Successful digital marketing campaigns generate leads, nurture prospects along the marketing funnel, and compel them to buy a product or service. Although it sounds straightforward, meeting or beating conversion rate goals while adequately managing the marketing budget and delivering ROI (return on investment) can be challenging. One way to maximize conversion rates is to use tools that help take the guesswork out of what will work best, such as A/B testing. A/B testing enables marketers to test digital marketing assets against one another to determine which delivers the best results. Below, our guide to A/B testing reviews what it is, how to create A/B tests, and outlines some of the best A/B testing software tools.
What is A/B testing?
A/B testing is the process of comparing a control version (group A) of a digital marketing asset such as an ad, landing page, or email to a variant (group B) with one element that is changed. Tests are conducted for a set duration with a defined audience. Based on the results indicating variances in KPIs (key performance indicators), marketers make data-driven adjustments to improve performance and enhance the user experience. Metrics to measure include but are not limited to conversion rate, click-through rate, page impressions, web page bounce and exit rates, and simple revenue lift. A/B testing is usually conducted before a full campaign launch to determine the top performers and during a campaign to make changes that improve the result you want to achieve.
Sometimes A/B testing is referred to as split testing, but they are different. Split testing compares two completely different versions of a marketing asset to determine which version performs best.
How does A/B testing work?
Determine the digital marketing assets you want to test and compare. The first one, which may be a new or an existing version, is assigned to control group A. The second, is an alternate design, to variable group B. Define the sample audience, the test duration, and the tools and metrics you’ll use to compare groups A and B to one another.
Multivariate vs. A/B testing: What’s the difference?
A/B testing focuses on a single independent variable at a time, but what if you want to test the combined effect of several elements, such as a brand new landing page? Multivariate testing, unlike A/B testing, is designed to indicate how several different components interact with one another.
Is A/B testing worth it? What are the benefits?
If you have the resources and knowledge base to conduct A/B testing, it’s a powerful tool that helps ensure your marketing efforts’ success. A/B testing allows you to impact conversion rates near-term and better manage your marketing budget. You can also apply your learnings to future marketing campaigns.
A/B testing benefits include, but are not limited to:
- Increased website traffic: Boosting website traffic and acquiring new customers and growing your business usually go hand-in-hand. Using the most effective digital marketing assets determined via A/B testing, you can optimize click-through rates and generate more traffic to your website.
- Higher conversion rates: Ultimately, marketing efforts aim to add new customers or upsell existing ones and generate revenue. Optimizing landing pages, emails, digital ads, and other content increases the probability of higher conversion rates and better ROI.
- Higher engagement and lower bounce rate: A “bounce” is when a website visitor leaves without interacting with your sites, such as visiting an additional page to gather more information or opt-in signup. Landing page optimization based on A/B testing results can boost engagement and reduce bounce.
- Lower cart abandonment: Cart abandonment is when shoppers leave items in a website shopping cart without completing the purchase transaction. According to the Baymard Institute, the average cart abandonment rate averages around 69 percent. Use A/B testing to determine which designs and messaging result in more conversions and fewer abandoned carts.
- Increased opt-ins: Opt-in marketing is when interested website visitors sign up to receive specific information. Opt-ins help you move prospects down the marketing funnel, build a relationship with customers and potential customers over time, and enable you to collect contact information for lead generation. A/B testing will tell you what messages, positioning, and graphics work the best to improve opt-in rates.
10 A/B testing examples you should try
Below is a list of the typical elements analyzed and optimized via A/B testing. Depending on your business and your goals, you may identify additional items to test.
1. Headlines: One of the first things people notice is the headline of a marketing asset, so it needs to be catchy and persuasive. Test the headline variants to determine which version piques the most interest and leads customers to engage and make a purchase.
2. CTAs: A call to action is a brief statement on a web page, in an advertisement, or in other content that encourages the viewer to do something. Simple examples are “buy now” to drive immediate conversion or “learn more” to further engage a prospect. Even changing a single word in a CTA can impact results. A/B testing can reveal this.
3. Buttons: Use A/B testing to determine which button color, placement, size, shape, placement, and copy best captures audience attention and generates the most click-throughs.
4. Opt-in modals: Modal windows are notification windows that display different content, including opt-in subscription forms. Improve your signup rate by testing different variations in size, layout, and timing.
5. Sales copy: Optimized content, including sales copy, plays a crucial role in delivering successful conversion rates. A/B testing copy and layout variants indicate what helps or hinders sales.
6. Page layout: Web page layout and design influence viewer buying behaviors. There’s nothing like the testing layout and design variants with potential customers and fine-tuning them to enhance the user experience and impact results.
7. Form length: Prospects aren’t apt to fill out long forms that require too much time and attention. A/B testing not only helps you determine optimal length but also indicates what information customers are comfortable providing.
8. Page length: If a page is too long, viewers may never see the content toward the bottom. If it’s too short, viewers may not engage, resulting in a high bounce rate. A/B tests will reveal what works best for your audience.
9. Trust indicators: Trust indicators, such as reviews, social profiles, and association memberships, influence viewers’ decisions on whether they trust your business and are willing to become a customer. Determine which trust indicators are most important for your business and optimally present them on your website with A/B testing.
10. Media: Conduct A/B testing with different types of media, such as a video, gif, hero shot, or infographic, to understand what elements drive more conversions.
How to conduct an A/B test in 12 steps
A/B testing requires upfront planning with adequate time and resources to develop variants, implement the tests, analyze the data, and make changes to improve results.
1. Outline your hypothesis
Start by constructing a clear hypothesis aimed at altering customer behavior. For example, to improve conversion rates, look to solve website issues that prevent visitors from becoming customers.
2. Pick one variable
If you test multiple variables at a time, it’s challenging to know which variable or variables had the most significant positive impact.
3. Identify the goal
This dependent variable is the primary metric you choose to focus on and measure for each test.
4. Create control and a challenger
The whole intent of A/B testing is to compare two groups, a control group, and a variant to determine which one, if any, performs best.
5. Split your groups
Test audiences must be equal and large enough for an adequate sample to provide usable, conclusive, “apples-to-apples” results.
6. Determine your sample size
Define a test sample size that’s large enough to yield statistically relevant results and reduce the probability of a sampling error. If you’re testing a web page that doesn’t have a select audience, the A/B test duration will determine the sample size.
7. Run only one test at a time on any campaign
If, for example, you test a digital ad and a landing page simultaneously, you can’t determine which one created the most prospects or resulted in the highest conversion rate.
8. Use an A/B testing tool
Attempting to perform A/B testing without the right tools is like a chef trying to create new recipes without the proper equipment. There are several A/B testing tools and CRO (conversion rate optimization) platforms to choose from, some of which we’ve highlighted later in this guide.
9. Test both variants at the same time
Test the A and B groups simultaneously so you know the results aren’t due to timing differences.
10. Give the test enough time to populate significant data
This gets back to the need to test a sample size that’s large enough to yield statistically relevant results. If, for example, your website receives a high volume of traffic, the duration of your A/B test can be shorter than one conducted on a website with less traffic.
11. Analyze data
Methodically analyzing the collected test data enables you to measure your results and derive valid conclusions.
12. Take actions based on results
If one group (A or B) performs better than the other, then you know which version you’re going to implement. Sometimes, however, there may not be a clear winner. When this occurs, you can either stop testing and stick with control group A or develop a new A/B test with a different variant (group B).
10 popular A/B testing tools you should consider
As with all technology and services, changes in features and pricing occur. Check each vendor’s website to review up-to-date information.
1. Optimizely: One of the oldest and best-known platforms, Optimizely’s features include A/B/n, split, and multivariate testing, page editing, multi-armed bandit, and tactics library. Setup and subscription run around $1000.
2. VWO: Visual Website Optimizer (VWO) focuses on graphics and design. Features include page editing, tactics library, and A/B/n, split, and multivariate testing. Pricing is scaled. The most basic plan begins at $368/month, paid annually.
3. Google Optimize: Connects to Google Analytics, which is required to use the software. Features include A/B/n, split, multivariate testing, and page editing. Setup and subscription are free.
4. Omniconvert: Targets small and medium businesses. Features include A/B/n, split, multivariate testing, and page editing. The company’s headquarters are in Romania.
5. Adobe Target: Adobe’s tool provides high-powered, large-scale testing offered as an executive service for larger corporations. Features include A/B/n, split, and multivariate testing, page editing, and multi-armed bandit.
6. Accenture Testing Platform: This omnichannel platform provides a comprehensive suite of digital testing systems, including A/B testing. Installation and setup costs are over $25,000. A/B testing features include A/B/n, split, and multivariate testing, and page editing.
7. AB Tasty: This platform from France targets enterprise marketers. Features include multi-armed bandit, A/B/n, split, and multivariate testing, and page editing. Check the website for pricing.
8. Convertize: This UK-based company targets its tool to web agencies and mid-sized companies. Features include A/B/n and split testing, autopilot mode with hybrid statistics, page editing, and tactic library. Pricing starts at $49 for 20,000 a month.
9. Freshworks: Freshworks CRM’s features include A/B/n, split, and multivariate testing, and page editing.
10. Kameleoon: Another French A/B testing tool, Kameleoon’s executive pricing plan begins at $500/month. Features include A/B/n, split, and multivariate testing, page editing, and multi-armed bandit.
Next Steps
Despite reviewing best practices and current thoughts on how to develop winning marketing assets that deliver results, you never know what will work best with your audience until you test it. A/B testing reduces the guesswork, enabling you to make insightful, data-driven decisions.
If you don’t have the available staff or a team with the appropriate experience and knowledge to plan and conduct A/B testing, consider staff augmentation. Staff augmentation increases your team’s talent capacity, filling the gaps with high-quality, independent professionals who have the right skills. Upwork enables you to hire top talent with the confidence of using the world’s largest work marketplace.
