How do you know if your content has any impact at all?
In a world that seems constantly flooded with content, people can be facing a decision fatigue not that dissimilar to opening one’s Steam library just to stare at the endless choices for half an hour before gravitating to YouTube videos instead of actually playing anything. Content performance evaluation tools can also only tell us so much about user activity and engagement that goes beyond session length and when people stop watching a video or reading a blog post. “Content fatigue” is quite real with BuzzSumo finding that the law of diminishing returns doesn’t just apply to business development, it also applies to content: the more content on a specific topic that gets produced, the less engagement it gets over time. This has lead to people wondering if content marketing is dead (spoiler alert: it’s not, the rules and norms have just changed) because metrics only paint part of the picture.
Metrics Getting More Sophisticated and Complex Over Time
Measurement, better known as content performance, is the fifth pillar of the content marketing framework and remains a focus area for marketers and content strategists. Closing the gap between content performance and management requires an understanding of not only basic engagement drivers but also which metrics are the most important to evaluate. Metrics started out pretty simple such as tracking pageviews, bounce rates, social shares, then tracking more sophisticated analytics like demographics and location to see which groups were more likely to convert. A variety of tracking implements can be used like UTMs which track which sources are the most fruitful. With the content overwhelm out there, this is particularly important to determine which sources to prioritize.
However, metrics are becoming much more complex. For instance, marketers first evaluated session length which evolved to scroll depth (seeing how far each user got on each page) and now it’s session length over scroll depth e conversion over clicks.
Automation is totally inevitable to make sense of these complex metrics that keep being developed. But what makes AI different from machine learning is that it comes closer to resembling human intelligence and capabilities while machine learning perfects predictions and algorithmic processes over time. Machine learning has made a formidable complement to content management because it’s proven to be an excellent curator, but a human touch to some degree is still necessary to decide which metrics get evaluated.
Simple metrics like bounce rates can be displayed on a screen, but AI can be programmed to determine how to evaluate more complex metrics and demonstrate why these results are significant. Tracking conversion by source is nice but imagine if you had an AI that told you if your leads from Facebook read much farther into your blog posts than the leads coming from search traffic. Machine learning could help you predict future engagement patterns but AI will arrange the data in a meaningful way so that humans can properly evaluate your content performance.
Metrics will continue to become more sophisticated and firms with significant investment in content marketing need to start deploying AI and machine learning in the proper capacities. Algorithmic reasoning only tells part of the story, a human touch is still needed to get the whole picture and AI is where the two meet in the middle as far as the pillars of content marketing are concerned!
Rachel P is an indie game developer, writer, and consultant. She is also a content strategist here at Writer Access and would be happy to help you with keyword maps, customer journey maps, and buyer personas in addition to writing for you. If you would to like to hire Rachel to devise a content strategy for you, please contact your account manager or send a direct message.