Optimizely Web Experimentation – A/B Testing                                            

Digital product improvement is one of the most essential parameters for achieving long-term growth and profitability. Though improving a digital product involves a lot of subjectivity, it results in increased revenue, improved user experience, and reduced costs. Businesses can improve user experience through A/B testing a framework that helps businesses make decisions based on user feedback.

Web Experimentation is Optimizely's experimentation software platform that supports businesses in enhancing the user experience of digital products, commerce, and campaigns. By substituting digital presumption with proof-based results, Optimizely allows product and marketing professionals to fast-track innovation, lower the risk of new features, and drive the return on investment by up to 10X.

Optimizely Web Experimentation provides three different experiment types:

• A/B testing
• Multivariate testing
• Multi-page testing

This blog explores the reasons a firm should use A/B testing for decision-making. But first, let us understand what is A/B testing.

What is A/B testing?

A/B testing, also referred to as bucket testing or split testing, is a technique for optimizing websites that compares the conversion rates of two or more iterations of the same thing (page, block, or feature) while using actual traffic. Multiple variants of the page are randomly presented to users, and statistical analysis is applied to identify which version performs better for a particular conversion objective. To assess which alternative is more powerful, A/B testing software keeps track of things like how users interact with a page, sections viewed, links clicked, newsletter signups, etc.

A/B testing enables data-informed decisions, making business conversations more result-oriented than guesswork. Businesses can measure the impact of change on matrices and ensure every change produces positive results. Let us see what steps are involved in Optimizely A/B testing.

How to do A/B test?

Below are generic steps to perform Optimizely A/B testing:

1. Collect Data: Collect the optimization data by analyzing data from an analytics tool. This helps to begin with high-traffic areas of the application to gather data faster. For conversion rate optimization, identify the pages with high bounce/drop-off rates that can be improved.
2. Identify Goals: The conversion goals are the metrics to determine whether the new variation is successful.
3. Generate test hypothesis: Once the goal is identified, list the ideas and prioritize them in terms of expected impact and difficulty of implementation.
4. Create different variations/versions: Using the Optimizely A/B testing tool, make the desired changes to the webpage or app screen and modify it to create a new version of the same page.
5. Run experiment: Randomly split the traffic so that half of the website users will see the original version of the page (control variant), and the remaining will see the new version (the variation). Kick off the experiment and wait for participation.
6. Test results: The results of tests are dependent on the number of the target audience, so it will take some time to achieve a satisfactory result
7. Analyze results: The Optimizely A/B testing tool displays the data from the experiment and will show the difference between the two versions.
8. Stop experiment: End the experiment and keep the variant that performed better.

Benefits of A/B testing Using Optimizely Web Experimentation

• Optimizely Web Experimentation allows businesses to make careful changes to the user experiences by collecting data on the impact A/B testing makes.
• The platform enables businesses to effortlessly A/B test digital assets at scale based on real-world data.
• It maintains high standards of continuous refinement that boost revenue and reduce customer acquisition costs without increasing the advertising spend and developer workload.
• The simple interface of the Optimizely A/B testing tool enables editors/users to make design changes to the website and test the variations with live traffic and get faster statistical results.
• It provides clear results and detailed reporting.
• It allows running more than one experiment at a time.
• The platform also measures the number of conversions obtained from the original (control) versus the variation (challenger). The one that generates the most conversions during the testing period is typically promoted to the design for that page.

Challenges of using A/B testing

• Inaccurate conclusions: In case a business fails to identify the number of users (i.e., sample size) participating in the tests and if it is too small, the test results won't be reliable and will lead to inaccurate conclusions. Results obtained with low traffic may lead to biased decisions. To avoid this, data collection should be done accurately using analytical tools, and the traffic should be used appropriately.
• Time and resources overuse: To get reliable and accurate results, A/B testing requires a sufficient test duration depending on the traffic flow on the page, which may overuse the resources. This needs to be addressed by analyzing the traffic on the website and performing the test during heavy-traffic periods.
• Technical difficulties with complex scenarios: A/B testing can help with simple, everyday problems, like modifying the visual behavior of a website or improving the digital experience. However, A/B testing may be time-consuming and difficult to implement for complex issues like infrastructure changes. Before stepping in for A/B testing, businesses should discuss and analyze the scenarios and should always do a feasibility check.
• Improper result analysis: Misinterpreting or exaggerating A/B test results may lead to inappropriate conclusions and failure of A/B tests. It is crucial to understand and set the test scope before performing the A/B tests to avoid incorrect and broad conclusions.

Conclusion:

Overall, Optimizely A/B testing is a powerful tool for website optimization and improving digital experiences. It provides a user-friendly, simple visual editor, which is helpful for non-technical editors/users. The statistical data returned by the tool helps in achieving efficient conclusions. Before performing the A/B tests, businesses need to focus on careful planning and analysis of data in existing analytics tools. Also, identifying the sample size and duration to perform the test plays a vital role.

Being a solution partner with Optimizely, Nous offers extensive knowledge of different customer-centric digital experience platforms and deep expertise in the Optimizely CMS. Learn how we can help businesses in test execution, continuous monitoring, and analysis of results to ensure they are reliable.