With the content marketing industry predicted to be worth $300 billion by 2019, it often seems as though businesses create content for the sake of generating content.
Most B2B companies produce content with a goal in mind, whether it be for brand awareness or to position themselves as thought leaders and generate leads. But few can really analyze the success of their content marketing efforts or calculate the ROI for their content campaigns.
As Blaise Lucey, senior content strategist at Bitly, states: “Every content marketer in 2016 should be able to point to a piece of content and say, ‘This content generated this amount of value for the company.'”
Move beyond traditional KPIs to analyze user behavior
Although pageviews, number of shares, and even comments are still important content performance metrics that should be monitored, content performance measurement must go deeper. To show true value for companies, content performance measurement should demonstrate an understanding of user behavior.
In this post, we want to introduce four more accurate methods of measuring content performance:
- Measuring engagement over time
- Transitioning from pageviews to completion rates
- Moving from counting shares to measuring virality
- Combining external data to measure content ROI
We will present you with examples of each in the remainder of this post and the takeaway for brands or publishers.
Measuring engagement over time
Engagement can be determined by measuring content “stickiness, a term that refers to how many times readers come back to the content over time after the first time they were exposed to it. Content stickiness can be measured by using cohort analysis, a method of grouping users according to a specific action to determine how that action has changed over a period of time.
In measuring content stickiness, cohort analysis groups readers into cohorts based on when they first read the piece of content and the number of times they returned to the content piece over a period of time.
In this cohort, around 70% of the audience who read the article on November 21st returned to that same piece of content three days later.
Takeaway: Publishers or brands can use these insights to drive successful promotions by offering more content from each of these content categories to encourage more loyal users.
Transition from pageviews to completion rates
Pageviews measure how many times a piece of content has been viewed. But just because a piece of content has been viewed does not mean it was necessarily read (or read completely). Completion rates divide an article into sections to determine how many sections readers complete. Articles with fewer sections have higher completion scores, whereas longer articles have lower completion rates.
For instance, if we analyzed this list of content by pageviews, we’d conclude that the audience was mostly interested in the video about “Brits return Keane to number one.” But if we wanted to understand which article/video was read/watched all the way through (keep in mind that a completion rate of 1.00 represents a piece of content being 100% read) we’d find two pieces of content that had a much lower number of pageviews, were read in their entirety.
When calls-to-action are in the middle or end of a piece of content, you want to make sure your readers see them. That’s why completion rates are a far more accurate factor in understanding a piece of content’s effectiveness.
Takeaway: Publishers or brands might use completion rates to determine which types of best performing content or categories should be increased. They may also create campaigns that promote engagement around the 40% scroll reach border for lower-performing content.
Move from counting shares to virality
Virality is not to be mixed with popularity, which counts how many shares that content received and is normally measured over an endless period of time and. Viral sharing is commonly measured in short timeframes, from one to three days after publishing, normally represented by the K-factor.
Explained simply, the K-factor is calculated by the number of invites and invite conversions, and the equation is calculated as follows:
i = the number of invites sent by each customer (e.g. if each new customer invites five friends, I = 5)
c = percent conversion of each invite (e.g. if one in five invitees convert to new users, c = .2)
k = i *c
A K-factor of 1 means that the content is being shared at a steady rate while a K-factor greater than 1 indicates exponential growth and a K-factor less than 1 indicates exponential decline.
Takeaway: By using behavioral cohort analysis, publishers and brands can analyze and identify sharing patterns of users over time. They can then monetize the content pieces that achieve (or are expected to achieve) exponential growth within a couple of days.
Combine external data to measure campaign ROI
Of course, you’ll want to analyze specific campaigns, as well, in order to optimize your budget. By integrating data from external sources such as Facebook and Google Ads, LinkedIn campaigns and email campaigns, you’ll be able to combine behavioral data with data about your ROI to check which campaigns were the most successful.
For example, you could query data from Google’s DoubleClick for Publishers (DFP) and blend it with the customer acquisition cost (CAC) taken from a mobile attribution supplier such as AppsFlyer. Blending these data sets would give you the ROI of each campaign.
Takeaway: Publishers and brands can determine which types of content from different campaigns generated the most revenue. They can also drill down further to see which types of content converted more customers.
The true value of each piece of content
How can your company follow the advice of Blaise Lucey and understand the true value of each piece of content? First, learn how to measure engagement of your content over a period of time. Next, make sure you analyze beyond traditional metrics such as pageviews as shares to metrics such as completion rates and virality.
Finally, calculate your content ROI by blending your external data sources such as your CRM, marketing automation data and ad campaign data. What better way to calculate the true value of each piece of content?