Media and Publishing Content Performance

As a publisher or content creator, if you had a nickel for every time you heard: “This is going to go viral — I just know it!” well, you’d probably have a lot of spare change.

Whether that content ended up going viral or not each time, you were probably at a loss to explain why.

Most media analysts are still tied to the traditional content performance measurement – which means measuring popularity. What’s popular? Say a major breaking news story in the New York Times gets viewed and shared many times, thus making it a popular article.

The thing is, measuring popularity is not enough. It’s old news by now. What if you want to know the effects of your content now, or at least in the very near future?

For instance, maybe as a publisher, you want to know what articles will be popular for the rest of the day or tomorrow. Or the rest of the week? What if you want to know which segment and what medium to promote these types of articles in order to monetize more effectively?

If a publisher wants to measure a boost of popularity over a short period of time, they’ll want to measure virality. The question is — how?

Virality can be measured through analyzing audience behavior over time. Time-series behavioral analytics enables us to dive deeper, slicing and dicing the data and segmenting the characteristics of both the viral content and the behavioral profile of the audience that made it viral.

The result is a clearer picture of how your audience responds to content over a specific period of time and a greater understanding of the type of content that is likely to be shared in the future.

This post is taken from the complete guide for Media and Content Analysis
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Popularity is a Never-ending Series of Snapshots

Successful content is content that attracts an audience. The larger the audience, the more successful the content.

One method of measuring this ability to attract an audience is according to popularity. Content popularity can be measured by the number of views, the average time spent on the article, or the number of views or shares. Unlike virality, however, it isn’t specific to short timeframes.

For example, these are the most popular content pieces from the New York Times in 2015 measured in total combined time readers spent reading them:

The Top New York Times Stories of 2015, by Total Time Spent

The Top New York Times Stories of 2015, by Total Time on Page
source http://www.nytimes.com/interactive/2015/12/09/upshot/top-stories.html?_r=3

That’s all fine if all you want are continuous snapshots of your user journey and don’t care about how long it will take for your content to become popular. But if you want better insights, you’ll have to dig a bit deeper.

Virality Measures the Immediate Popularity of Your Content

If you want to gain a better understanding of the immediate effect of your content on your audience, you’ll want to start measuring virality. Virality is much more than just counting the number of pageviews and shares of your content; it takes into consideration the time that elapses between the publication of that content and the time the content was shared, as well as the clicks, pageviews, shares and conversions that were a direct result of those shares.

Virality is commonly measured using the K-factor. In a nutshell, the K-factor is measured by the number of invites and invite conversions. A K-factor greater than one indicates exponential growth.
The K-factor should be part of any publisher’s dashboard, along with other must-have metrics. The K-factor of each article over time, since it’s important for monetization and for setting the ad price by the best parameter.

Content Performance analytics dashboard

Content Performance Dashboard containing the K-Factor

Since virality is measured in short timeframes, it is best visualized using the cohort report. Like in this example below that shows the percentage of shares over a five-day period after publication. The segment of users who shared the most are those who first came to the site on February 1st, 2016.

Cohort Analysis of Content Performance

If we’d like to learn more about these users who shared the most, we’d have to drill down a further to see that paying readers share the most:

Content Performance Cohort Analysis by User

Now, if you want to increase the number of shares among this segment of users, you’ll want to understand the type of content these users are sharing the most:

Virality Content AnalysisThese are the types of categories and articles you’ll want to make sure are suggested content for paid users after they complete articles. (And you’ll probably want to pay more attention to articles with a K-factor higher than 1).

Predicting Virality

Measuring content success and its influence on users is certainly challenging. Different metrics and trends are constantly evolving in the world of digital media, including attempts at predicting whether a particular piece of content will get viral. These types of predictions include various studies into the psychology of sharing and the type of emotional triggers that can increase the probability that a piece of content will be shared.

While these types of studies are important, time series behavioral analytics that measure content performance is a more accurate way to predict content virality. By analyzing past behavior and segmenting our audience  according to user type, category and article preference, we gain a clearer understanding of the path users might take in the future. From there, it’s a quick path to optimization and capitalizing on user attention.

To summarize –
First, when coming to measure content performance, don’t mix popularity and virality  – it’s not the same. Popularity is a metric that stands alone, without any relation to time. In the digital publishing world, where days, hours, and minutes makes a difference in content success, virality is the more meaningful metric.

Secondly, once you’ve analyzed your past data and are able to predict virality, you can focus your marketing efforts accordingly. Understanding timebound past sharing patterns of a specific audience or segment as well as their individual characteristics (their age, country of origin, preferred method of content consumption, etc) gives us the ability to predict future consumption in short timeframes among similar audience segments and to focus marketing in short timeframes to maximize their potential virality.

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Hagit Ben Shoshan

Hagit is the VP of Customer Success at CoolaData. She is a big data scientist, and a time-series behavioral analytics evangelist. Hagit is part of the founding team that created the core data models used in the platform. She now works with global companies, helping them analyze user behavior, get meaningful insights and answer complex business questions. Hagit has over 20 years’ experience working as DBA consultant and BI manager for companies like Oracle and Air France, using data mining and predictive modeling techniques to drive digital success. Hagit has a B.A in Psychology and MBA in Data science and knowledge management.