Research shows that 40 to 80% of students drop out of online classes. When as many as 80% of students leave, what is the profitability of the course? Additionally, many learners give up their e-learning experiences in the first few weeks, which may be a problem for the company’s sales managers whose remuneration often relies on the student staying in the course for a given number of weeks or them making an upfront payment. Without proper retention rates, the sales team will not receive their commission.
A student’s decision to leave may be influenced by various social or motivational factors. It is, however, possible to predict the attrition rate, just like telecom companies can predict churn. The results of using predictive analytics to predict churn can be impressive: in one of our projects, we managed to reduce churn by 20%, saving the client $39k every month (and even more than that after rollout), ending up with more than 10x ROI. How could this be applied to an e-learning platform? Since the platform collects data about the students’ engagement and performance, this information can be analyzed to find patterns suggesting that a particular student is at risk of dropping out. This is an issue difficult to address in online courses, since direct contact between the educator and the students is limited (or, sometimes, non-existent). Predictive analytics can cut down on the dropout rate and identify the students who face academic or financial challenges so they can be provided with help.
Predictions can also be made about the markets that present good opportunities for investment. The e-learning industry is affected by a variety of factors related to the political and economic situation, as well as technology. The e-learning is a global business, expanding far beyond Europe where e-learning platforms compete not only with each other but also with traditional, public and private, educational organizations. Targeting markets such as Africa or the Middle East, companies have to be aware of many aspects influencing the students’ situation, which may seem quite unpredictable. Courses can lose accreditation, retention can drop unexpectedly, enrollment rates can decrease, students may struggle to continue paying tuition fees due to currency denomination. Issues like that are difficult to predict and manage, especially when you’re not prepared for such a sudden twist. With predictive analytics, however, you could see which markets are doing better than others and are worth investing in.