Sunday, February 21, 2016
Why Your Content May Be More (or Less) Engaging Than You Think
Several firms that provide content marketing technologies or services have developed and published frameworks for measuring content marketing performance. Some examples are the frameworks developed by Jay Baer at Convince & Convert, Curata, and Contently.
Overall, these frameworks provide a sound approach to measuring content marketing performance. However, like most measurement systems, they include metrics that have some limitations. So, it's important for marketers to understand what each metric does (and does not) measure and therefore how each metric should (and should not) be used.
B2B content marketing effectiveness is difficult to measure for several reasons. What B2B marketers ultimately want to know is whether (and how much) their content marketing efforts are contributing to revenue generation. But in the B2B world, months or even years can separate a potential buyer's exposure to a content asset and a final buying decision. Few if any B2B marketers can afford to wait that long to find out if their content is effective.
B2B marketers also really need to know whether their content is earning and sustaining meaningful attention from potential buyers because sustained engagement is essential for winning sales. Unfortunately, however, engagement quality can be extremely difficult to measure directly.
To address these and other challenges, marketers often use what are called proxy measures. In the field of measurement science, proxy measurement refers to the act of substituting one measurement for another. The most common use of proxy measurement occurs when a measurement that is relatively inexpensive and easy to perform is substituted for a measurement that is costly, difficult, or even impossible to perform.
It's perfectly acceptable to use proxy measures, so long as the proxy has a strong correlation with the true focus of interest. However, it can be tempting to use an "easy" metric as a proxy for a "difficult" metric even when the correlation is weak or nonexistent.
To illustrate how the inappropriate use of proxy metrics can impact the measurement of content marketing effectiveness, let's consider content sharing metrics. These metrics are designed to capture how many times a piece of content is shared on social networks such as LinkedIn, Twitter, and Facebook. Content sharing contributes to content marketing success by exposing content to individuals who would not otherwise see it, and therefore it amplifies the potential reach of the content.
But some marketers also view content sharing as an indicator of - a proxy for - content engagement. The assumption is that content that is widely shared is also engaging, but that's not necessarily the case. Research by Chartbeat has shown that the correlation between social shares and content engagement is virtually nonexistent. In this research, Chartbeat examined 10,000 socially-shared articles and found that there was no relationship between the number of times an article was shared and the amount of time an average reader gave that article.
The social sharing metrics that most companies use to measure content effectiveness can also lead to inaccurate conclusions because they only capture sharing on "public" social networks. In 2014, research by RadiumOne found that 69% of all content sharing globally takes place via private digital communications tools such as e-mail and instant messaging - what is typically called "dark social" sharing.
The RadiumOne research focused on consumers, but it's likely that private content sharing is even more prevalent among business buyers. When a businessperson privately shares business-related content with his or her work colleagues, the engagement with that content is likely to be very high. Therefore, the typical content sharing metrics are even less effective for measuring content engagement in a B2B setting.
The bottom line is that measuring the true effectiveness of content marketing is difficult, and the use of proxy metrics will sometimes be necessary. But when using proxy metrics, its critical to understand what the limitations of those metrics are.
Image courtesy of Kumwenl via Flickr CC.
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