It’s no revelation that all EdTech companies (be it learning platforms like Coursera and Busuu, or course marketplaces like Udemy, Domestika, etc.) hardly set their growth directions relying simply on inner intuition. Every decision these companies make is (or should be) based on data-driven analysis of all educational process components.
And you’ve likely come across many clickbait materials eloquently enumerating the ways data is transforming education with improving performance efficiency, creating customizable learning programs, personalizing content for individual needs, and… we could go on forever.
But let’s not take away the bread from such articles on obvious Big Data benefits. You’re probably here for more precise and actionable ideas, like:
Oxagile’s expert will equip you with these and other crucial insights that ensure Big Data makes any EdTech platform perfectly do what it should — captivate an ever-growing number of students with 100% effective courses.
Since the performance and overall success of any eLearning product can be examined from diverse viewpoints, we’ll explore 3 distinct aspects that one needs to measure for a comprehensive picture of the way different elements within the platform impact its dynamics.
This metric implies collecting data on the students enrolled in the course, with the purpose of understanding how effective and compelling training programs are to keep them engaged. As a result, you get a chance to fix any issues at the right stage to maintain current users’ interest at the desired level and to bring back the lost ones.
“Retention Rate is found to be most effective when being calculated and interpreted both in terms of cohorts (e.g., registration dates) and periods.
To assess the effectiveness of product changes, other strategic actions, or to identify emerging trends, such metrics as DAU, MAU, WAU should better be displayed in dynamics.
For some metrics, it will be also useful to do the segmentation of users, which will allow the generation of analytics within each segment (e.g., active learners, those who migrate to a more budget subscription, etc.) and applying a specific and custom approach for each case.
So, if you determine a category of “off-track souls” — the users who have registered, bought a subscription, set auto-renewal, but do not use the products, do not log in or do not attend classes, you might want to rethink their involvement, offer them some custom proposals, send personalized reminders and notifications, include more interactive activities, or simply facilitate discussions.”
A detailed analysis of user behavior, starting from him seeing an enticing ad to becoming a paying customer (assuming that the course is fee-based), helps to evaluate the effectiveness of the whole process and understand the possible problems. They can range from technical glitches to UI/UX issues regarding non-obvious clickable elements. Additionally, there may be cases where the offer is simply not interesting for certain users, resulting in them clicking on an ad but not progressing further.
“The thing to consider here is the source of data (whether it’s a website or a mobile application), because each of them has a different funnel for tracking a user’s path. For example, the initial stage for mobile apps’ funnel involves tracking the number of people who visit the application page, the subsequent number of downloads, and then the funnel progresses similarly to the web, measuring metrics such as registrations, and so on.”
While it’s unlikely to be news to you, we still feel like emphasizing that timely gathered and processed user feedback has all the chances of becoming the easiest way of creating an ultimately pleasant experience for everyone involved.
“On top of these three types of metrics, there is an aspect that is also undoubtedly worth your attention — it’s Payment Success Rate (PSR). This metric focuses on the reliability and efficiency of payment systems, as well as the proportion of successfully completed payments in comparison with the total number of orders. Here we are specifically referring to technical aspects at the stage of processing the payments: the reliability of the eCommerce system, the percentage of orders that are completed successfully, the number and reasons of failures, the quantity of pending cases, and related issues.”
It’s important here not to confuse users who are unable to make payments due to issues outside your platform with users who experience difficulties with payments due to poor UX on your side. This way, by closely monitoring these two different, yet similar, processes you’ll be able to promptly address the issue, for example, by reaching out to the support team of the payment system.
No matter how much in-depth data you obtain, the primary objective is to ensure its clarity and accessibility for analysis. And that’s where graphics and visual presentations of all patterns and correlations come to the forefront. A good dashboard lets even those who do not have specialized Big Data skills (like marketing or product operation teams) examine all the complex data sets from different angles in real time.
So, what does it take to create a comprehensive dashboard with all education metrics? Here are the key things to consider:
“To fully leverage the available information, it’s vital for the dashboard to have a drill-down capability.
Let’s say you notice that the overall retention rate has dropped. In an ideal drill-down dashboard, you would be able to click on the specific month and see which products and services experienced a decline in retention. A competent and dedicated product team, responsible for that particular product, can then quickly identify the reasons influencing these results. For instance, if the retention rates have decreased for a specific subscription after a recent interface change, the team may decide to conduct a survey among product users to get feedback and better understand the situation.”
Oxagile will meet any of the project deliverables.
To make any dashboard with complex data tell not only colorful, but meaningful stories, our expert transformed his experience working with learning platforms and coaching services into best practices and a set of optimal strategies.
In an ideal scenario, you’d be able to quickly create an all-encompassing data analytics solution that presents high-level statistics, as well as user-level details. However, achieving such functionality requires a meticulously planned and optimized architecture. Besides, not every system can handle the vast amount of data. Therefore, to still have a chance of getting data-driven insights, it becomes crucial to conduct on-the-spot analysis.
Analysts take on the task of unloading the list of users based on certain conditions and supplement the data with additional details. For example, if you find that 50% of users left last month, the data analyst uploads a list of users who were previously active in the product but stopped using it. He also adds information about these users’ payments and activity in your other products, as well as the results of surveys conducted among them.
Once this data is obtained, these users can be tagged in the CRM system, which initiates personalized efforts to engage and retain them. The dashboard analyst can independently perform a descriptive or diagnostic analysis of the data, or simply provide a raw report.
One of the most important and, let’s face it, pleasant things about Big Data analytics is that all the research findings can be used perfectly well not only to fix immediate issues, but also to shape the future of your business. Years from now? Why not!
For example, in one of the recent educational projects (a language learning platform) I was involved in, we introduced interactive New Year’s gamification elements. Some were very trivial with bursting balls with right translations and getting random discounts, and some were almost substantial educational quests with characters and storylines that enabled getting more significant benefits if successfully completed. And thanks to the fact that during that hot New Year’s campaign we have clearly monitored our every user’s action related to our innovation, the next year the marketing specialists had a thorough dashboard on hand that showed exactly which activities were received with enthusiasm by particular groups of users, which had all the rights to be considered mishaps, and what modifications could be made to game characters to ensure the highest conversion rate possible.
And voilà — the next year showed a nearly three-fold increase in profits over the Christmas period. And yes, the best part is still that this year such monitoring will take almost no effort, because all the processes are already set up.