch between tabs. It’s difficult to figure out which tab captured the reader’s attention.
- Analysis challenge: Time varies much across pages, sections, and sites. Research by Lalmas-Roelleke found time spent on reading depended on the type of a reader, e.g., whether she is a generalist reading everything or a specialist interested only in some topics. It depended on the type of a page or a section, e.g., readers typically spent less time on a sports section and more on a home page. And it depended also on the type of a task, e.g., whether the reader searched for something or just killed time browsing.
Best practices in time analytics: To mitigate the challenges of time measurement during single sessions, Lalmas-Roelleke recommended measuring engagement across multiple sessions — for example, the aggregated time spent by a reader in a month. A version of this is used by German regional publishers who collaborate on audience analytics.
Another solution could be to analyse time in connection with other metrics, even the vilified pageviews:
- For example, The Financial Times in the United Kingdom developed Quality Reads metric that measured pageviews qualified by the threshold of time and scroll depth. For a pageview to be counted as a Quality Read, the reader needed to spend at least 50% time required to read the whole article estimated by the number of words and scroll to at least 50% of the page’s length.
- Time could be also integrated into article or user scores used by journalists or to inform algorithms that curate home pages or trigger paywalls. For example, Die Welt in Germany and Dagens Nyheter in Sweden used time in their complex article scores presented on the newsrooms’ dashboards and regular reports.
There are benefits in measuring not only engagement time but absence, too:
- News sites want readers to visit repeatedly and develop a habit. Measuring absence time may trigger engagement actions, such as notifications or e-mails, as Aftenposten in Norway discovered.
- Measurement of absence time between visits helped Der Groene Amsterdammer in the Netherlands map readers’ paths to conversion and attribute influence of individual articles on the decisions to purchase a subscription.
What’s your North Star engagement metric? How did you choose one? What are the benefits and challenges? E-mail me at: email@example.com.
INMA CLINIC: What the third-party cookie apocalypse means for our audience analytics
Every week, members reach out to INMA and me to help them find relevant research, case studies or best practices in audience analytics. Here’s a recent exchange:
Question: We are using Google Analytics 360 for audience analytics now. Would a first-party data strategy mean building our own analytics system?
Answer: It’s a simple question, but the answer will be complex.
Technically, Google Analytics and Google Analytics 360 both use first-party cookies to analyse traffic to you site. To be more precise, the so called first-party cookies are issued by your Website when a reader visits directly and are saved on the reader’s computer. Google Analytics uses these cookies to capture data about your visitors and their behaviour on your site. Nothing changes here.
What changes is what happens with the so-called third-party cookies. These cookies are issued by other Web sites than yours. For example, when a reader visits your Web site but watches an embedded video or sees an advertisement, these other Web sites — of a video host and an advertiser — try to save their cookies on the reader’s computer, too.
These third-party cookies can be blocked by Web browsers. Readers can browse in an incognito mode. Apple Safari and Mozilla Firefox are blocking them in default. The big news is that the most popular browser in the world — Google Chrome — announced it will start blocking them within two years.
It matters because third-party cookies are heavily used in online advertising. As advertisers add their tags to your page when you display their ads, they can track users who saw the ads and their devices across the different Web sites.
So, the risk of the third-party cookies being blocked is primarily to advertisers. They will not be able to track and target users, and therefore they might spend less money on traditional advertising networks. Publishers who rely on this sort of revenue will be hit, too.
This change might potentially pose an opportunity for publishers. They enjoy high volumes of users and traffic, and therefore they, in theory, collect a lot of data in comparison to other sites and they could offer their services for a higher price.
The problem is that publishers’ tech infrastructure, including analytics systems such as Google Analytics, are not really built for those purposes. They track and analyse users in aggregate. For example, Google Analytics can show you a site or page had so many views. It doesn’t really track individual users — so you cannot see that a given person saw this and this page in that order and at that time.
Without being able to view individual users’ behaviours, you cannot really group these users into segments advertisers search for. You also cannot make predictions based on users’ past behaviours — for example, whether they are likely to click on an ad or buy an advertised product. These predictions are critical to modern ad targeting.
In summary, the first-party data strategy is about building capability to view users individually, segment them, analyse the behaviours of groups and do predictions.
These kinds of analyses require different analytics tools — for example, customer data platforms. Similarly to Google Analytics, they collect first-party data about visitors to a Web site, but differently to Google Analytics, they track behaviours of individuals rather than only aggregate numbers.
The opportunity lies also in registering users and logging them in so you can collect more precise data about individuals, and in integrating other types of data, for example, on transactions or from the surveys.
The result would be richer profiles for each and every user and capabilities to group them based on those profiles and then target them with the right advertisements.
- INMA Report: The Third-Party Cookie Trigger, INMA, June 2020.
- INMA Knows: Third-Party Cookies and Advertising curated by Dawn McMullan.
What’s your question? E-mail me at: firstname.lastname@example.org.
About this newsletter
Today’s newsletter is written by Grzegorz (Greg) Piechota, researcher-in-residence at INMA, based in Oxford, England. Here I share results of my research, notes from interviews with news publishers, reflections on my readings.