It’s a common question among product, marketing, and development teams: How many A/B tests should you run each month? If you’re a digital leader or working on conversion rate optimization, you’ve probably wondered about the answer, too.
While it’s a fair question, it might not be the most helpful one for supporting your experimentation program and organizational goals. It’s possible that what you’re really thinking about is how you can run tests, draw conclusions, and implement improvements more efficiently.
That’s because whether you’re a startup or an enterprise, there’s no single number of tests or rate of testing that guarantees success. The right A/B testing cadence will depend on your traffic, resources, and testing maturity. What matters most is making the effort to experiment consistently, learning from the results, and redirecting those learnings back into your customer experience.
If you’re still looking for data, here are some common benchmarks:
- For sites with 10,000–50,000 weekly visitors: Maximum of 2–3 tests per month
- For sites with 50,000–200,000 weekly visitors: 3–5 tests per month
- For sites with 200,000+ weekly visitors: 5–10 tests per month
Whether you’re above or below those amounts, keep in mind that not every test offers the same value, and it’s more important to scale tests intentionally than to increase testing volume. Be sure to have the proper experimentation infrastructure and protocols in place before aiming for these numbers to help you learn as much as possible from your experiments.
Here, we’ll break down some of the best ways to structure your A/B testing cadence, which factors contribute most to your ideal testing rate, and how to maximize efficiency.
What Influences Your Ideal Testing Volume?
1. Traffic Volume and Segment Size
With insufficient traffic, your tests can drag on for weeks without reaching statistical significance. Of course, this means enterprise companies with millions of monthly users will be able to test more aggressively than an early-stage startup with a limited audience. Don’t forget that segmentation, which can be an important part of your testing strategy, also impacts your sample size. If you’re testing a highly specific audience (such as first-time visitors on mobile), your audience and testing velocity will shrink.
2. Test Complexity
If you’re testing a button color change, the experiment will likely run quickly. On the other hand, a multi-step onboarding flow or change to backend logic requires more resources, involves more risk, and can take longer to develop.
3. Team Capacity
Who owns testing in your organization? Some organizations manage tests across multiple domains or manage it among other competing priorities. In those cases, your A/B testing cadence will be lower. When testing is concentrated within a dedicated experimentation team, you’ll be able to handle more experiments.
4. Tools and Setup
Monetate is designed to eliminate common bottlenecks by giving marketers and product owners the ability to launch tests independently through a visual editor. When needed, developers can still support more complex, backend experiments. With a platform that supports a high level of flexibility, your team can run both quick, lightweight tests and impactful technical experiments without sacrificing speed or quality.
5. Optimization Maturity
Newer testing programs should focus on building an effective process and setting up the necessary infrastructure. Advanced, well-established programs can move faster and scale testing across teams using their existing workflows, test libraries, and governance frameworks.
Benchmarks and Ranges by Company Type
Here’s a glance at how a monthly A/B testing cadence tends to look across growth stages:
Startups / Early-Stage
- 1–2 tests per month while building infrastructure and learning
- Focus areas: onboarding flows, pricing page, key conversion paths
- Goal: develop a culture of testing and prove early value
Growth-Stage / Mid-Market
- 3–5 tests per month depending on traffic
- Expansion beyond CRO into product experience, retention, and feature adoption
- Goal: broaden the scope of experimentation to include lifecycle and product touchpoints
Enterprise / High-Traffic
- 5–10+ concurrent tests across multiple teams
- Often 60+ tests per year
- Goal: embed experimentation into every part of the business from marketing to product to customer success
While this can help you set new goals and priorities, it’s important to focus more on building an A/B testing cadence that supports continuous learning than on the precise number of tests.
How to Increase Testing Velocity
Want to run more tests without sacrificing quality? Develop your processes and make sure you’re using the right testing tools first.
1. Use Templates and Test Libraries
2. Build a Test Backlog and Prioritization Framework
3. Run Hybrid Experiments
4. Use Tools Accessible to Marketers and Product Owners
5. Adopt Group Sequential Testing
Remember: Quality Over Quantity
You might find yourself tempted to equate running more tests with success. However, this can lead you to run “micro tests” that don’t result in meaningful outcomes. Every test should drive a decision or give you new insight to act on.
High-impact areas to prioritize include:
- Pricing and packaging pages
- Onboarding experiences
- Retention and churn points
- Key product flows that support engagement
A Monthly A/B Testing Cadence You Can Use
Here’s an example of an A/B testing cadence for a mid-sized company running 3–5 tests per month:
- Week 1: Launch 1–2 new experiments
- Week 2: Monitor current tests, QA next batch
- Week 3: Analyze completed tests, share new takeaways across teams
- Week 4: Plan the next sprint based on learnings
This rhythm maintains a steady flow of experiments without overwhelming your team. It also builds and reinforces a cycle of consistently adding insights back into your personalization and optimization strategies.
Final Thoughts
So, how many A/B tests should you run each month?
Unfortunately, there’s no magic number. The right A/B testing cadence depends on your traffic, team, and goals. If you do want to use the recommended benchmarks, treat them more as a guide than a mandate. What matters most is learning something new to fuel your next iteration.
With Monetate’s advanced A/B testing and personalization tools, you’ll gain faster learnings, more impactful insights, and digital experiences that evolve with your customers.
Ready to scale your experimentation program? Talk to a personalization expert.
