Deliver a great learning experience with AI-driven analytics

Digital education has become a necessity in these times more than a convenience.  With greater flexibility and personalization, EdTech apps are attracting both students and working professionals alike. An immersive and personalized learning experience is critical to the success of any EdTech app.

Education is no longer a one-sided delivery of content or information. Companies, today are trying to build experiences around the content to make it more consumable and engaging. With features such as Closed Captions, Playback Speed control, bookmarking, notes, additional resources, etc, companies are enhancing the learning experience that they deliver.

Behind the scenes, a lot of analytics goes into driving seamless and engaging user experience. A lot of data is collected to understand user behavior such as login times, courses explored, learning style, duration of sessions, features used while taking the course, etc. There is some level of gamification involved as well such as flashing the learning goals and showing the progress with rewards, badges, and certificates on completion.

Need for anomaly detection in EdTech

Since there are a lot of metrics to monitor, it becomes challenging to monitor and generate relevant insights in real-time. Therefore there are chances of overlooking business-critical incidents and missing out on new business opportunities. All of this can be easily avoided by using real-time anomaly detection to monitor EdTech KPIs.

With augmented analytics, companies can actively monitor streaming EdTech data to find hidden issues and insights in real-time to improve the overall app experience. Anomaly detection powered by AI and ML can help analyze large volumes of data at a granular level, allowing business owners to analyze and manage all performance metrics of their learning app from financial transactions to course performance in one place. 

How real-time anomaly detection helps the EdTech Industry 

Here are ways in which EdTech platforms can use augmented analytics to deliver an engaging, personalized, and intuitive learning experience. 

1. Optimize content recommendation 

Be it for higher education or corporate training, the quality of an online learning experience depends greatly on the relevance of the educational content. The more relevant the content recommendation is, the more likely a user is to explore the courses further. Artificial intelligence and advanced analytics empower companies to dynamically monitor topics, find trends in topic relevance, and build a better, more personalized content recommendation system. 

2. Monitor course/content performance 

User engagement is a crucial element that provides stickiness to the content. When it comes to online learning apps, page performance is critical to evaluate the effectiveness of course content. With real-time data analytics, you can analyze page performance metrics such as average time spent on a page, the average duration of videos watched within a course, percentage of revision quiz completed, etc to see how effective the course content is and identify new areas to improve teaching. Anomaly detection helps you to detect trends which provide opportunities for optimizing such performance metrics and get notified if in case of any deviation. 

3. Increase student enrollment and grow subscriptions 

Business owners can further monitor purchases, microtransactions, subscriptions, renewals, retention rates, and new inquiries in real-time to optimize the revenue from subscriptions. They can also evaluate the performance of marketing campaigns and different courses targeted at students. Through anomaly detection, businesses can actively monitor metrics that impact subscriptions, allowing them to take corrective action in time to drive new enrollments and grow subscriptions for a healthy MRR. 

4. Monitor daily transactions and detect fraudulent activities 

Businesses have to protect themselves from fraudulent activities and threats by actively monitoring streaming data to spot issues such as payment gateway errors in time. The granular level analysis of real-time data enables decision-makers to identify the root cause of issues and failures. By using anomaly detection on the key metrics, you can detect fraudulent transactions, misuse, and fake accounts, allowing you to improve the security and efficiency of online transactions. 

5. Manage KPIs and track SLA breaches in real-time 

EdTech companies have various teams that work together to make the user experience seamless. Companies have to monitor the day-to-day operational KPIs that impact the health of their learning app. A very crude way of maintaining operational efficiency is to track the SLAs. An anomaly detection system helps to monitor SLA breaches within the business functions or with external vendors. A significant deviation signals issues that need attention. 

6. Improve the online learning experience 

Instead of a one-size-fits-all approach, companies can use data-driven insights to understand their students better and customize their marketing campaigns and course content to drive engagement. Artificial intelligence and advanced analytics can help businesses track customer behavior metrics in real-time to deliver hyper-personalization at scale. 

With AI-driven analytics, EdTech platforms can address multiple challenges that impact online user engagement. The real-time data-driven analysis through anomaly detection empowers companies to understand learning behavior, optimize content recommendations, and improve business-relevant metrics to drive a great learning experience. 


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