Small Margins: Why A vs. B Testing Matters in Data Science Courses

Tablet computer running A/B test

In a competitive online market, digital marketers are constantly looking for an edge. A vs. B data testing can provide this advantage, helping organisations assess the success of their newest online features and offers.

When testing a new marketing campaign, conducting A vs. B tests will help you determine if certain elements of it deliver more web traffic than alternate options. This method, also known as split testing, provides clear and actionable results - a highly desirable outcome for digital marketers with data science expertise.

Are you curious about the marketing benefits of A vs. B testing, and why it is taught on data science courses? Read on to find out more.

A vs. B Testing Helps Students on Data Science Courses Understand Key Learning Outcomes

For professionals enrolled on data science online courses, the many learning opportunities offered through the study of A vs. B testing cannot be overlooked. A vs. B testing is a chance for data science schools to impart crucial lessons using a relatively simple concept. Above all, this method is a lesson in causality, as data scientists compare two variables to isolate which is responsible for the desired market effect.

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A vs. B data training helps professionals understand data science concepts

A vs. B testing encourages students to identify causal relationships, and ultimately argue the merits of one proposed initiative over another. A vs. B causality also helps students link data science knowledge with key business concepts, including effect size and return on investment (ROI).

Leading Businesses Use A vs. B Testing

Students on data science courses are often keen to know how the skills they learn relate to leading digital marketing best practices, and A vs. B testing is one technique which is regularly employed on a large scale by major global organisations. The simplicity of the method means it has virtually innumerable marketing applications, each of which can be adapted to a company's size and objectives.

For instance, since Google revenues are driven by clicks, the search engine runs hundreds of A vs. B tests every day. The company even reportedly once conducted an enhanced test to determine which among 41 shades of blue would be most appealing in their logo. The drive for clicks also leads Facebook advertisers to conduct A vs. B testing to determine the appeal of a given ad layout over another.

A vs. B Tests Allow for Steady Web Growth

Widely disseminated among digital marketers, A vs. B testing is crucial for continued growth on the web. This method avoids the risks of a complete overhaul, optimizing digital marketing products through incremental improvements.

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For smaller web companies, A vs. B testing means outsmarting the competition

For digital marketing professionals, these careful refinements are crucial. For example, companies often test a new ad by making it available to a certain percentage of its users, then comparing activity across all users. Carefully releasing new ads and site features, they can then track their progress and prevent sudden drops in web traffic.

Are you looking for opportunities in digital marketing?

Contact Southampton Data Science Academy to learn more about our online data science course for marketers.

Topics: Study Data Science Online, study data science, online data science academy, online courses for the data scientist

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