Tony Hsieh, CEO of the wildly successful Zappos, has a theory: he believes that whatever your goal is, you should ask yourself “why?” and keep asking. His theory isn’t about measuring content performance; it is about understanding your purpose.
So if you are asked what your goal is and you respond “making a lot of money,” ask yourself “why?” And if your answer is “to buy a bigger house,” ask yourself “why?” At the end of this exercise, you will inevitably get to a “why?” question whose answer will be “to be happy.”
Hsieh and other followers of Positive Psychology believe that our goals are but milestones on our path to something much more significant: happiness.
There is a valuable lesson in this philosophy when applied to analyzing and measuring content performance.
As publishers and content creators, interested in the performance of our content pieces, when we ask a question of “why?” we get clearer, more valuable insights than when we ask “what?”
For example, we could ask what kind of content gets the most pageviews… or we could ask why that content gets viewed most. We could measure what article gets shared on social media or we could analyze why users shared one article over another. We could determine what our most monetized pieces are, or we could explore why a certain piece went viral and generated exponential revenue gains.
To quantify the answers to these more significant questions, content publishers must be able to analyze online user behavior – not just clicks or shares, but the path that led to content consumption and distribution.
Adding a Dimension to Content Performance Analysis
In 1964, Isaac Asimov published a collection of scientific essays called “Adding a Dimension.” In it, he explained, “There is not a discovery in science, however revolutionary, however sparkling with insight, that does not arise out of what went before.”
The same is true for analyzing content performance. We still regard our pageviews and clicks, but when we add more dimensions, the legacy KPIs become the base for the multi-dimensional insights we can use to gauge content performance.
For example, reporting pageviews of content may show that an article called “Brits return Keane to number one” had over twice as many pageviews as the next article. Based on this information, we would seek out more pieces of this style.
But if we explore the raw data and add more dimensions, like completion rates and share statistics, we realize that this may have not been the strongest piece. In fact, “Asylum children to face returns” had a much higher completion rate and more than double the share rate:
“The journey of a thousand miles begins with one step,” said Lao Tzu. The Chinese philosopher would likely agree that this first step defines the user journey. When we assess content performance, an important element of user behavior becomes the origin: the acquisition channel.
By understanding where users began their journey, then adding a dimension of engagement, we can study a cohort analysis, for example, of engaged users who discovered content pieces on a social media platform.
To this analysis, we can add the dimension of time to uncover how many times those users returned to consume additional content pieces.
By studying the journey and analyzing user behavior over time, we replace a snapshot of activity with a well-defined time series analysis that enables us to truly understand why users behave as they do and how that affects the performance of our content.
Measuring User Loyalty Pays Off
Another important metric in content performance is user loyalty. Gauging loyalty however, must be looked at from the perspective of why.
Case in point, knowing that 6.3% of the readers in the technology section return 28+ days later is understanding the what of user loyalty. Drilling down to understand that the highest loyalty rate comes from users who consume content in a certain category helps us understand the why.
What is our loyalty? 6.3%
Why do 6.3% of our technology content consumers come back 28+ days later? Because something they read within the technology section compelled them to return. and we can certainly analyze the raw data to discover what it was.
We often mistake popularity for virality.
A popular content piece is one that is shared the most, regardless of time.
Viral sharing is commonly measured in short timeframes, from one to three days after publishing. The content pieces that achieve exponential growth within a couple of days are the ones that can be best monetized.
Properly accounting for virality of content performance requires the added dimension of time. Without it, we can ascertain which content piece is popular (by gauging a KPI of number of shares), but virality is about popularity achieved quickly, measured by the K-Factor.
Using time-series behavioral analysis, we can analyze social share patterns over time to determine a virality score per content piece in real time.
By cross referencing this data with user types, we can understand not only what kind of content goes viral, but also why. For example, a post about kittens went viral because it was shared both by Millennials and by Baby Boomers. Therefore, one actionable insight based on this data may be that when we create content that appeals to both of these generations, they will likely both share and cause virality.
Content Performance Happiness
Measuring content just by factors like pageviews and shares was a first step. Now that there are more mature models, we would be remiss not to account for multi-dimensional analysis that give us insights into the user behavior that leads to more pageviews.
When we ask why and study behavior patterns and the trends they generate, we get a full picture of the journey – one we can understand and leverage to improve our content performance. Why? Because when we improve performance, we increase monetization. Why? Because with greater monetization we can earn more, do more, have more… and be happy!
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