It’s natural and pragmatic for a business, particularly one engaged in e-commerce on the web, to want to test out different presentations of its store and tune its features to optimize sales. This evokes a multitude of continuous theories and conjectures.
Meanwhile, A/B testing does away with the guesswork. It is a wonderful method of letting the data lead the way while continually testing out two versions of the same product in a real online sales environment.
Today, we are going to be talking about the ways mockups wonderfully facilitate this process.
What is A/B Testing?
Also known as split testing, A/B testing entails the testing of two or more versions of a design, product, or marketing concept, returning performance results. There is a broad range of success indicators that people can focus on to help inform further modification:
- user engagement
- add-to-cart rate
- sales rate
- click-through rate
- user clicks
- time spent interacting
The data obtained from each individual test helps drive additional changes that end up gradually increasing the indicators of interest based on real live data. This could, for instance, be the headline of a landing page in which a bold headline is compared against a softer, more informative tone.
The version with the better results is then used as the foundation for further design iterations or as the final implementation. The beauty of A/B testing is its ability to isolate specific variables. Teams can target the impact of visual elements such as:
- button color
- image placement
- calls to action
- navigation flows
This is a far more practical strategy than overhauling an entire design based on pure assumptions. That would entail investing in ineffective design and marketing strategies.
For this reason, A/B testing is a cornerstone of modern design and marketing workflows. It empowers teams to make informed decisions by optimizing final products and campaigns for success.
Why Mockups Are Ideal for A/B Testing
Mockups are early representations of products or pages consisting of different layers that can easily be edited with little time and effort required. In fact, entire batches can be modified at once.
This is a match made in heaven for A/B testing, where large amounts of modifications are often necessary. On top of that, A/B testing usually requires having to account for changes on a variety of different platforms at once.
Mockups can be easily adapted for websites, mobile apps, and social media. Furthermore, thanks to mockups, teams can take creative risks and test unconventional ideas without worrying about the consequences of implementing them on a live product.
On top of that, you can acquire a free mockup and play with large numbers of them to see which one suits your vision the best.
Mockups are great as they drastically reduce the cost and facilitate experimentation. They prove useful in many more scenarios beyond just UX and product page optimization. They’re used in marketing campaigns to test ad creatives, promotional messaging, and landing pages.
A retailer could prepare one page to emphasize a discount offer and another page highlighting product reviews to determine which one is creating a higher sales conversion.
Setting Up A/B Testing with Mockups
It’s essential that you begin your A/B testing with your objective absolutely crystal clear, namely your statistic, whatever that is for you. If you are designing a mockup for a video game menu, the objective may be to get players to explore more game modes.
On a sales landing page, the goal will be to increase your conversation rate and your average purchase size. Avoid making too many changes at once, as in that case, it will be difficult to identify which specific element influenced the results.
Here are some additional factors you’ll need to decide on:
- Selecting your target audience: it must be a representative sample of the users you aim to reach. For the sample to be statistically reliable, you’ll need a large enough sample size. Then, divide them up into two or more groups with each exposed to one version of the mockup. One could be viewing a minimalist design of a gaming app while another could be displaying elaborate graphics.
- Test across all relevant channels: if what you have is a social media ad, test it on Instagram or Facebook. If you have a mobile app that works on Android and iOS, make sure to test on both systems.
- Set a timeline and budget: make sure you run the test for a sufficient time to collect data but not so long that seasonal trends or market shifts can influence the results. Short campaigns can last just a few days or it could be several weeks long if you’re running a complex project.
- Keep an eye on your primary performance indicator as well as other relevant data.
- Collect qualitative feedback: engage with users, set up polls, and ask them what they liked or disliked about the mockup they were shown.
A/B testing analysis
At the end, say your focus was your clickthrough rate, determine which mockup performed the best. Statistics is important in this process and the standard confidence level for determining statistical significance for mockups is 95%.
At this point, you should:
- Identify what worked and why: delve deeper into the reasons behind the test results.
- Look for patterns: in the feedback or data that point to broader user preferences
- Incorporate feedback: if you’ve gotten the same comments from different users in personal conversations, test out how well their preference works with the audience at large. This may be that it’s not easy enough to navigate or isn’t clean enough.
- Test additional variations if needed: make sure you’ve covered all your bases. Sometimes a third color, style, or variation is worth testing out.
- Be sure to keep a record of your insights and share them with your team. This should be a document that you keep in a folder that will be easy to find later.
Common pitfalls
Here are some things to avoid in this process. There are plenty of confounding factors that could hamper the validity of your findings.
These include:
- Lack of realism: make sure not to rely on an oversimplified version of your product. It may be lacking the functionality, interactivity, or responsiveness that your live design will later have, which will skew user behavior. Be sure to make your mockups as realistic as possible.
- Limited context: always ascertain that you are testing consistently in each situation. Check that your test groups are not different, they are not being exposed to additional modifications at the same time, and you are not collecting data from them at different times of the day or year.
- Low sample size: randomness can provide very different results, so having a decent sample size is important.
- Overemphasizing the visual elements: oftentimes, factors like content clarity, functionality, or site performance can be the issue.
- Inability to test dynamic elements: mockups are static or semi-interactive by design, which limits their ability to test dynamic features like animations, transitions, or real-time data interactions. These can play a role in user engagement or satisfaction.
- Misinterpreting results: frequently, small differences in performance may be more a matter of chance. Be sure to always ensure statistical significance.
