Visuals are the backbone of successful marketing campaigns. They capture attention, evoke emotions and directly drive conversions — studies show that visuals increase content retention by 65% and engagement by up to 80%.
However, with the explosion of marketing channels — social media platforms, mobile devices, new CMS interfaces and various display sizes — marketers face a daunting challenge:
How do you create visuals optimized for every touchpoint?
There is clearly no one-size-fits-all solution.
Thankfully, generative AI can help. It does more than writing social media copy, correcting grammar or generating product descriptions.
Generative AI can simplify A/B testing and visual optimization. It allows you to rapidly create, test and refine visuals for each specific channel.
In this blog, we will uncover strategies to use AI for creating visuals that boost engagement and conversions. By the end of it, you will have actionable tips to overcome the visual optimization challenge.
Click to jump ahead:
- The role of generative AI in visual optimization
- Using generative AI for A/B testing visuals
- Creating visuals tailored to specific audiences
- Advance AI-powered visual optimization tips
- Best practices for using AI in testing and optimizing
Understanding the role of generative AI in visual optimization
You have likely seen the remarkable efficiency and speed generative AI tools can bring to your marketing workflows if you have used tools like ChatGPT, Claude, Bing Chat or Google’s Gemini.
But if you’re not leveraging gen AI for your design use cases, you are missing out on good opportunities to engage your audience.
What generative AI offers to marketers
Generative AI tools can generate multiple design variations tailored to different audience segments and platforms.
These AI tools use advanced machine learning algorithms to understand the elements of a design — such as color, layout, typography and imagery — and create variations based on defined parameters.
For example, Venngage Muse can generate an infographic based on a simple prompt that you provide and allow you to customize the color scheme, adjust the placement of text or test different call-to-action buttons.
This process is incredibly fast and saves you the time and effort of manually creating each version. Instead of you wasting time on trial and error, AI can perform multiple tests in the same amount of time — and cause much less headache.
Experimenting with AI means you can create visuals faster and come up with variations that you can test with different segments of your audience.
Why testing and optimizing visuals is critical
Visuals play a critical role in user engagement and overall campaign success. People are 80% more likely to engage with content that includes visuals. But you wouldn’t know that if you don’t measure how your audience receives your published content.
There is another, bigger challenge: every high-traffic platform has different dimensions and audience preferences. For example, LinkedIn favors visuals that are 1200 x 627 pixels, while X.com (formerly Twitter) optimizes for 1200 x 675 pixels and Facebook’s ideal image size is 1200 x 630 pixels.
These are seemingly minor differences, but they can drastically affect how visuals appear across platforms. If you try to post an image you have created for LinkedIn to X.com, for instance, it will appear blurry or poorly cropped which will distract rather than attract audiences.
Here’s an example of a blog graphic that, when I posted on X.com, appeared cropped and stretched because it wasn’t optimized for the platform:
Similarly, visuals that perform well on desktop websites may not translate to mobile users. Over 60% of global website traffic comes from mobile devices, but visuals optimized for desktops often don’t display well on mobile screens, which leads to a poor user experience.
This can cause higher bounce rates and a drop in conversions — something no marketer wants. For example, a hero image designed for a desktop layout might cut off critical elements when viewed on a mobile device, rendering the message ineffective.
Here’s an example from an Ivy League university’s website that translates poorly into mobile screens:
To be fair, the desktop version of the website isn’t great from the user experience point of view to begin with. But there are three things specifically wrong with its mobile experience:
- Text readability issues
- Cluttered layout
- Confusing navigation
The reasonable solution here is to leverage generative AI tools for testing and optimizing visuals (in addition to other elements) across different platforms and device types. Using AI tools to resize and tweak designs to fit specific real estate requirements can help you eliminate the grunt work of adjusting every visual asset for each use case.
Iterating designs based on user behavior and performance data is another key advantage. By testing multiple variations and analyzing how users interact with each, you can identify what kind of visuals resonate the most with your audience. You can use this data to create visuals that give you the most attention, engagement and clicks.
How to use generative AI for A/B testing visuals
Some, if not all, generative AI tools allow you to create multiple versions of a single visual effortlessly, making it an invaluable tool for A/B testing. You can tweak elements like color schemes and layouts to quickly generate diverse design variations to test with your audience.
Here are three tools that make creating image variations a breeze:
- Runway ML: Runway ML uses machine learning to generate and modify designs based on input parameters. For instance, you can adjust color palettes or create unique visual styles by training the model on your brand’s assets.
- Adobe Firefly: Adobe Firefly lets marketers experiment with design elements like backgrounds, textures and overlays. It’s particularly helpful for creating polished, professional visuals in seconds.
- Fotor: While primarily a photo editing tool, Fotor’s AI features allow for quick adjustments to typography, layouts and even mood-based filters, making it versatile for visual testing.
Did you know? Netflix used A/B testing to boost its market capitalization by up to $200 billion. They tested variables like blocking password sharing and launching an ad-supported version, which led to improved user engagement and revenue growth.
Test specific design elements
Once you have AI-generated variations ready, the next step is to decide which elements to test. Not all design elements hold equal weight in influencing user behavior, so it’s crucial to focus on those that directly impact engagement and conversions.
Here are some examples of high-impact elements to consider:
- Imagery: Test different image styles, such as product shots versus lifestyle images. If you are generating visuals for the B2B audience, try testing between isometric and orthographic images.
- Typography: Experiment with font types, sizes and colors to see which resonates with your audience. You can read our how to pick the right fonts for your brand to get started.
- Button placement: Position call-to-action (CTA) buttons in various areas of your design to identify the most effective spot.
If you want to make your designs accessible to people with visual disabilities, check for elements like color contrast, headings and alt text.
With Venngage’s Accessibility Testing Tools, you can even preview how your designs appear to individuals with visual impairments, including those with complete color blindness, difficulty detecting colors and blurry vision.
Here’s an example of how you can test how a document appears to people suffering from Deuteranopia — a type of color blindness that makes it difficult to distinguish between red and green:
Once you have a variety of AI-generated visuals, the next step is to integrate them into an A/B testing platform to identify the best-performing version.
A/B test your AI-generated designs
Did you know that you can connect your AI design tools with A/B testing tools? Integrating the two allows you to improve your A/B testing process and improve your design iterations based on facts, instead of opinions.
Here are four A/B testing platforms that you can integrate with a range of generative AI tools to boost your visual testing process.
Tools for A/B testing:
- Optimizely: Optimizely offers robust A/B testing features, advanced targeting, personalization and program management tools. It also supports integrations with various analytics and generative AI tools, which makes it an ideal choice for experimentation across web and app platforms.
- Visual Website Optimizer (VWO): The tool offers comprehensive conversion rate optimization with tools for A/B testing, multivariate testing and behavior analysis. It features an in-app editor and advanced segmentation for precise targeting.
- AB Tasty: The tool is ideal for testing design elements with real-time data tracking to measure engagement metrics such as time on page or scroll depth.
- Kameleoon: This platform combines A/B testing with AI-powered personalization. It supports complex experiments and offers an intuitive editor. Kameleoon also integrates with a variety of analytics tools.
Best practices for defining test parameters and goals
Establishing clear, measurable parameters is crucial for any A/B test. Without them, it’s challenging to determine the effectiveness of your AI-generated designs. Your goal should be to align your visual testing with key performance indicators (KPIs) that reflect your marketing objectives.
Here are some parameters to consider for your tests:
- Click-through rates (CTR): Evaluate which design encourages the highest number of users to visit your landing page. This metric helps assess how compelling your visuals are at driving immediate action.
- Engagement: Track user interactions, such as hovering over buttons or clicking on elements, to identify which designs resonate most with your audience.
- Time on page: Observe how long users spend on your page with different designs to determine which layout holds their attention better.
- Conversion rates: Analyze the percentage of users completing desired actions — whether signing up for a newsletter or making a purchase — based on the visuals they encounter.
By focusing on these metrics, you can refine your visuals to achieve better alignment with audience preferences and campaign goals.
Analyze test results to refine visuals
The value of A/B testing lies not just in gathering data but in translating it into meaningful insights that you can act on. By analyzing data, you can find what your audience prefers the most and refine your designs for optimal performance.
When analyzing your A/B test data, consider these key approaches:
- Identify patterns: Analyze recurring trends, such as a particular color scheme or font style consistently outperforming others.
- Segment results: Break down data by demographics, device types or regions to understand preferences within specific audience segments.
- Compare metrics: Evaluate performance across KPIs like engagement, conversions and bounce rates to determine what drives the most impact.
Iterate designs for better performance
Once you have identified a winning design, use AI to iterate further. For instance, if a particular layout performs well, experiment with slightly different variations of font style or button color to improve engagement. Regularly refining your visuals ensures they stay relevant and continue delivering strong results as user preferences evolve.
By combining generative AI with robust A/B testing, marketers can create highly optimized visuals tailored to their audiences, driving better engagement and campaign success.
Creating visuals tailored to specific audiences
The problem with AI is that everybody and their moms are using it. That means if you are relying 100% on AI to create content, your visuals will most likely look like a dozen others out there.
To stand out and make an impact, your visuals need to connect with the audience they are designed for. A generic approach rarely works, especially in today’s noisy digital landscape.
Thankfully, you can use generative AI to customize your designs to appeal to specific segments, helping brands stand out and engage their audience more effectively.
Tips to personalize your visuals with AI
We already talked about how you can use A/B testing to segment users based on demographics, location or device type.
Let’s say that your designs for an online apparel brand are a natural hit among Gen Z users on Instagram. That means mobile users who are likely into K-pop, trending Netflix shows or TikTok hook steps.
Or perhaps you are a SaaS startup that has found its audience in millennial entrepreneurs on LinkedIn. These are users who value clean, subtle and professional designs.
Based on these findings you can prompt your favorite gen AI tool to help you create visuals that match these unique preferences across platforms. For example, you can create “dank memes” for the first group to strategically position your brand as the cool brand that “gets it.”
Okay, not so dank after all. But you get the drift.
For the second group, you are better off creating an informative infographic of how Apple started:
It took me less than 10 seconds to come up with this infographic. And the best part? I can regenerate the infographic or customize it to match my audience’s liking.
Want to try it yourself? It’s free!
Pro-tip: Want to avoid coming up with similar-looking AI-generated designs? The more nuanced your AI prompts, the more unique results you will get. Also, don’t forget to customize the AI result and add your personal touch.
Test localized designs
Localization goes beyond just translating text — it involves adapting visuals to reflect the cultural nuances of different audiences. AI can simplify this by creating designs tailored to various languages and cultural contexts. For example, a global cosmetics brand could generate AI-driven visuals featuring diverse models, appropriate color palettes and locally relevant themes.
Testing these designs is equally important. Once again, you can connect your AI tool with an A/B testing platform to identify the most effective variations for each market. This can help you scale your designs quickly and with greater precision.
Did you know? The Vietnamese language uses the same word for ice, rock, stone or kick. Next time you are in Hanoi and want to order an iced Mocha in a local café, be careful — you might get a Mocha with a side of stones or with an extra-strong kick!
Beyond A/B testing: Advanced visual optimization with AI
While A/B testing gives you a solid foundation for understanding which design elements perform best, the growing complexity of marketing environments might require you to look for more nuanced optimization strategies.
These advanced techniques allow you to not just test individual elements but also predict and refine designs in real-time. Let’s go over how you can use AI to optimize your visuals by implementing multivariate testing and predictive analytics.
Multivariate testing with AI-generated designs
Sometimes, testing a single change in a visual — like swapping out a CTA button color — just isn’t enough. You may need to experiment with multiple variables at once, such as color, layout, typography and even imagery, to see how these components interact with each other. This is known as multivariate testing.
In multivariate testing, instead of testing one design variation, you can test multiple combinations of elements to identify which combination of changes has the highest impact on performance.
Tools like Unbounce or Convert.com allow you to rapidly generate numerous design combinations based on different criteria and user preferences.
An online retail brand like Warby Parker could use multivariate testing to try out different combinations of visual elements on their product pages. They could test variations of product images, text placements, CTA buttons and background colors to optimize the user experience.
The brand can quickly generate hundreds of unique designs without having to outsource their design requirements or extensive manual work.
Predictive analytics for design performance
You can test design variations that are already published to see which performs better. But how do you choose the designs that will perform best?
This is where predictive analytics comes in. AI tools can analyze historical data and user behavior to predict how new design variations will perform.
Tools like Adobe Sensei and HubSpot’s AI-powered analytics tools can track user interactions, such as clicks, scroll depth and engagement time and predict the likelihood of a design leading to conversions. These tools can also provide insights based on heatmaps, which show where users are clicking, hovering or spending the most time on your page.
For example, a SaaS company like Notion can use predictive analytics to optimize the design of its onboarding page. By analyzing past user behavior and applying predictive models, Notion can figure out which design elements will likely improve the free-to-paid upgrades.
As an aside, we recently surveyed 292 marketers globally to understand their predictions about the future of AI in design and content creation. If you’re curious about these insights, check out our full blog post on how AI is reshaping design and content.
Best practices for leveraging AI in testing and optimizing visuals
Visual testing and optimization is more than just experimenting different variations — it’s about identifying ideas and strategies that can have the biggest business impact. To make the most of AI, it’s crucial to approach the process systematically.
Below are three best practices to guide your testing and optimization strategy with AI.
Set clear test objectives
Earlier, we discussed the importance of defining test parameters for your A/B testing experiments.
To make your experiments worthwhile and conclusive, you should run each of your tests with a well-defined objective and specific success metrics. If your goal is to improve CTR on social media ads, your success metric might be an X% increase in CTR. Setting precise goals helps you measure the impact of your visual changes more effectively.
Here’s an example to put this into real-world perspective. Let’s say you are a marketer working for a food delivery startup. One of your immediate goals is to increase orders by testing two visuals on your website: one featuring a close-up of mouthwatering dishes and another highlighting a quick delivery icon.
By measuring the click-to-order ratio for each, you can identify which visual resonates the most with your audience and gets more orders.
A common misconception about A/B testing is that it’s simply about putting two variations of an element…and seeing which one performs better. In reality, it goes much deeper. You need to understand what you’re testing, why you’re testing it and how it will impact your users.
Santiago Vera, CRO Specialist, Omniconvert
Manually review the AI-generated designs
While AI can speed up the process of generating designs, it doesn’t replace the importance of human oversight. Human input ensures that AI-generated visuals align with brand guidelines, cultural nuances and the emotional tone of your message.
This personal touch also adds authenticity and make your content feel less generic. For instance, manually adjusting the color palette of AI-generated ads to match your brand colors can create a more cohesive visual identity. Tools like AdCreative.ai allows you to automate ad creatives and product photoshoots — but with a manual review in place for you to edit and change the designs.
Test and iterate continuously
Testing isn’t a one-and-done process. Regular experimentation ensures your visuals stay fresh and effective, especially as audience preferences evolve. Repeated testing allows you to build on past successes and gradually refine your visuals.
Frequent tests can also reveal trends over time. For instance, you might discover that while minimalist designs perform well in one quarter, bold, vibrant visuals take precedence during the holidays due to seasonal preferences or cultural shifts.
Scale your testing and optimization with AI for visual excellence
In today’s noisy digital landscape, standing out is both a challenge and an opportunity. Generative AI can be a catalyst to help you not just stand out, but also improve your design process. It empowers you to create stunning visuals in seconds, optimize them for better impact and test them at scale.
But remember, human touch and adapting to market preferences are still your greatest assets. Marketers who can integrate AI tools with their own creative intuition will elevate their brands to greater heights.
Ready to take the leap? Start designing great-looking visuals optimized to your audience preferences with Venngage AI and simplify your marketing design process.
