Build a Culture of Experimentation: Drive Growth and Innovation

A lot of companies say they believe in experimentation. They run A/B tests, analyze data, and make incremental tweaks. But the uncomfortable truth is that most of them aren’t actually innovating. 

They run safe tests, celebrate predictable wins, and avoid any experiment that might challenge their fundamental assumptions. Real growth—the kind that actually moves the needle—doesn’t happen in the comfort zone; it happens in the space where comfort ends.

But before we unpack what separates genuine experimentation cultures from performative ones, let’s look at exactly what experimentation culture is and some key strategies to build a culture of experimentation.

What is an experimentation culture?

An experimentation culture is an environment where teams are empowered to test hypotheses, learn from outcomes, and iterate based on data. It encourages curiosity and a mindset that views failures as learning opportunities rather than setbacks. 

Companies that embed experimentation at their core move faster, make smarter choices, and uncover opportunities that competitors miss. 

The gap, however, between saying “we’re data-driven” and actually being willing to let data drive decisions is huge. Building a true experimentation culture requires more than just good intentions. It demands a shift in mindset and a solid strategy to ensure it flourishes. 

Ben Labay, CEO of Speero, underpins the importance of a shift from an optimization to an experimentation mindset. In the age of AI, where the cost to produce different creative variations is decreasing, but the need for and speed at which these variations are produced is increasing, growth experimentation is more vital to every organization than ever before. 

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Labay suggests testing sets of variables and model testing rather than individual variables. For example, “test different AIs that create those variables, test different propensity variables, recommendations, etc.”

Key strategies to build a high-impact experimentation culture

Without a structured framework, experimentation becomes chaotic. Here are a few foundational principles to ensure you build a culture of experimentation that’s hypothesis-driven and aligned with your business goals.

1. Leadership sets the tone. 

True experimentation challenges hierarchy itself, encouraging creativity and risk-taking at every level. When leaders model this—sharing both wins and failures—they create psychological safety that empowers teams to innovate without fear. 

Amazon’s leadership meetings begin with reviewing data, ensuring hierarchy doesn’t trump insight, which is underpinned by this key principle:

Leaders are “obligated to respectfully challenge decisions when they disagree, even when doing so is uncomfortable or exhausting; they do not compromise for the sake of social cohesion.” 

Google famously allowed employees to spend 20% of their time on experimental projects, leading to innovations like Gmail and Google News. 

“We encourage our employees, in addition to their regular projects, to spend 20 percent of their time working on what they think will most benefit Google,” said co-founders Larry Page and Sergey Brin in 2004.

Strategy: 

• Set clear expectations that decisions should be based on data, not hierarchy.
• Tie executive incentives to experimentation-driven outcomes, ensuring leadership actively models and prioritizes a test-and-learn mindset.
• Hold regular reviews where leaders analyze both successful and failed experiments, reinforcing that testing is about learning, not just winning.
• Publicly share leadership-led experiments to help normalize risk-taking and motivate your team to test bold ideas.

2. Cutting the red tape: Autonomy in experimentation.

Every layer of approval slows execution and dilutes responsibility and ownership, creating a system that rewards inaction over innovation.

The result? Safe biased experiments that prove a desired outcome or teach you what you already know.

Netflix’s culture of “Freedom and Responsibility” allows teams to experiment without bureaucratic hurdles. The results? Their personalized recommendation algorithm, for example, was developed through continuous experimentation. Not to mention, Netflix’s experimentation culture has contributed to a 93% customer retention rate, one of the highest in the streaming industry.

Spotify’s squad model demonstrates what real experimental authority looks like. Teams take full ownership over features, allowing them to test and iterate without waiting for top-down approval. 

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For example, the now-iconic Discover Weekly playlist was a squad-led experiment that leveraged machine learning and user data to personalize recommendations. 

By decentralizing decision-making, Spotify enables rapid testing and innovation without bureaucratic slowdowns.

Strategy: 

• Streamline approvals by setting clear thresholds—small tests shouldn’t need approval, while high-impact ones should follow a fast-track process. 
• Empower teams with pre-approved testing budgets and decision-making authority to reduce bottlenecks. 
• Establish a “test first, review later” policy where experiments launch quickly, with results analyzed in regular review cycles.
• Provide training on experiment design and analysis so teams feel confident testing.

3. Align experimentation with strategic goals, not just micro-optimization.

It’s easy to get lost in the weeds of low-level optimizations. Strategic alignment ensures that every experiment has a purpose and ties back to broader business goals, maximizing meaningful, measurable impact. Guillame Cabane, founder of HyperGrowth Partners, suggests taking a hypothesis approach, starting by asking the right questions:

1. Problem: What problem are we solving? Why are we doing this?
2. Hypothesis: What’s our underlying belief? If we do this, what do we think will happen?
3. Evidence: What’s the data supporting our hypothesis?
4. Success: What outcomes and metrics are we optimizing for? What’s our current baseline?
5. Resourcing: What are the resources needed in order to get minimum viable learnings?
6. Prioritizing: What sample size and run-time are needed to run the experiment?

“Two weeks later you should ask post-mortem questions such as:”

7. How accurate were our baselining, resourcing, and run-time estimations?
8. What did we learn from our success or failure?

Netflix doesn’t just A/B test thumbnails for click-through rates. They experiment at a strategic level, leveraging machine learning and rigorous A/B testing to test different personalization algorithms to determine what keeps users watching.

For example, they found that personalized visuals significantly increased watch time, leading to dynamic artwork implementation across the platform. Instead of chasing short-term KPIs like click-through rates, Netflix focuses on what really matters—long-term retention.

Strategy:

• Prioritize experiments that impact business-critical KPIs (e.g., customer retention, LTV, acquisition costs)
• Require teams to justify why an experiment matters, ensuring every test contributes to growth, not just optimization.
• Encourage deeper analysis of test results—every test should contribute to growth, not just optimization.

4. Encourage experimentation through behavioral nudges.

Behavioral prompts—like framing experiments as opportunities for growth rather than risks or recognizing and rewarding innovative ideas—can encourage teams to integrate experimentation into their daily routines.

But, it’s important to set clear expectations and define what a ‘culture of experimentation’ looks like. Clarity can be achieved in many different ways. For example, Andrew Anderson, Head of Optimization at Malwarebytes, has the following guidelines for a discipline-based testing program:

1. All Test Ideas are Fungible
2. More Tests Does Not Equal More Money
3. It Is Always About Efficiency
4. Discovery is Part of Efficiency
5. Type 1 Errors are the Worst Possible Outcome
6. Don’t Target just for the Sake of It
7. The Least Efficient Part of Optimization is the People (Yourself Included)

Another example that comes to mind is metrics. What’s the one metric of success that will define your tests? Defining it ahead of time will help eliminate political upheaval in the case of an ambiguous test.

X (previously Twitter) does this to prevent hypothesizing after the results are known (HARKing). As they wrote:

“One way we guide experimenters away from cherry-picking is by requiring them to explicitly specify the metrics they expect to move during the set-up phase. Experimenters can track as many metrics as they like, but only a few can be explicitly marked in this way. The tool then displays those metrics prominently in the result page. An experimenter is free to explore all the other collected data and make new hypotheses, but the initial claim is set and can be easily examined.”

Strategy:

• Script critical moves: Set clear optimization rules (e.g., prioritize efficiency, avoid biased decision-making) and define key metrics upfront to prevent political resistance.
• Commit to a testing cadence: Commit to a weekly experiment release schedule for at least a month. This will help maintain momentum and ensure you run enough tests to make a good impact.
• Prime your team each week with a 5-minute plan: Use surveys to encourage data-driven thinking and reinforce experimentation. The “mere-measurement effect” suggests that simply asking about behavior increases follow-through.
• Gamification of experimentation: Track and display teams’ experimentation success in internal dashboards, creating friendly competition and motivating teams to run more tests.
• Create a “Fail Fast” Culture: Encourage rapid experimentation with a “fail fast” mentality, where teams are nudged to quickly test new ideas, embrace failure as a learning tool, and iterate quickly without fear of mistakes.

“At Ramp, we take on risks like a series A startup that’s looking for product market fit and is desperately clinging to its survival. We apply early-stage, aggressive tactics at a late stage situation. While it’s an unorthodox approach for most other businesses of our size, we believe it allows us to move at a high velocity and test/iterate faster than our competition.

We perform two-week sprints to constrain the size of the experiments, ensuring that we can test as many tactics as possible. The focus is high-velocity–our teams ship anywhere between 10-30 experiments per sprint,” said Cabane.

5. Build a system for continuous learning and iteration

Experimentation isn’t just a tactic—it’s a mindset. And like any good habit, its payoff compounds over time. The true value lies in what happens after the test: the insights you gather, the questions they raise, and the improvements they inspire. But to drive meaningful progress, teams need to not only run experiments but also close the loop between insight and action. 

Iteration is the key that turns isolated experiments into organizational momentum.

By establishing feedback loops, you ensure that the lessons learned from every experiment inform the next. That means treating experiments not as endpoints but as building blocks in a larger cycle of learning, refinement, and innovation.

Atlassian’s Experiment Week is a great example. It exposes the messy reality of experimentation, encouraging employees to challenge assumptions constantly. Teams present failures alongside successes and share insights across teams, creating a culture where learning is valued more than being right.

Meta built a centralized “Experiment Review” system where teams across product, marketing, and engineering document and share test results. This system prevents redundant testing, speeds up decision-making, and ensures teams learn from each other’s successes and failures.

Strategy:

• Establish a centralized knowledge base where all experiment results are documented and easily accessible to all teams.
• Schedule cross-functional meetings to review experiment outcomes and discuss what worked, what didn’t, and what can be improved next time.
• Encourage reflection on key experiments at team retrospectives and company-wide reviews, emphasizing learning from mistakes, not just celebrating wins.

Overcoming experimentation barriers

Every company loves the idea of experimentation—until it challenges existing assumptions, slows down decision-making, or requires leadership to admit they were wrong. 

Below are three common barriers hindering building a culture of experimentation and how to break them down.

Challenge #1: Streamlining Processes for Agility

Markets shift, customer behaviors evolve, and what worked yesterday might not work tomorrow. Endless approval chains and red tape don’t just kill momentum; they can lead to missed opportunities and let competitors take the lead. To be able to pivot when it matters most:

• Predefine risk thresholds and automate approvals: Set clear criteria for what requires approval and automated workflows to greenlight experiments that meet predefined conditions. 
• Create a rapid experimentation framework: Develop a standardized process for test execution, analysis, and iteration. 
• Limit unnecessary meetings: Replace lengthy approval discussions with async documentation or structured check-ins.
• Measure decision-making speed: Track the time from test proposal to execution to identify and eliminate bottlenecks. 

Challenge # 2: Breaking silos and building data trust across teams

Teams often treat their experimental insights as territory to be protected rather than wisdom to be shared. But breakthrough innovation doesn’t happen in isolation. It relies on shared visibility, cross-functional input, and, above all, data trust: a shared confidence in the accuracy, accessibility, and alignment of results across every team. 

When different teams work together, they design more comprehensive and innovative experiments, preventing silos, eliminating redundant work, and accelerating learning. Here’s how to cultivate more cohesion in your team:

• Establish a centralized experimentation dashboard: Create shared dashboards to provide real-time visibility into ongoing tests and results and prevent duplication.
• Hold regular knowledge-sharing sessions: Encourage teams to present key learnings, including failed experiments.
• Encourage cross-functional testing squads: Pair marketers, product managers, and engineers to design better experiments. 
• Incentivize collaboration: Recognize and reward teams that contribute to company-wide experimentation initiatives.

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Challenge #3: Testing ideas vs. relying on gut instinct

The most expensive decisions in business aren’t the ones that fail—they’re the ones we never test. When you bypass testing in favor of gut instinct, you bypass challenging your strongest convictions in exchange for validation.

The problem with gut feelings is that they’re often inaccurate, outdated, or just plain wrong. So, how do you shift from opinion-based decision-making to a test-driven approach?

• Make testing the default: Every major decision should be validated with an experiment before full implementation. Encourage teams to ask, “What data do we have to support this?” before moving forward.
• Challenge leadership assumptions: Regularly test the ideas leadership is most confident about. If they’re right, great. If they’re wrong, you’ve saved time, money, and effort.
• Encourage “disagree and test” thinking: When two teams have conflicting opinions, run an experiment instead of debating endlessly. Let data, not hierarchy, determine the direction.
• Track past gut-based decisions: Keep a record of instinct-driven decisions vs. test-driven ones. Over time, this will show clear patterns of what works best, making it easier to justify a culture of testing.

The comfort of consensus is perhaps the greatest threat to effective testing. Too many teams run experiments designed to succeed rather than to learn, creating an illusion of data-driven decision-making while actually reinforcing existing biases. This wastes resources and leads to poor decisions based on false confidence that can be costly to fix.

Measuring success: Going beyond surface-level KPIs

To measure the real impact of your experiments, focus on strategic metrics that align with long-term goals. While surface-level KPIs like conversion rates can show small wins, they don’t paint the full picture. Rather focus on:

• Strategic impact
• Learning from failures
• Recognizing effort, not just wins

Strategic impact

Too many companies measure success with vanity metrics—clicks, likes, or short-term conversion bumps—without asking the bigger question: Does this move the business forward? Instead of chasing easy optimizations, focus on strategic impact, prioritizing experiments that contribute to sustained growth:

• Revenue growth: Measure revenue per visitor, average order value, and customer lifetime value. Understanding CLV helps determine whether experiments lead to high-value, long-term customers rather than just short-term gains.
• Retention and engagement: Track repeat purchases, churn rates, and customer satisfaction. Successful experimentation isn’t just about acquiring new customers—it’s about keeping them engaged and maximizing their lifetime value.
• Scalability: Test changes that can be expanded across products, teams, or markets. To build a strong experimentation culture, focus on initiatives that drive long-term, cross-functional growth, ensuring that successful tests lead to scalable business impact.

Learning from failures

Not every test will succeed—and that’s a good thing. The most valuable insights often come from failed experiments that reveal what doesn’t work, helping teams refine strategies and make smarter decisions. Failed experiments:

• Reveal what doesn’t work before investing heavily.
• Challenge assumptions and help refine future strategies.
• Encourage innovation by making data-driven risk-taking a norm.

Recognize efforts and experimentation wins

A culture of experimentation thrives when teams feel empowered. The key is to celebrate both wins and learnings, reinforcing a culture where teams feel empowered to test bold ideas without fear:

• Breakthrough successes that drive measurable impact.
• Well-designed failures that provide critical learning.
• Teams that take risks and iterate based on insights.

Companies like Amazon and Netflix succeed because they don’t just test—they analyze, learn, and optimize continuously. Real growth comes from measuring what matters, learning from every test, and rewarding bold experimentation.

By emphasizing these strategic metrics, you’ll avoid the trap of celebrating small wins while overlooking bigger opportunities for transformation and growth.

The experimentation imperative

Experimentation isn’t a one-off task—it’s a core business strategy. To drive real growth and innovation, it needs to be embedded into your company’s DNA. Ask the tough questions, challenge your assumptions, and be open to change.

Don’t settle for safe testing that won’t move the needle. Question what you think you already know, test boldly, and focus on long-term success. That’s where the real insights live.

The key is building a culture where experimentation is the foundation of every decision, every strategy, and every initiative—not just a side project. When experimentation becomes a shared mindset rather than a separate initiative, real innovation takes root. (Explore more in our webinar.)

Whether you’re looking to learn experimentation fundamentals, level up through an Advanced Experimentation Masterclass, or improve testing accuracy, CXL offers expert-led courses designed to help you build a culture of experimentation, implement high-impact testing, and scale experimentation effectively.

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