With A/B testing, you can compare multiple versions of a campaign to see which one performs better with your visitors. When anyone visits a relevant page, a predefined ratio of the traffic will see each variation, and you'll see how each version performs by comparing engagement analytics such as the views, signups, and conversion rate.
Plan your test
It might seem obvious, but planning your test ahead of time is crucial. Changes that often result in the most significant changes in visitor behavior include the:
- Headline: The headline is usually the first thing that your visitors will notice when viewing a campaign, so it's essential to create one that attracts attention. Consider testing different lengths, tones, or design elements like the headline's font, size, or color.
- Offer: Once the headline has the visitor's attention, the offer needs to get them to convert. Consider making different offers to determine what your visitors find most appealing. Maybe it's a set discount instead of a percentage discount or a free add-on instead of free shipping.
- Form: A campaign's form can easily convert new customers or discourage conversions if it is too lengthy or arranged poorly. Aim for a form that's short (3 fields or less) and easy to use.
- Call-to-action: A compelling CTA is one that entices visitors to click or submit your campaign. Consider experimenting with its color, alignment, and text.
- Images: Images can make a campaign more visually appealing and can help your visitors understand the campaign's offer. However, that doesn't mean an image is always the answer. Consider testing campaigns with and without images. For campaigns with images, consider comparing image sources (e.g., stock vs. custom), types (e.g., still vs. gif), and placements.
- Campaign Type: The Convert tool allows you to present similar information in various ways. Consider offering the campaign in different formats (e.g., Fly-out vs. Banner).
- Campaign Settings: The Convert tool allows you to target visitors based on various rulesets. Consider experimenting with the campaigns When To Show settings.
Whatever you decide to test, each variation should reflect a single change as it makes it much easier to know which change led to more engagement. Additionally, it's recommended you limit the number of variations in play as it also makes understanding your results easier. Consider starting with the original versus a single variation before branching into more complex tests.
Create an A/B test
To create an A/B test for a campaign:
- Navigate to A/B Tests and click the New A/B test button.
- In the prompt, provide an internal name for the experiment, select the campaign you'd like to test from the dropdown menu, and then click the Create experiment button.
- Click into Test Settings and decide how you want to configure the experiment. You can choose to end the testing after a set amount of time or after a set number of participants. Save.
- Click into the Original variation, provide a name, set a traffic ratio, and Save. Please note, leaving the traffic ratio to 1 will divide the traffic evenly across all variants. Once you update the traffic ratio of one variant, the rest of the traffic percentages will update automatically.
- Click the Add Variation option, provide an internal name for the variant, and then click the Create variation button. The variant will start as an exact copy of the original campaign so that you are in control of what changes, from the settings to the design.
- Make the desired changes and then select the experiment's name listed under the variations name to return to the A/B test builder.
- Add additional variation if desired by repeating the previous steps.
Start the A/B test
Once you've configured the test's settings and created the desired variations, select the Start test button at the top-right of the A/B test builder to launch the experiment.
Review the results
To review the result of an ongoing or finalized A/B test, navigate to A/B Tests, and select the desired experiment. A summary of the experiment will appear outlining various performance metrics, including the views, signups, and conversion rates broken down by participation and by views. If an experiment is complete, you will also see the variation with the highest conversion rate clearly identified as the winner.
That's it! Now you're ready to incorporate your findings into even more effective future campaigns.