“Content intelligence” refers to the use of AI and algorithms to analyze the subject matter of learning content, to assess quality, relevance, and alignment with a company’s critical skills and capabilities as well as philosophy and approach in those areas.
Today, large organizations spend millions of dollars on content libraries often without a clear understanding of which content is most relevant for their employees. Learners are therefore inundated with choices—those that are available behind the paywalls of these content providers as well as those in the free universe on the internet. Multiple sources and assets often offer different viewpoints on similar topics creating more chaos rather than a unified and consistent understanding across common topics. And companies struggle to measure the ROI on the content spend and lack insights to determine what content is most aligned with the skills and capabilities needed for business success.
Amid the proliferation of digital learning content, there is a growing need to move from more to less and from quantity to quality and relevance. Organizations can optimize their learning content strategy by investing in content that is most relevant and of high quality, and trimming back on the rest, but truly assessing relevance of content is a very tough problem to solve. Most organizations speculate content relevance and quality by the title of a course—judging the book by its cover, or of course consumption metrics. However, assessing alignment of content with the company’s skills and capabilities as well as vision and philosophy in each topical area seems like an impossible feat. Doing this manually would require an army of people and thousands of hours of effort to comb through and categorize. Content intelligence solutions use AI and algorithms to analyze content libraries, assessing them for relevance, benchmarking against other libraries as well as free resources, and then ranking them by degree of relevance, thereby providing concrete data to support informed decision-making. Essentially, these platforms use three measures for defining “good” content:
Relevance: Alignment to a company’s set of skills. Relevance is calculated for each asset, relative to the agreed, high-value skills in a company. Relevance is an especially important metric because it can be calculated by analyzing the content before putting it in front of learners, whereas usage and usefulness metrics are determined after learners have interacted with the content.
Engagement: Completions-per-access. For engagement, the platform looks at the proportion of users that access the content and see the learning experience through to the end, as a potential indication of actual usage, and the ability of the content to engage a user, rather than just the number of employees accessing or clicking into the content.
Applicability: % of people who find the content useful. For applicability, the platform explicitly solicits user feedback on each learning experience and calibrates it with the insights on relevance and usage.
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