How A/B Testing Can Help Publishers Produce Content That Resonates

More than ever, publishers across all verticals are under pressure to provide compelling content for users on all platforms and to ensure that their content is consistent across all device types.

But how can publishers make sure they are designing the best reader (or user) experiences while still accounting for time and money, given the rapidly evolving world of screen sizes and 21st-century audience expectations? Let's start with A/B testing.

What is A/B testing?

The technique of making minor adjustments to the textual material, interactive elements, or graphic design of information to find the optimal combination is known as A/B testing. Subgroups of the audience are then shown these modifications to gauge their effectiveness and degree of involvement. The winning format becomes the recommended format for publication once sufficient metrics are gathered to determine a clear winner.

In the days of traditional print publication, A/B testing would have been unthinkably expensive and inefficient, but in today's digital age, when the web is essential to practically every business, it has become common practice.

With the use of A/B testing, publishers can run controlled experiments that provide information to the design, marketing, and content teams, enabling them to make evidence-based decisions and create material that ultimately boosts revenue in addition to user engagement as per content updates.

What Specific Ways Can Publishers Use A/B Testing for Content, Then?

For the sake of argument, let's say that a content team is deciding between two extremely attractive instructional visuals to go with some textual text. Although there are minor visual changes between the two diagrams, they both do a good job of illuminating the educational content and separating opinions in the class. Half of the group prefers the first diagram, which uses ovals to make its point, while the other half prefers the second diagram, which uses rectangles.

Aside from the forms that hold the text, these versions are nearly identical in every aspect. Divided over which version offers the reader greater educational value, the team chooses to ask their audience which version speaks to them the most.

These kinds of choices would be too little in a print-first (or print-only) workflow for a team to take into account each time they come to a small fork in the road. This indicates that, historically, the people who made these decisions did so by combining their intuition with the opinion that was loudest in the room.

However, A/B testing is a helpful tool in today's digital age that enables teams to make meaningful and controlled user-centric decisions. By implementing a digital-first process and developing digital prototypes and products, publishers may leverage A/B testing to gather, evaluate, and act upon user feedback without having to shell out money for expensive focus groups or protracted user research projects.

Iteration is everything.

A/B testing ultimately relies on iteration, which is a fundamental step in the design thinking process. Teams can use it to set internal best practices, make well-informed decisions, and keep up with the demands and expectations of the ever-evolving modern audience.

Because people's attention spans are getting shorter by the second, this non-linear approach to problem-solving emphasizes experimentation and urges teams to reframe obstacles in a way that puts the needs of people first. By testing potential solutions before developing the final product, teams can minimize the time to market.

Teams can save time, money, and headaches by regularly A/B testing material, either in real-time or through digital prototypes. They can also gather important data that will help them make decisions later on.

Publishers should also anticipate a large return on investment from internal teams and procedures, in addition to readers. With the use of reusable templates and meta-tagging, teams may find relevant material and expand their knowledge base by utilizing the user data that comes with A/B testing. Teams may build on past achievements and avoid having to reinvent the wheel thanks to internal centers of resources with supporting data.