The Product Analytics Dashboard includes a built-in A/B Testing engine that lets you run controlled experiments directly through your product configuration — no external tools or coding required.
What Can You Test
You can experiment with almost any part of your checkout flow. 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:
1. Define Your Goal and Hypothesis
First, decide what you want to improve or learn from the test.
Create a simple hypothesis around the change you plan to make, such as: “Adding an upsell offer will increase overall revenue per user without hurting conversion rate.”
2. Create Two Product Variations
Version A: Control — your current setup
Version B: Variant — the change you want to test
Create a duplicate of a product or checkout in the system and then modify only Version B, for example, add an upsell, change pricing, adjust content).
Each version will have its own Product ID in the system once configured.
3. Notify Ventrata to Launch the Test
Send both Product IDs and a brief description of what your are testing to the Ventrata team. We will configure the traffic split and ensure users are randomly assigned to A or B.
4. Run the Experiment
Once launched, your customers will automatically see either Version A or Version B at random. The Product Analytics system will track all relevant metrics — conversion, revenue, upsell rate, etc.
📒 NOTE
Avoid making unrelated changes during the test to keep results reliable.
5. View Results in the A/B Testing Dashboard
The dashboard compares both versions side by side and automatically calculates statistical significance.
You’ll see the metrics that matter to your goal, such as:
conversion rate,
revenue,
click-through rates on the upsell
Updates happen continuously as new data arrives.
6. Interpret and Decide
Once the test has run long enough:
If Version B wins: roll it out as your new default.
If results are inconclusive: you've validated that the change does not materially affect performance — time to test a new idea.
Check secondary effects: for example, higher AOV but slightly lower completion rate.
Ventrata's analytics team is available to help interpret results or advice on next steps.
📘 EXAMPLE
Test adding an upsell:
Version A: no upsell
Version B: upsell offered
Review:
Whether AOV increased
Whether completion rate changed
How many users bought the upgrade
Overall revenue impact
If Version B shows a higher AOV with no drop in conversion, it’s likely a positive change.
If it increased revenue but caused a slight drop in completion, you’d weigh the trade off.
Armed with that knowledge, you can confidently deploy the winning variant or refine the experiment further.