From Hunch to Hypothesis

Experimentation isn’t a simple “switch” that a brand can flip, but more like a journey that benefits from small steps to create big shifts. 

Most brands start with a single test and eventually evolve into growth engines, such as using AI-powered tools, multivariate testing, and more.

But why even evolve from smaller tests to more complex ones? The answer is simple: high-maturity teams that continue to expand their experimentation strategies don’t just “test” – but are more bound to learn, iterate, and drive increased revenue. 

In this article, we’ll break down the roadmap from the first win to becoming an industry “blazer.”

What is Experimentation Maturity? 

Experimentation maturity refers to how developed an organization’s current strategy is in running, analyzing, and learning from experiments. 

This can include their expertise in running things like A/B tests, including its tools, processes, and culture. 

Why is Experimentation Maturity Important?

Experimentation maturity is important as stronger expertise allows for more reliable, faster experiments – which can better aid decision-making.

In addition to an increase in data-informed decisions, more developed experimentation strategies also mean brands continuously improve products, outcomes, and reduce overall business risks.

Step 1: Discover – Find Your First Win

In the first stage of experimentation maturity, a single person or small group is still advocating for experimentation. This is better known as the “Ad Hoc Testing Phase”, which is where experimentation is more reactive than proactive. 

Tests in this phase are typically: 

• Launched on a whim with less strategic planning in mind
• Implement with a hypothesis, but without long-term goals in mind
• Conducted without stable metrics for greater insight
• Using ideas that haven’t been backed by data
• Maybe you are looking for a quick win

During this first step of your brand’s experimentation journey, the platform may be live – but it hasn’t been put to its best use yet. This is similar to someone who has recently purchased a new smart device, but hasn’t set it up to sync with all of their other devices – making their new electronic device less efficient than it could be.

Oftentimes during the discovery phase, there is one “lone champion” trying to fight for greater optimization tactics. This person is challenging those around them to overcome skepticism and testing for the sake of testing, but instead as an effort to create real change. 

Goals for this stage: 

• Achieve one clear, high-visibility win to prove the ROI of a testing program
• Avoid mistaking excessive testing activity for experimentation maturity 
• View it as a stepping stone to ease into the world of optimization

Step 2: Structure – Build Your Blueprint

The second stage of experimentation maturity is when the organization shifts their focus from spontaneous tests to a more reputable, repeatable “rhythm”. 

In turn, this is when a more formal optimization team is usually built and more technical tools are established – such as client-side and initial server-side tools.

This step of a brand’s experimentation maturity usually involves: 

Hypothesis Frameworks

Develop stronger frameworks for creating and validating your experimentation hypotheses.

Defined KPIs

Establish clear primary and secondary Key Performance Indicators to measure success accurately.

Clear Ownership

Define who owns which experiments to ensure accountability and streamline the process.

Starter Roadmap

Create a foundational experimentation roadmap to guide your initial testing efforts and strategy.

Adopted Templates

Ensure templates for experiment design and analysis are widely adopted for consistency.

Team Expansion

Expand usage beyond marketing and CRO teams for company-wide implementation and impact.

This stage of the experimentation journey is where the brand is no longer a complete stranger to the world of optimization, but not yet an expert either. 

Goals for this stage: 

• Transition from “random tests” to a structured roadmap with prioritized hypotheses
• Treat learnings as a more systematic process that can lead to better tests
• Recognize that experimentation isn’t about singular wins, but creating a reliable formula for continuous improvement.

Bolder Tests to Your Better

We learned more about this with Marianna Stjernvall, who has conducted over 500 A/B tests and understands the value of learning from your failures to accomplish future wins. 

With her ample knowledge in CRO, A/B testing, and experimentation – we discover all of the different ways that Marianna came to understand the value in testing, learning, and trying again. 

Listen to the full podcast episode here → 

Step 3: Accelerate – Team Up. Try Something New. 

In the third step of a brand’s experimentation journey, they will start to foster stronger cross-team collaboration. This is generally when experimentation will move beyond the marketing department and start to involve Product and UX.

During this phase, an organization becomes more well-versed in cross-team collaboration – ultimately standardizing the practice as they dive deeper into future experiments and testing. 

Advancing with AB Tasty

Our work with Pion is the perfect example of teaming up to make progress. Through the use of Feature Experimentation & Rollout, Pion was able to benefit from our server-side platform. This helps brands manage various user experiments before the full release, which can allow for both precision and security throughout the testing process. 

Partnering with AB Tasty allowed Pion to:

• Select smarter tests to prioritize experiments that could deliver most valuable information to move forward
• Be bold in their experiments to explore inventive ideas and break routine thinking 
• Develop stronger hypotheses to ensure tests conducted would provide meaningful insights 

Learn more about Pion’s collaboration with AB Tasty here → 

Goals for this stage: 

• Improve upon current technical capabilities such as by leveraging more advanced data 
• Implement the use of more complex experimentation tactics such as heat maps and user recordings 
• Increase testing velocity and finalize a company-wide communication plan for shared learnings
• Ignite interest in using AI and machine learning to develop predictive models and boost personalization 

Step 4: Scale – Testing at the Speed of Business

At this stage of the experimentation journey, optimization is no longer a project – but a core business strategy. In turn, this is usually when brands will begin to develop stronger standards, and maybe even experimentation or personalization “playbooks” to establish company-wide guidelines.

Empowering Speed with PUMA

PUMA and AB Tasty teamed up to accomplish this precise goal: personalization at scale that can bring long-term growth. 

Together, PUMA was able to empower global personalization through: 

• Gender-Based Personalization: PUMA implemented the use of our segment builder to see which products men and women preferred to purchase. This helped with metrics including page views, on-page time, and more. 
• Led Data Lead the Way: Through the use of newfound insights, PUMA guided their strategy to become more information-led. This encouraged more effective experimentation that resulted in increased revenue long-term. 
• Teamwork Makes the Dreamwork: Experimentation isn’t just for CRO teams, but can be used as a brand-wide method to boost optimization. PUMA learned this with AB Tasty, now fostering a company-wide culture where everyone can contribute ideas for new tests.
  

Read the full case study with PUMA here → 

Hallmark characteristics for teams progressing to this stage of experimentation maturity include developing a Center of Excellence (CoE) model and an overall more robust team structure for optimization.

Goals for this stage: 

• Measuring KPIs and additional success metrics to ensure they are aligned with top-level business goals (Revenue per Visitor, CLV, etc.)
• Encourage all departments to not only come up with ideas for tests, but to conduct them safely and independently at scale

Step 5: Blaze – Dare to Go Further

In the highest level of maturity, brands become experts in experimentation – recognizing that this is a pivotal growth and product strategy.

Once this last stage of experimentation maturity has been achieved, every employee can be considered an experimenter. This is when the “HiPPO” (Highest Paid Person’s Opinion) is replaced by data, and choices can be made from insight-led statistics as opposed to emotionally driven business decisions.

Goals for this stage: 

• A cultural shift where failure is viewed as a high-value learning opportunity to test and try again rather than a loss
• Continuous and automated optimization that represents the organization’s DNA
• Dedication toward long-term development of innovative ideas and pursuing bold experimentation 

Conclusion: Your Journey to Better

Every stage of experimentation maturity is a stepping stone to finding your better.

No matter where your brand currently stands on their optimization roadmap, we believe that your next big insight is just one test away. Small steps can create big change, and it all starts with curiosity calling your company’s name. 

Want to see your brand progress through the steps of experimentation maturity, together? 

FAQs

Still have questions about experimentation maturity? Here are the answers you need.

What is Experimentation Maturity?

Experimentation maturity refers to measuring how effectively your organization uses testing to make decisions and encourage company growth. This is usually determined by the level of your various processes, tools, and culture for running experiments consistently and at scale.

How can I know which stage of experimentation maturity my company is at?

You can determine how mature your brand’s experimentation process is by looking at how structured your testing process is, how many teams contribute, and how you use data to make decisions. Comparing these factors to industry benchmarks can help you identify your current stage in experimentation maturity. 

How can my company boost its Experimentation Maturity?

Investing in training, tools, and clear processes can enable teams to run more tests confidently. This can also encourage collaboration across departments and use insights from experiments to guide strategic decisions, which also helps your brand progress through the stages of experimentation maturity. 

About the Author

Angélique de Taddeo

Angélique is Chief of Staff at AB Tasty, where she works closely with leadership on strategic initiatives and cross-functional projects. With a strong interest in product, customer experience, and market trends, she shares insights and perspectives through her articles.

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The New Mandate for Growth

In 2026, growth in digital marketing and web experimentation is no longer contingent on brand-standard-styled CMOs – but their now pivotal, progressive role to be a growth architect. 

CMOs today pioneer the digital direction a company takes, and how that use of marketing is perceived by potential clients and users. This is because the C-suite now expects marketing to deliver quantifiable business results, from revenue to customer lifetime value.

What Does a CMO Do? 

A CMO, otherwise known as a Chief Marketing Officer, is the head of marketing operations in an organization. In turn, other roles in the marketing team will report to the CMO – with the CMO often communicating with other C-level executives in the organization.

CMOs are responsible for developing the planning and execution of all marketing activities within the organization. This means that successful CMOs should learn to be comfortable with growing technology and understanding changing consumer behavior. 

Some of the new tasks CMOs are responsible for in web experimentation include:

Discover new potential AI-driven marketing solutions

Communicate value of product across partners & C-suite

Improve digital marketing efficiency as digital age skyrockets toward AI

Driving company revenue & building brand relationships for future income generation

The Web Experimentation Gap: How CMOs Inspire Teams 

The challenge with web optimization in 2026 is that many experimentation teams are still focused on low-impact “superficial optimization”. 

To drive meaningful growth, CMOs must reset their expectations. Accomplishing this requires demanding that their experimentation teams evolve from tactical testers into strategic partners who can answer their business’s most pressing marketing endeavors and critical questions.

Demand #1: A Shift From Tactical Uplifts to Strategic Impact 

Moving Beyond Conversion Rate Optimization (CRO)

There’s no doubt that CRO is important, but it’s only one piece of the puzzle – as the real goal of web experimentation is Business Experience Optimization (BXO).

BXO, or Business Experience Optimization, refers to the process of seeking to better understand your customers and improve their shopping experience accordingly. 

In turn, CMOs should question how they read their analytics. Experimentation teams often share  how many conversion lifts are on a single page. Instead, CMOs could pursue analytics that reveal how experiments increase their revenue per visitor, reduce customer churn, and improve lifetime value.

Connecting Experiments to Business KPIs

To get real value from experimentation, every test should be tied to a clear business objective. Without that link, teams risk running isolated experiments that generate substantial insights – but little to no meaningful impact. A strong framework ensures that each experiment contributes to a bigger picture, whether that’s driving revenue, improving retention, or increasing customer lifetime value.

This is where concepts like North Star Metrics and OKRs (Objectives and Key Results) are key. A North Star Metric defines the single most important measure of long-term success for your business – such as active users, transactions, or engagement. This provides all experimentation efforts with a unified goal, helping teams prioritize tests that move the metric that matters most.

Meanwhile, OKRs translate that high-level ambition into actionable goals. While objectives define what you want to achieve, key results reveal how success will be measured. When experimentation is aligned with OKRs, each test has an individual motive: to influence a specific outcome. This can make it easier to measure the true impact of your experimentation program.

By tying experiments to both a North Star Metric and structured OKRs, organizations shift from running tests for incremental gains to building a disciplined, outcome-driven experimentation culture.

Here’s an example of how CMOs could inspire new conversations regarding KPIs and OKRs:

Old way: “We increased the click-through rate on the homepage banner by 8%.”

New way: “Our experiment on the homepage banner drove a 4% increase in average order value for first-time visitors. As a result, our models predict we will add $1.2M in incremental revenue this quarter.”

Demand #2: Leverage AI to Answer Bigger Questions, Faster 

It is imperative that CMOs view AI agents as a strategic co-pilot as opposed to an automation tool. This is because in 2026, AI is no longer just a tool for simple tasks – but a valuable, strategic partner for insight discovery and prediction.

Here are three main points CMOs should expect from their experimentation teams with AI: 

Three Core AI-Driven Demands From CMOs

Demand for Predictive Personalization at Scale

CMOs should anticipate teams to use predictive AI to personalize experiences for the 90% of anonymous traffic, not just known customers.

In this case, CMOs could ask their teams to avoid relying on static, rule-based segments. Instead, CMOs should convey to their experimentation teams that it’s better to use AI tools to forecast user intent in real-time and adapt the experience for every visitor, whether they’re logged-in or out. 

Tools like AdaptiveCX can help web experimentation teams to easily implement this exact strategy. This is because AdaptiveCX, cookieless by design, allows brands to personalize according to user preferences on the fly – even for anonymous visitors. 

Demand for Deeper Audience Understanding

As users today are more impatient than ever, it’s crucial for CMOs to employ the concept of emotional and psychological segmentation. This is because to succeed with conversions, it’s key to understand not just what users do, but why they do it.

CMO’s should challenge their teams to go beyond demographics. This includes using AI to reveal the emotional incentives of key audience segments. Tools like EmotionsAI can accomplish this, as it groups visitors into 10 different categories according to their sentimental preferences. 

Demand for AI-Powered Ideation and Analysis

Experimentation teams shouldn’t be limited by their own biases. These predisposition results could be inclusive and fail to lead brands towards their next best test. Luckily, AI can analyze data to generate high-potential hypotheses – which can pave the path for better experimentation, improved conversions, and greater brand loyalty long-term. 

CMOs should be examining AI’s current involvement in how their experimentation teams come up with test ideas. Instead, CMOs could encourage their experimentation teams to explore generating hypotheses with AI that has analyzed our site data, competitor trends, and user feedback.

Demand #3: Build a Privacy-First, Future-Proof Program

In 2026, people are increasingly concerned with third-party cookies and personal information. As the use of these cookies has come to an end, brands that continue to rely on them have hit a strategic dead end. This means brands must find a new method to ensure data privacy for their users.

The Strategic Advantage of Privacy

Privacy doesn’t have to be a constraint, but can be used as a competitive advantage – as it’s a way to build deeper trust with your users. This is paramount for brands that want to cultivate a sense of exclusivity, long-term loyalty, and returning customers with high conversion rates. 

CMOs and their experimentation teams should aim to create a cookie-less personalization strategy with their experimentation teams. Seeking to implement the use of in-session, first-party data to create relevant experiences without compromising user privacy is key to making users feel safe to convert. 

The Right Technology

To make users more comfortable in this new age of data privacy, the right technology needs to be used. Our in-house tool at AB Tasty, AdaptiveCX, can help brands focus on real-time behavior rather than stored personal data. This ensures compliance with regulations like GDPR and CCPA and builds a sustainable foundation for the future.

Demand #4: Foster a Culture of Experimentation, Not Just a Team

One Team, Endless Dreams

Experimentation shouldn’t be the sole responsibility of a small, isolated team – but rather a  shared task across marketing, product, and even sales.

This cross collaboration can allow for new, innovative ideas across the board – contributing to continued growth and more robust experimentation. 

Empower Every Idea

Experimentation teams should strive to go from being the “testers” to being the “enablers” who provide the tools, frameworks, and education for others to test safely and effectively.

In this case, CMOs should be evaluating their team’s plan to increase experimentation velocity across the entire organization. This can include how teams are scaling access to testing and creating a shared repository of learnings that are approachable for everyone.

From Trial to Better

Introduce the concept of an “Experimentation Maturity Model”. 

An experimentation program shouldn’t be static, but something that re-shapes itself in real-time to accommodate for new learnings and discoveries.

This is why CMOs should demand a clear roadmap for this evolution by using an Experimentation Maturity Model – which is a method for organizing the efficiency of how brands run various experiments, such as A/B tests or Multivariate testing. The main goal of an Experimentation Maturity Model is to build an organized experimentation program that delivers real results. 

This framework charts the path from early testing to a fully integrated culture of continuous improvement. This involves the organization progressing from the initial Discover stage, where tests are simple and singular, to the Scale and Blaze stages. 

At these more advanced levels, experimentation becomes an integral pillar for brands. This is because high-velocity testing, cross-team collaboration, and strategic insights contribute to major business decision making. 

This is exactly why CMOs should prioritize a clear path for advancing the organization from a “Discover” stage to a “Scale” or “Blaze” stage, where experimentation is eventually embedded in the company’s core culture. 

The interactive timeline below will break down these different stages: Discover, Scale, and Blaze:

Conclusion: The CMO as the Chief Experimenter

CMOs could spark growth in web experimentation by encouraging their teams to make more daring decisions in testing.

This includes”

• Valuing strategic impact over tactical lifts
• Benefiting from AI-powered insights over automation
• Curating a privacy first foundation
• Creating an enabling a company-wide culture of growth. 

Teamwork makes the dream work

Working smarter instead of harder is key for efficiency. Encouraging your web experimentation teams to take new, innovative paths towards success could prove worthwhile in the end. 

By making these demands, CMOs are not just overseeing a function – but transforming marketing to be the sustainable, customer-centric business model for growth suitable for 2026 and beyond. 

FAQs

Still have questions about CMOs and web experimentation? Here are the answers you need.

What Does a CMO Do?

CMOs are in charge of leading digital marketing and raising brand awareness for a company. In turn, they spend a lot of their time scouting new technology to improve efficiency in digital marketing, cross-coordinating with other teams and C-suite level, and organizing meetings with other brands, both online and in-person, to further promote the company and its product or service.

How Can Web Experimentation Benefit CMOs?

Web experimentation and optimization platforms like AB Tasty can greatly benefit CMOs as it provides them direct insight into how certain catch phrases, CTAs, colors, banners, buttons, and more are performing on the site. Furthermore, they’ll gain a better understanding of what their users respond to – allowing for more efficacious digital optimization, and ultimately improved revenue long-term. 

Are CMOs responsible for seeking AI-driven marketing opportunities?

Yes, CMOs are responsible for scouting out new AI-driven technologies that could help their marketing teams to run more efficiently. This is one of the new, evolving responsibilities of CMOs in the age of artificial intelligence and optimization. 

About the Author

Stephanie Safdie

Stephanie Safdie holds a bachelor’s degree in English Language and Literature from the University of Maryland, specializing in multimedia studies. She has worked as a social media video creator, freelance copywriter, SEO copywriter at Greenly climate tech, and runs a travel blog Destination Dreamer Diaries. 

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