In Customer Education, there are always conversations about how to better harness data to make decisions about programs that educate the customers of the business. As a fledgling function at many businesses, and having been subject to one reduction-in-force after another in the last two years, Customer Education is keen on proving its value as a function vital to (especially subscription-based) business success.
In the Learning Analytics section of Artificial Intelligence for Learning (Clark, 2020), Clark makes a key point. He says, “…the goal is not to improve training but to improve the business. (p. 183). Analyzing the data is key in making that connection. However, the data can be so difficult to get. In my experience, request after request to integrate the LMS data with key business data or to have a share of a data analysts’ time frequently fell on deaf ears. So I had to tell the best story I could with the data I had available. I was working in a hypergrowth software company that had just started investing in Customer Education. The story that I wanted to tell was that “trained” customers onboarded more quickly, took fewer customer success and customer support resources, and adopted the product more quickly and thoroughly. I had access to data like when they became customers (via SalesForce), as well as the LMS data like learners’ view times and dates for content titles, percent completion of courses, and number of visits to the learning site. Without spending too much time on the data, I had an intuitive sense that there were aspects of being on the right track. However, AI-supported analytics could have confirmed that intuition. For example, was the curriculum was solving the problem it intended to solve regarding onboarding? By tying the completion of a set of courses to product adoption metrics and account license usage, we could have confirmed this. We might also learn that customers needed to learn and practice beyond that initial onboarding content, which would have required further investment. Another example is I sensed the self-paced online instruction methods were generally working based on learner time spent and number of courses completed. But there was little to compare to, since the company wasn’t investing in other instruction methods. Perhaps by analyzing community posts, conversations with customer success, and other sources, we could have confirmed that the limited forms of instruction we offered weren’t enough for all but the most motivated learners. That is a perfect task for AI-enabled learning analytics. Let’s look at the framework of Clark’s four goals for learning analytics of describe, analyze, predict, and prescribe (Clark, 2020) in the context of Customer Education. Describing who, what, where, and when are marginally useful, but this goal is much more valuable when learning data is tied to who, what, where, and when of product usage and advocate behaviors. Analyzing is where AI could save Customer Educators time, not only in making decisions about curriculum and needed course improvements, but also in connecting learning with business impact. The Predicting goal is different in this context. Grades and drop outs are less relevant if customers are achieving their goals without the measured learning. When it comes to prescribing, some Customer Education products already incorporate engines to recommend learning based on a Customer’s other learning or performance on an assessment. The real win would be recommendations based on anticipating their learning needs during their work and offering the appropriate bite-size learning to get them started, followed up by other relevant experiences to deepen their learning. References Clark, D. (2020). Artificial Intelligence for Learning: How to use AI to support employee development. Kogan Page Limited.
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AuthorMichele Wiedemer has worked in software as an "accidental instructional designer" for many years. She is currently completing the MS in Learning Technologies at The University of North Texas. This blog represents reflections on specific assignments in the coursework. Archives
February 2024
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