In the digital world, the role of Quality Assurance (QA) professionals goes beyond mere creating test cases and discovering bugs. Over a decade of evolution in quality assurance field, it has been observed that knowledge of web experimentation can greatly improve software testing.
This article delves into the transformative role of QA in web experimentation and highlighting how to go beyond traditional bug discover journey to a strategic quality assurance that can help in business growth. Through systematic testing approach, QA professionals can contribute to informed decision making and improving user engagement and conversion rate.
What is web experimentation?
Web experimentation has become significantly more widespread as it provides an alternative to lab-based experiments for any websites and web applications. Web experimentation is a systematic approach to testing various hypotheses, collecting data that helps businesses stakeholders to make data-drive decisions. It is a process of conducting controlled tests to evaluate changes to web pages against current design of a web page.
By showing different variation to different users and visitors, a business can collect data, analyze, and implement changes that may result in improved conversion rates, user engagement, improved user experience and other relevant metrics. To exemplify, an e-commerce business running tests on product page design or on checkout page designs results in significant improvement to complete the purchase transaction.
Where can quality assurance have an impact?
Quality Assurance plays crucial role in web experiments. A/B testing, also known as split testing, is most popular to compare two version of a web page or app features to determine which one performs betters. In A/B testing, A represents the control group and B represents the variant with changes element.
A/B testing is best to use when wanting to validate 2 to 5 variables on a single web page. One step further, if there are combinations of variables that need to be tested on a web page simultaneously, Multivariate testing (MVT) can be considered. Multivariate tests can provide comprehensive insights of user’s behavior and conversions and optimize web pages by finding the best element combinations. Refer below figures to understand A/B testing and Multivariate testing.
A/B Testing (Source: https://support.optimizely.com/)
Multivariate Testing (Source: https://support.optimizely.com/)
Planning experiments with precision
Understanding web experimentation enables QA with foresight to the test coverage for variables, target audiences, events, and pages to be tested and ensure that test is not a random trial, but instead a well-thought-out experiment. It also enhances the ability to design tests that may yield actionable insights, paving the way for informed decision-making.
Setting up experiments for success
Setting up an experiment on platform like Optimizely requires a keen eye for details. It is not only about setting up the campaign and experiments, but also creating an environment that mimics the live site with variables under tests. This setup is important for obtaining clean and unbiased data.
Quality Assurance before going live
Before the experiment goes live, it undergoes thorough and rigorous quality assurance checks. This is where expertise of any QA professionals becomes invaluable. They do not only check for the functionality, but also ensure that experiment’s integrity is intact, and it will not adversely affect the live site’s performance, but accurately captures the visitor’s interactions. For instance, consider a poor designed checkout process where ‘Submit Order’ button is hard to find. This bad UX can frustrate users and a user may abandon carts which may directly impact on sales and customer satisfaction. By rigorous testing of experiment setup, QA ensures that data collection covers precise reflection of user and visitor’s interaction with a site. QA simulates real used interactions in an experiment to identify and rectify issues experienced by user, ensure the design experiment that capture usability issues, and data collection when user visits the site. This through validation helps to prevent negative user experiences and ensure the experiment success.
Data collection for precision analysis
Once the experiment is live, the focus shifts to data collection. To structure data collection effectively, QA professionals set up comprehensive framework that includes defining key metrics, setting up data points, and adding proper tagging. They monitor the data collection process to maintain data integrity and watch for any anomalies or discrepancies that could affect data quality. The best practice includes validating data points, performing audits after certain intervals, and using any automated tools to identify inconsistencies etc. QA also participates in initial stages of data analysis to identify any quality-related insights such as patterns of defects or area of improvement in the software that could impact user experience. Data collection is not only about collecting numbers; it is about the story behind those numbers. Those stories speak about visitor behavior helping stakeholders drive conversions, enhance site speed, performance, improve UI/UX and boost customer engagement.
A catalyst for transformation
In summary, web experimentation knowledge is not just an add-on, but it acts as a catalyst that will transform the mindset. It will empower QA professionals to not only contribute to experiment success flawlessly, but also resonates with users.
Are you struggling with structuring your web experimentation projects? Working with Optimizely or a comparable digital experience solution but don’t know where to turn for expertise? Thinkmax was founded in 2009 to help mature and complex businesses get the most out of their digital systems, contact us to learn more about how you can get the most out of your web and product experience.
Article written by Hetaxi Morker, Quality Assurance Manager at Thinkmax.
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