How measuring audience engagement is evolving

Identifying how to best measure engagement and message quality has challenged marketers since marketing began. Before the Internet age, marketers and communications teams relied on relationships, face-to-face interaction (i.e. focus groups) and even gut feelings to measure engagement. Not only were these methods untimely, providing results well after-the-fact; there was also an increased chance for bias, and marketers weren’t capturing the whole customer story.

With the innovation of Net Promoter Score, marketers were suddenly able to measure engagement by intention—a powerful measure of loyalty. But then, the arrival of the digital era brought with it a wealth of information—an embarrassment of riches. It took marketers years to adequately collect, process, and identify actionable insights from all of this data, but now, many of us well understand the world of time spent on site, click depth, and visit frequency, to name a few. Digital engagement has evolved into a behavioral wonderland for marketers with each and every online action easily captured and analyzed to measure impact, effectiveness, and quality of the interaction.

The value and limitation of tracking digital footprints

With engaged audiences leaving robust digital footprints, marketers now have a gold mine of data available to inform their content and messaging strategy and differentiate them from the competition. While marketers can get near-instant feedback on whether their content is resonating or how their landing page is performing, relying on digital trails still has one big limitation: it is almost entirely focused on looking backwards—analysis is conducted after a message has been disseminated. Though the message can often be adjusted in light of findings, this is after valuable time and money has already been spent.

Using analytics to predict engagement before publishing

Now, we are at the forefront of a new era, one in which we can analyze content before it is published and predict with high confidence its expected performance in the marketplace. Using Quantified Communication’s proprietary communication analytics platform, marketers can utilize predictive analytics to make their content more engaging before they press publish.

For example, we sought to identify which language components best predict which New York Times content will go viral. When comparing the most-viewed content to the most-shared, using predictive analytics we found that the most-shared content contained 24% more thought leadership (the desire to create a discussion), 11% more persuasive language and 28% more statistics (both insights that demonstrate a sense of credibility) than the most-viewed content. So even in the realm of content sharing, which seems based on impulse, a savvy writer, editor or marketer who is aiming for a wide readership can get a leg up on the competition by using predictive data.

Marketers have mastered the art and science of measuring engagement in real-time, but with only 15 seconds to get a reader’s attention, an even greater opportunity exists to use predictive analytics to further increase the effectiveness and impact of all of the engaging content being shared today.

To learn more about how we can help your team use data to inform and improve your content strategy, contact us at info@quantifiedcommunications.com.