Track your content marketing metrics with these 5 data insights
For content marketers, planning and strategizing what kind of content to use/create is just scratching the surface; they still must deal with large data sets, and how they can utilize information from these data for improvement.
94% of businesses believe that data is essential for growth, so content marketers need to adapt their skillset, and use data that enables them to create highly-relevant, credible and engaging content.
Here are four key data types relevant to content marketers.
<p><h2><b>Industry trend projections</p></h2></b>
Content marketers can analyse historical data to predict topical trends, emergence of new distribution channels, changing trends, and more.
Some metrics to measure industry trends include traffic, keyword search volumes, and retention rates for products and services.
<p><h2><b>Trend and category engagement</p></h2></b>
Know which categories your visitors navigate when visiting and after they leave your page as this offers a lot of insights into audience engagement.
This means you can add whatever content is lacking based on available data. Additionally, specific performance metrics like conversions allow for high-level analysis of content ROI when sorted into categories.
<p><h2><b>On-site behaviour and experience</p></h2></b>
Data from on-site behaviour can show you whether your content types, formats, and channels are effective. And with machine learning, you can quickly process qualitative feedback.
For instance, sentiment analysis relies on advanced technologies like biometrics and text analysis, and this enables you to visualize the entire customer journey from initial search to purchase or bounce.
<p><h2><b>Data, content, and customer profiles</p></h2></b>
Well-defined user segments that include data points such as location, visit times, purchase frequency, interests, among others allow content marketers to create tailored, highly specific content that will perform well in performance metrics such as engagement and conversions.
Aside from providing real-time insights into the nature of users' current interests and preferences, detailed profiles also serve as a solid foundation for predicting future behavior.
94% of businesses believe that data is essential for growth, so content marketers need to adapt their skillset, and use data that enables them to create highly-relevant, credible and engaging content.
Here are four key data types relevant to content marketers.
<p><h2><b>Industry trend projections</p></h2></b>
Content marketers can analyse historical data to predict topical trends, emergence of new distribution channels, changing trends, and more.
Some metrics to measure industry trends include traffic, keyword search volumes, and retention rates for products and services.
<p><h2><b>Trend and category engagement</p></h2></b>
Know which categories your visitors navigate when visiting and after they leave your page as this offers a lot of insights into audience engagement.
This means you can add whatever content is lacking based on available data. Additionally, specific performance metrics like conversions allow for high-level analysis of content ROI when sorted into categories.
<p><h2><b>On-site behaviour and experience</p></h2></b>
Data from on-site behaviour can show you whether your content types, formats, and channels are effective. And with machine learning, you can quickly process qualitative feedback.
For instance, sentiment analysis relies on advanced technologies like biometrics and text analysis, and this enables you to visualize the entire customer journey from initial search to purchase or bounce.
<p><h2><b>Data, content, and customer profiles</p></h2></b>
Well-defined user segments that include data points such as location, visit times, purchase frequency, interests, among others allow content marketers to create tailored, highly specific content that will perform well in performance metrics such as engagement and conversions.
Aside from providing real-time insights into the nature of users' current interests and preferences, detailed profiles also serve as a solid foundation for predicting future behavior.
