Increasing experiment velocity: Run tests faster

Ever notice how some companies seem to ship new features every week while others take months to change a button color? The difference isn't just about moving fast - it's about testing velocity, or how quickly you can run experiments to figure out what actually works.

Here's the thing: companies that run more experiments learn faster, period. They're not guessing what users want; they're constantly testing, measuring, and iterating. And in a world where user preferences shift faster than ever, that ability to quickly validate ideas isn't just nice to have - it's survival.

The critical role of testing velocity in experimentation

Testing velocity is basically how fast you can go from "what if we tried this?" to getting real data on whether it worked. It's not just about launching experiments quickly - it's the entire cycle from ideation to implementation to analysis.

The teams at companies like Amazon and Google have figured this out. They're running thousands of experiments annually, and that's not by accident. Harvard Business Review found that companies with high testing velocity see significantly better ROI because they're constantly learning what drives user behavior. More experiments mean more shots on goal, and more chances to find those game-changing insights.

But here's where it gets interesting: testing velocity is actually a pretty good indicator of how mature your experimentation program is. Optimizely's research shows that programs evolve from basic infrastructure (just trying to run tests without breaking things) to advanced stages where teams across the company are collaborating and much of the process is automated. The mature programs? They're the ones gaining deep customer insights and driving real business impact.

Of course, getting to high velocity isn't exactly straightforward. Most teams hit the same walls: not enough developers, leadership that's risk-averse, or data infrastructure that takes forever to give you answers. The experimentation gap is real - teams know they should be testing more, but practical constraints keep getting in the way.

The solution isn't just throwing more resources at the problem. You need to build a culture where experimentation is the default, get different teams working together, and invest in the right data infrastructure. Do that, and you unlock what your experimentation program can really do.

Practical strategies to accelerate testing velocity

Let's get tactical. The single best way to increase your testing velocity? Run smaller experiments.

Think about it - instead of building a completely redesigned checkout flow that takes three months, what if you tested just the button placement first? Or the copy? Or the number of form fields? The software development community on Reddit nailed this when discussing the balance between coverage and speed. Smaller tests mean shorter development cycles, faster launches, and quicker learning.

Here's what else actually moves the needle:

Automation is your best friend. The Vue.js community discovered this when they started using tools like Vitest with GitHub Actions for parallel testing. Instead of waiting hours for tests to run sequentially, they're done in minutes. Less waiting means more experimenting.

Use proxy metrics to make decisions faster. This is something Statsig's data science team advocates for strongly. Rather than waiting weeks to see if a change impacts your quarterly revenue target, find metrics that correlate with your main KPI but move faster. User engagement in the first 24 hours often predicts long-term retention. Click-through rates can signal future conversion improvements.

Stop over-analyzing everything. The experienced developers on Reddit put it perfectly: you're wasting time if you're doing deep dives on every single metric for every experiment. Pick your primary KPIs, check for major red flags in secondary metrics, and move on. The goal is learning quickly, not writing a dissertation.

One more thing - manage your time ratio between building and testing. If you're spending 80% of your time on test infrastructure and only 20% actually experimenting, something's wrong. Flip that ratio.

Leveraging advanced techniques to run tests faster

Once you've got the basics down, it's time to level up with some advanced techniques that can seriously accelerate your testing.

CUPED (Controlled-experiment Using Pre-Experiment Data) is a game-changer that most teams haven't discovered yet. Statsig's implementation shows how powerful this can be. By using data about user behavior before the experiment starts, you can reduce the noise in your results by up to 50%. Less variance means you can detect real effects faster with smaller sample sizes. It's like having X-ray vision for your experiments.

Then there's adaptive allocation and sequential testing. These sound complex, but the idea is simple: why keep sending traffic to a losing variant? Contextual bandits dynamically shift more users to the winning variant as data comes in. Sequential testing lets you call experiments early when there's a clear winner, without increasing your false positive rate. Google's been using these techniques for years to run more experiments with the same amount of traffic.

Parameters and layers might be the most underrated way to speed up experimentation. Instead of hardcoding experiment logic and redeploying every time you want to test something new, you use configuration parameters. Want to test a new recommendation algorithm? Update a parameter. New pricing tier? Parameter. No code changes, no deployments, no waiting. LinkedIn's engineering team credits this approach as a key factor in their ability to run 35,000 concurrent experiments.

The teams getting the best results combine all of these:

• Run tests in parallel across different parts of the product

• Use CUPED to get results faster

• Implement adaptive allocation to maximize learning

• Set up parameters so non-engineers can launch tests

The compound effect is massive. Each technique might give you a 20-30% improvement, but stack them together and you're suddenly running 10x more experiments than before.

Building a culture and infrastructure for high-velocity experimentation

Here's the hard truth: all the techniques in the world won't help if your culture isn't built for experimentation.

LinkedIn's ability to run 35,000 concurrent experiments isn't just about technology - it's about having teams across the company that think in experiments. Product managers propose tests instead of features. Engineers build with experimentation in mind. Data scientists are embedded in product teams, not sitting in a separate analytics org.

Duolingo's exponential growth story is instructive here. They didn't just run a lot of tests - they built a systematic approach where winning experiments were quickly expanded and losing ideas were killed fast. Every team had clear metrics, the infrastructure to test against them, and the autonomy to act on results.

The infrastructure piece is crucial but often overlooked. You need:

• A modern data stack that can handle real-time analysis

• Engineering systems built for experimentation from the ground up

• Tools that make it easy for anyone to launch and analyze tests

Microsoft, Amazon, and Google have figured this out. They're running thousands of experiments not because they have massive teams, but because they've made the infrastructure investment. Cheap, scalable systems mean experiments cost almost nothing to run.

Start with the basics, then layer on sophistication. Eppo's approach is worth studying - they focus on proactive diagnostics to catch issues early, democratize experiment planning so it's not bottlenecked on a few people, and define clear criteria for when experiments end. No endless tests that drag on for months.

The companies winning at this share a few characteristics:

• Experimentation is everyone's job, not just the growth team's

• Infrastructure makes testing easy, not painful

• Results are shared widely and celebrated

• Failed experiments are learning opportunities, not career risks

Build this foundation, and high testing velocity becomes the natural outcome, not something you have to force.

Closing thoughts

Testing velocity isn't just another metric to track - it's the heartbeat of modern product development. The companies that can go from idea to validated learning the fastest are the ones that win. Not because they're reckless, but because they've built the culture, processes, and infrastructure to experiment efficiently at scale.

The good news? You don't need Google's resources to dramatically improve your testing velocity. Start with smaller experiments, automate what you can, use proxy metrics, and build from there. Layer on advanced techniques like CUPED and adaptive allocation as you mature. Most importantly, make experimentation part of your team's DNA, not just something you do when you have extra time.

Want to dive deeper? Check out Statsig's guides on experiment velocity features and speeding up A/B tests. The Harvard Business Review's analysis on the power of online experiments is also worth your time.

Hope you find this useful! Now stop reading and go run an experiment. 🚀