From the course: Creating and Deploying Microlearning
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Asset analytics tracking
From the course: Creating and Deploying Microlearning
Asset analytics tracking
- [Instructor] Tracking the effectiveness of your MicroLearning assets can give you insights into where to invest more time and resources for future assets. Now one way to do this is to normalize your data and track benchmarks of success. Normalizing your data means bringing the data for each MicroLearning asset onto a level playing field so you can really compare one asset's performance to another. For example, let's say you track the number of views, likes, and comments for each asset on a weekly basis. By simply tracking these data over time, it's hard to tell what's really going on for any one parameter across all of your different assets. But if you normalize these data by aligning each parameter with the day of launch, then you can quickly see which assets are performing best over time and discover trends that indicate the lifecycle of your assets. Now to compare the effectiveness of individual assets, establish a set of benchmarks for say, the number of weeks it takes a…
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