We analysed over 9,000 A/B tests from 800 businesses to discover which types of experiments really boost referral performances.
By tweaking aspects like the text, rewards, and images in referral offers — and measuring results when tests were statistically significant — we now know the most impactful tests to run in 2025.
We also used Generative AI to analyse parts of our referral offer designs, combining these findings with our historic A/B test results to see which imagery captivates customers the most.
So, which design elements spark referrals? Are simple offers or detailed promotions better for acquiring new customers?
Read on to find out…
Our A/B testing experts revealed distinct patterns that impact conversion rates across different industries and types of experiments. Optimizing landing pages was found to be particularly effective in boosting conversion rates across different industries. Our experts also noted that incorporating machine learning algorithms can automate the analysis of user interactions, enabling predictive A/B tests and continuous improvement based on real-time data. Watch our 3 favourite stats from the research below, then read on to see which experiments caused the biggest uplifts.
1. 62% of the time, white background offers win vs non-white designs. (1.6x more likely).
2. 60% of the time, human-focussed designs lose vs designs without humans. (1.5x more likely).
3. 59% of the time, product-focussed offers win vs non-product-led designs. (1.4x more likely).
These are the big hitters. As a median, incentive-based tests boosted conversion rates by an impressive 91%.
For incentive-based experiments, testing scenarios like minimum spend versus no minimum spend, and percentage discounts versus flat amounts, lead to the highest uplifts.
Never underestimate the power of good design. Our findings show that imagery that focused on the product can significantly boost engagement vs simply showing a person or lifestyle image, especially in industries like fashion.
We also found that simple, bright, and higher contract images perform better than complex, darker, low contrast images.
Words matter. I’m not just saying that as a copywriter. For A/B testing, concise vs. descriptive language and different lead flows (referee-led vs. referrer-led) produces the highest uplifts.
How do your customers want to physically share referral offers? The list, and particularly the order, of sharing options can have a huge impact.
In our research, we saw the highest sharing rate uplifts when customers adopted Name Share® and placed it first in their share option list.
On average, you’ll see the best performance when you run at least five A/B tests. Statistical analysis is crucial for determining the effectiveness of different versions of a webpage or app. You’ll likely see the uplift much earlier — most likely after the first or second test — but your performance should continue to increase until the fifth experiment.
But which metrics should you measure for success? In our testing, we looked at:
Our research shows that these metrics improve significantly with continuous testing.
From our results, we know that impactful A/B testing varies depending on the industry. Optimizing various web pages for better engagement and conversion rates was found to be particularly effective in different industries. Here's what we found:
A/B testing is vital to running a successful referral programme — on average, our top-performing brands acquire 4x new customers in just six months of continuous testing. Using the right testing tools is crucial for conducting effective experiments and optimizing referral programs.
Why? Because A/B testing allows you to experiment and fine-tune. Whether you test offers, visuals or messaging, there are many ways to see what actually motivates customers to share and refer friends.
We’ve got the research to prove it.
Thought the priceless insights stopped there? Think again. We’ve got a couple quick tips to help you run better, more insightful A/B tests than ever before.
For more A/B testing advice, check out our video or read our blog on the top A/B testing mistakes to avoid.
Our research revealed how A/B testing can redefine the way you design your referral marketing campaigns. We know which tests produce the greatest impact across key industries and how they can introduce enormous improvements in customer acquisition and engagement.
Let’s recap…
Incentive-driven tests drive the highest impact within the Home, Pets, and Garden industry. In Fashion, design-driven tests were most influential, closely followed by incentive tests.
Our Health and Beauty results show incentive tests come out on top, with design changes close behind. Finally, incentives came in first for Food and Drink when trying to persuade customers to take action.
Ready to turn insights into action? Start A/B testing now and kickstart 2025 with KPIs and results to be proud of. Contact your account manager today.
A/B testing, otherwise known as split or bucket testing, refers to a method of comparing two different versions of a webpage or app against one another in order to understand performance. It remains one of the highest drivers of optimizing performance and improving the conversion rates of websites. Leading brands keep on doing this as guidance in building or updating their web material. Testing involves running two versions of a page against each other — with and without the variable under test-to know which will work better.
A/B testing is the ultimate resource that enables SaaS, eCommerce, and business websites to perform calculated changes in user experiences while gathering data about its after-effects. It can also be conducted on a single goal, say conversion rate optimization, in order to keep improving an experience iteratively over time. In that sense, A/B testing provides a reason to make changes in one's business while saving one from risks and enhancing their customer experience.
There are different sorts of A/B Tests, including:
There's a bit of planning and preparation before an A/B test can be started. Here are the steps:
Analysis of test results is a very crucial step in A/B testing. Following are some key things that one should keep in mind:
By following these steps and keeping these factors in mind, businesses can make data-informed decisions and enhance their website performance along with conversion rates.
A/B testing can be a powerful tool for improving conversion rates and user experience, but it’s not without its challenges. Here are some common mistakes to avoid:
There are many A/B testing tools and resources available, each with its own strengths and weaknesses. Here are a few popular options:
By leveraging these tools, you can streamline your testing process, collect data more efficiently, and make data-driven decisions to optimize your web pages and campaigns.