What is A/B testing for media?
Marketing strategy is all about understanding the wants and needs of your customers. A/B testing for media allows you to get into the mind of the buyer by gauging their responses to incremental changes you make to your website.
A/B testing involves more than asking customers if they prefer option 1 or option 2; these tests yield immediate, actionable, and accurate insights into consumer behavior.
Discover how A/B testing works, common A/B testing tools, and how this technique can help you increase visitor traffic and engagement with your website, products, or services.
What is A/B Testing?
A/B testing is a specialized type of incrementality measurement. In A/B testing, randomized groups are shown a variant of a single variable (web design, landing page, marketing creative, etc.) to determine which variant is more effective.
Incrementality measurements use A/B testing in certain media or general marketing channels, such as prospecting, where tracking a media exposure for both the test and control groups is required.
In the case of media incrementality, the A group (test group) is shown business as usual media exposure, while the B group (control group) has exposure withheld or is shown a null media exposure, typically a public service announcement (PSA) for a charity of the marketers choice.
The more generic form of A/B testing is called Design of Experiments (DoE).
What is the Goal of an A/B Test?
The goal of an A/B test is to determine the effectiveness of different marketing strategies so a company can optimize its conversion rate. If your users are changing their behavior or your audience is shifting, A/B testing can help you figure out why and how to capitalize on these new customers while retaining existing ones.
A/B tests that a company runs on its website are called on-site tests. A company can use on-site A/B testing on a number of its marketing-related web features, including:
- Calls to Action (CTAs)
- Webpage content
- Featured items
- Number of fields in a form
A/B tests a company runs off its website, like tests on ads or sales emails, are called off-site tests.
The A/B testing framework helps companies address core conversion metrics like low viewer engagement, high shopping cart abandonment rates, and unqualified leads. Often, A/B testing will reveal a simple fix that immediately improves your viewers' experience. Something seemingly insignificant, such as changing the size of your subscribe button, may increase your conversion rate.
Take a look at some specific goals of A/B testing and A/B testing examples to understand how this strategy can improve your company’s marketing efforts.
Problem-Solve Viewer Pain Points
Viewers may visit your site for different reasons but often share similar pain points. In marketing, pain points are the specific problems your prospective or current consumers face when engaging with your website, products, or brand. These pain points can lead to a high bounce rate, low conversion rate, and a dissatisfied customer base, which can decrease your ROI.
Before a business can address these pain points, it must identify them. A company can use A/B testing to determine the specific variables that lead to conversions. The company can then adapt its marketing strategy to incorporate more of what its target customers like.
Example: A company wants more viewers to click its “Book a Demo” button, but it’s not sure what changes to make to increase engagement. The company uses A/B testing to test the effectiveness of changing one variable, the button’s size, by creating two trial groups.
Group A sees the original button, and Group B sees the same button but larger. A/B testing reveals that viewers in Group B clicked the button more often than Group A. Based on this data, the company decides to increase the size of the “Book a Demo” button on their site.
Keep in mind: If increasing the button’s size does not change customer behavior, the company can test another variable, like the button’s color. Using the A/B testing framework, the company can ultimately determine the ideal “Book a Demo” button, meeting its goal of addressing a pain point.
A/B testing allows a company to take advantage of its existing high-quality web traffic, yielding valuable insights for lower costs. It can be costly and challenging to acquire new website viewers, but with A/B tests, you can gain valuable marketing information from your current visitors.
By running A/B tests on current viewers, marketers can identify the changes that increase conversions without spending excess money on ineffective marketing campaigns.
Example: An eCommerce boutique is struggling to sell dresses, so it decides to use A/B testing to identify why customers are looking but not buying. The boutique changes its product descriptions (the variable) to engage current customers. The boutique runs an A/B test consisting of a control group that sees the existing descriptions and a test group that views the new text. The results ultimately help them determine that the new text leads to more dress sales.
Modify With Low Risk
With A/B testing, a company can make website modifications bit by bit, reducing its risk of lost conversions. Making significant changes to a website can be a costly marketing move since customers may not be able to purchase your products or services while you make the changes. A/B testing, on the other hand, is low risk, high reward. Because each test only looks at one variable at a time, a company can make incremental changes to its site for the better without worrying about losing sales.
A business can also use A/B testing before launching a new feature to understand whether the change will appeal to its customers.
Example: An online store wants to change its logo but worries rebranding may confuse customers. The store uses A/B testing to test the logo on specific customers to see if it impacts conversions or traffic. Data from the A/B test reveals that the new logo increases the conversion rate, so the online store decides to go ahead with the rebrand.
When Should You Run an A/B Test and for How Long?
A company should run A/B tests when it wishes to test and make front-end changes to its website. Since A/B testing focuses on small, incremental changes, it’s important to use A/B tests as part of a broader, more holistic optimization strategy, not as an isolated optimization exercise.
For A/B testing to yield valuable and actionable insights, you need a representative sample of your customers. To ensure your data is accurate, experts recommend running a single A/B test for at least a week or two. You’ll want to ensure you are capturing data on the weekends and weekdays since customers often exhibit different shopping behavior on non-working days.
For the best results, create A/B tests that are segmented, validated, and possible to repeat.
How Many Variables Should You Test in A/B Testing?
You should only test one variable at a time with A/B testing, such as a feature’s size, color, or font. If you test multiple variables at once, you won’t be able to pinpoint what led your customers to prefer one variant over another.
If you want to test multiple variables using the A/B testing marketing strategy, you can perform numerous A/B tests. With this approach, you can switch out the variant that performs worse in each trial for another variant or perform a multivariate test.
What is the Difference Between A/B Testing and Multivariate Testing?
Both A/B and multivariate testing involve showing viewers options to determine preferences, but multivariate testing uses multiple variables, while A/B testing only uses two.
Multivariate testing is often used when a company wants to completely overhaul its marketing or branding strategy. If the company knows what direction to go with its website rebranding, it may save time and money on marketing by showing viewers all the new changes at once and gathering data on their responses.
Multivariate testing can also help a business:
- Avoid running several sequential A/B tests
- Analyze and determine how each page element contributes to measured gains
- Map interactions between independent element variations (banner image, headline)
This experimentation method is more complex than traditional A/B testing and is typically used by advanced marketing and development professionals.
Easily Run A/B Tests with Measured
We have worked hard to plug-in directly to 100+ media platforms and their APIs. Because of this, Measured provides incrementality measurement and testing with ease and speed. We can then run 100s of audience-level experiments with a quick one-time setup with your publishers.
Find out media’s true contribution across all your addressable and non-addressable channels. Book a Demo today.