One of the most powerful aspects of the suite is the built-in A/B Testing capability. This allows you to run controlled experiments to scientifically determine which changes improve your conversion rate or user experience.
The A/B testing is deeply integrated into your product configuration, meaning you can set up tests using the same familiar Ventrata product settings (no need for external A/B testing software).
What Can You Test
Virtually any product configuration of your checkout flow can be experimented on. Common test ideas include:
Pricing Strategies: Experiment with different price points or discount levels for the same product. For example, version A at standard price vs. version B at a 10% lower price measure the impact on conversion rate and revenue to find the optimal price.
Content & Messaging: Test different product titles, descriptions, images, or badges. You might find that a certain headline or image drives more trust and clicks. This helps identify which content resonates best with your audience.
And lot more
How to Run an Experiment
Running an A/B test with Ventrata is a structured process, but it’s designed to be straightforward. Follow these general steps to set up and execute a test:
Define Your Goal and Hypothesis
First, decide what you want to improve or learn from the test. For example, your goal could be “increase the checkout completion rate” or “raise the percentage of customers who choose an upsell.”
Formulate a hypothesis around the change you plan to make (e.g., “Adding an upsell offer will increase overall revenue per user without hurting conversion rate”). Having a clear goal will help in interpreting the results.
Create Two Product Variations
Version A will be the Control (the standard or current setup), and Version B will be the Variant (with the change you want to test). For instance, you might create a duplicate of a product in the system and then modify the duplicate: if you’re testing an upsell, add the upsell step to Version B only; if testing pricing, set a different price on Version B; if testing content, change the image or wording on Version B, and so on. Each version will have its own Product ID in the system once configured.
Notify Ventrata to Launch the Test
After preparing your two versions, send the details (both Product IDs and what the difference is) to the Ventrata team. Our team will handle the behind-the-scenes configuration to enable the experiment.
Essentially, we will set it so that traffic to your checkout is automatically and evenly split between Version A and Version B. This random assignment is crucial for a valid test it ensures each variant is shown to a comparable audience. You don’t need to do any technical implementation for the split; just coordinate with us and we’ll get it running.
Run the Experiment
Once launched, your customers will start seeing either Version A or Version B of the checkout at random. From the user’s perspective, it’s seamless they’ll just experience one of the two variations when they interact with your checkout widget.
The Product Analytics system will be busy tracking all user interactions and outcomes for both versions in real time. This includes measuring the key conversion metrics relevant to your goal (for example, purchase rate, or upsell take-rate, etc.) for each variant. It’s important during this phase not to introduce other major changes that could skew results; let the test run until you have a large enough sample to draw conclusions.
View Results in the A/B Testing Dashboard
We will provide you access to a dedicated A/B Test Results dashboard for your experiment. This dashboard will display the performance of Version A vs. Version B side by side.
You’ll see the metrics that matter to your goal (e.g. conversion rate, revenue, click-through rates on the upsell, etc.), and the platform will automatically apply statistical significance testing to show whether any difference in performance is likely due to the change or just chance.
The results are updated continuously as data comes in. For example, if Version B is outperforming Version A significantly on the purchase rate, the dashboard might highlight that result with a confidence indicator. This saves you from manually crunching numbers the system does the math for you.
Interpret and Decide
Once the test has run for an appropriate duration and the results stabilise, it’s time to decide on the next step.
If one version clearly wins (say, the data shows a statistically significant lift in conversion for Version B), you might choose to roll out that change to all users (i.e. make Version B the new default).
If the test is inconclusive or shows no meaningful difference, you’ve learned that the change didn’t impact the metric and you might iterate and test a new hypothesis.
In analysing the outcome, consider not just the primary metric but also any secondary effects (for example, did the upsell increase average order value but perhaps slightly reduce the overall completion rate?).
The built-in significance calculations will guide you, and remember, our team is available to help interpret the results. We can assist in understanding why a variant won or lost, and suggest follow up experiments. Testing is often an iterative process each experiment’s outcome can spark ideas for the next one.
📘 EXAMPLE
To illustrate, imagine you suspect that adding an upsell opportunity could increase revenue.
You set up Version A as a checkout flow with no upsell (the status quo), and Version B as the same flow but including a step that offers an upgrade for example, a premium package. During the experiment, you observe that in Version B a certain percentage of users click and purchase the upsell.
Key questions you’d look at include:
Did offering the upsell increase the overall average booking value (revenue per order)?
Did it affect the completion rate (do just as many people finish checkout, or did some get distracted and drop out)?
What portion of users chose the upgrade, and how much extra revenue did it generate?
The A/B test dashboard will show you these metrics. If Version B shows a higher AOV with no drop in conversion, it’s likely a success you’ve found a positive change. If it increased revenue but caused a slight drop in completion, you’d weigh the trade off. All these results would be clearly visualized, providing a data driven conclusion on which version performed better and why.
Armed with that knowledge, you can confidently deploy the winning variant or refine the experiment further.