The Pandemic Opened Up Opportunities For eLearning
As the pandemic gripped the world, "going remote" presented itself as the best available solution, and focus on eLearning escalated massively. Downloading of education apps more than doubled post the coronavirus outbreak in nine major geographies across the globe, including China, the United States, and Italy [1]. The acceptance of technology in the learning space accelerated for both students and professionals. As per a LinkedIn report [2], companies are likely to increase their Learning and Development budgets with more and more executive buy-in coming for online learning.
The boom in the eLearning industry presents tremendous opportunities for companies in this domain. To continue on this growth path, optimizing the existing traffic on their digital properties to drive up the experience, conversion, and revenue becomes a key priority. This is where A/B testing comes in.
It helps you identify and fix the problems that are affecting your core conversion metrics, which in the case of eLearning companies, could be drop-offs on the payment page, reduced sign-ups, etc.
Why Should eLearning Companies Use A/B Testing?
A/B testing helps eLearning brands make their users’ journey seamless. As an eLearning marketer, you can get a better ROI out of your existing traffic when your users’ journey is frictionless.
With A/B testing, you can address issues with your bounce rate by testing multiple variations of an element of your website till you find the best possible version. You can also make minor, incremental changes to your website instead of redesigning an entire page and jeopardizing your existing conversion rates.
How Does A/B Testing Work?
Briefly speaking, the process of A/B testing consists of the below 4 steps:
- Research
Understand where learners/students are getting stuck. Gather data on how users are interacting with your website from your customer service team, user tracking, watching user sessions, customer feedback and surveys, click maps, scroll maps, heat maps, and so on. - Formulate hypotheses
Formulate hypotheses from the data gathered on how to improve User Experience. Figure out what keeps learners hooked, be it free lessons, testimonials, or something else. - Create variations
Create variations of elements or pages by making the changes stated in the hypotheses. You can test everything from course formats to lesson durations to find out what works best. - Run tests
With the help of A/B testing, split your website traffic between your variations and see what entices your users and what is distracting or irrelevant to them. - Analyze results
The tests that you run will give you unambiguous data to drive your decision on how to move forward.
How A Small UI Change Impacted The Click-Through Rate By 10% For A Leading eLearning Brand
One of the leaders in the eLearning industry ran an A/B test for the color of their Call-To-Action (CTA). The hypothesis was that since blue is the universally accepted color for links, all their CTAs should be blue to eliminate confusion.
They ran tests to compare three CTA options, and watched session recordings. Different sets of users were shown different popups, and they observed that many users tried to click on words that were blue but not actual links.
This led the team to a new hypothesis: blue should be the only color for links. The team then created a new test on a select number of articles on their website and tried three colors for in-article links: red, orange, and blue.
The hypothesis was proven true. The colors other than blue reduced click-through rates by an average of 10%. Going forward, they continued using blue for all links and made sure no non-link text was blue.
Conclusion
Running tests methodically using interaction data, instead of gut instinct, is crucial to the success of any eLearning brand’s optimization journey. These brands have the power to shape a positive outcome of the ongoing calamity if they are able to market themselves optimally and offer the best User Experience to their digital traffic. By using the right A/B testing tools, they can get insights into who their users are, what they like and dislike about the brand’s products, and how learners’ journeys can be optimized end-to-end.
Disclaimer: This article and the opinions thereof belong to VWO. Companies run their marketing and product experiments on VWO to conduct successful A/B tests across the customer lifecycle.
References:
[1] Data Digest <https://go.sensortower.com/Q1-2020-data-digest.html>
