Concurrent testing: what it is and why it's important
Most experimentation programs still move in single file:
One test at a time. Wait. Decide. Repeat.
That approach feels safe, but slows down growth and misses what modern experimentation is really about.
If your product and marketing teams are running tests across different funnels, campaigns, or features, you’re already doing some form of concurrent testing.
But are you doing it with intention? With visibility?
Experimentation is a spectrum; so is concurrency
In the 2025 Experimentation-Led Growth Report, high-performing companies reported that they didn’t just run A/B tests. They ran personalization tests, progressive rollouts, feature flag observations, and high-rigor experiments, often all at once, across multiple teams and zones.
Each method comes with different levels of risk and complexity. But all benefit from a shared truth:
The faster you learn, the faster you grow.
Running tests in parallel isn’t reckless; it’s how experimentation scales.
The real fear: what if tests interfere with one another?
This is the core fear: “What if one test messes with another?”
It’s a valid fear.
But it's also rare, and, more importantly, manageable with the right safeguards.
When two experiments affect the same user experience or behavior, they might interact, but modern tools and practices are designed to detect this.
It's also important to remember that not all experiment types need the same degree of separation.
Let’s look at how concurrent testing plays out across the spectrum:
Progressive delivery (low rigor)
Progressive delivery is about rolling out features gradually, rather than flipping on changes for everyone at once. This makes it easier to catch technical or UX issues during rollout.
Concurrency risk: Very low. Changes live behind flags or go to limited audiences.
Tips:
- Avoid overlapping major feature rollouts
- Monitor key metrics (e.g. error rate, bounce rate, conversion rate, etc.) before scaling
Feature flag observation (low to medium rigor)
Teams use feature flags to observe user behavior or adoption trends without running a full experiment.
Concurrency risk: Low. Avoid multiple visibility tweaks in the same flow.
Tips:
- Track who saw what
- Be mindful of conflicting UI changes
Personalization with holdout (medium rigor)
Personalization optimizes and tailors user experiences for specific segments (based on things like demographics or purchase history) while measuring uplift.
Concurrency risk: Medium, especially if audiences overlap or stack across touchpoints.
Tips:
- Ensure clean segment logic
- Use holdouts for measurement
- Watch for unintentional message layering
A/B testing (high rigor)
A/B testing is used to prove causal lift by splitting users randomly into groups to compare performance between two or more variations.
Concurrency risk: Higher, but manageable with best practices. Overlapping A/B tests can muddy results because of their natural ability to isolate cause and effect.
Tips:
- Avoid testing the same element in multiple experiments.
- Use randomization, exposure logging, and cross-campaign analysis to keep results clean.
Best practices for safe concurrent testing
When you employ concurrent testing, make sure you have appropriate safeguards to keep your test results valid and actionable.
1. Avoid overlapping tests on the same element
Don’t run two tests that change the same piece of the experience (like the same promo module or form layout). If they interact, you won’t know which one caused the change.
Instead, map your test areas. If overlap is unavoidable, consider combining ideas into a multivariate test.
2. Use randomization, exposure logging, and cross-campaign analysis
Random assignment ensures clean comparisons. Logging exposure tells you which users saw what. Cross-campaign analysis helps you detect unintended interactions.
Kameleoon includes built-in tools for all three so you can:
- Keep data clean
- Attribute outcomes correctly
- Learn from overlaps, not fear them
3. Monitor for interaction effects and adjust if needed
Even when tests live in different zones, results might combine in unexpected ways.
What to do:
- Monitor performance during the test
- Analyze variant combinations (e.g. Test A1 + Test B2) after the test finishes
- If interaction appears, adjust rollout or test design in response
What leading teams already know
Microsoft runs over 10,000 experiments per year. At such a scale, concurrent testing is a necessity, rather than a choice.
Meanwhile, Booking.com runs over 1,000 concurrent experiments at any given time.
These companies are able to experiment at scale because their concurrent tests enable their teams to see how their experiments affect each other.
Kameleoon supports concurrent testing across the full spectrum with exposure tracking, zone mapping, and interaction alerts, giving all teams visibility into overlapping experiments.
You don’t need to test everything at once. But if you’re serious about experimentation at scale, concurrent testing isn’t risky.
It’s required.
