Why product UI experimentation is so hard

January 28, 2025ExperimentationBy Juliana Amorim

Let’s be real, optimizing product interfaces is no walk in the park.

You’ve got to ensure every pixel is consistent across the application. You must follow a strict design system (this is non-negotiable). You can't just tweak the DOM if you're working with modern frameworks like React. And don’t get me started on handling all that embedded business logic—it’s a mess.

Yet, no matter how far the product team gets, they always need a developer to make it all happen—one who dreams of focusing on core product development instead of running experiments.

We’ve been there, too. And we learned a lot. Let's dive in.

Why product teams struggle with experimentation

Conversion rate optimization (CRO) and experimentation are critical to improving a product’s performance. Yet, despite their importance, they remain challenging to execute, especially for the product UI.

While marketing teams have developed playbooks for AB testing landing pages and email campaigns, product teams often struggle to apply the same principles to core product experiences. Why is that?

The answer lies in a mix of technical, organizational, and strategic challenges that make product experimentation uniquely challenging. This post explores these challenges and explains why product teams often lack the autonomy to run meaningful experiments.

1. Experimentation requires deep integration

Unlike web pages, which are relatively static, product UIs are dynamic, stateful, and deeply integrated with backend systems.

Running an experiment on a product feature often means dealing with:

• Complex user flows that span multiple screens
• Backend dependencies that require coordination across teams
• Performance concerns that impact load times and interactions
• User state management (e.g., ensuring users see the same variant across devices).

This complexity makes it difficult to implement a simple “split test” without significant engineering effort, making experimentation far less agile than in marketing-led initiatives.

2. Most product UIs are not designed for experimentation

In many organizations, experimentation is an afterthought. When teams build product UIs, they prioritize functionality, usability, and performance—not AB testing or personalization.

As a result, the architecture is often rigid, with hardcoded elements that make it difficult to modify experiences dynamically.

Every experiment becomes a custom engineering effort without a system designed for modularity and dynamic content. This lack of built-in flexibility creates friction, blocking teams from running continuous tests.

3. Engineering bottlenecks slow everything down

Product experimentation often requires engineering support.

Unlike a marketing team that can launch a new landing page test using a no-code tool, product teams must rely on developers to:

• Implement feature flags and experiment variants
• Ensure backend compatibility
• Handle analytics integration
• Deploy changes safely.

It's easy to understand why experimentation always takes a backseat. Engineering teams are often focused on core development priorities, and their reliance on scarce engineering resources makes it nearly impossible for product teams to operate autonomously and scale experimentation programs.

4. Lack of clear ownership between product and growth teams

CRO and experimentation sit in a strange gray area between product management, growth, and engineering. In many companies, it’s unclear who owns the experimentation process.

The product team wants to improve user experience but may not be incentivized to optimize conversion rates. Meanwhile, the growth team lacks direct control over the product UI, and engineers view experimentation as a distraction from core roadmap initiatives.

This ambiguity creates friction, leading to stalled initiatives and missed opportunities for optimization.

5. Product experimentation is always risky

Unlike marketing experiments, where a failed AB test simply results in fewer conversions, product experiments carry real risks. A poorly executed test can:

• Confuse users by delivering inconsistent experiences
• Break critical product functionality
• Introduce performance regressions
• Damage trust if users experience bugs or instability.

These risks make teams hesitant to experiment, especially if they lack proper safeguards. Without confidence in their ability to roll back changes safely, many teams avoid experimentation altogether.

6. Analytics and attribution are harder in product UI

Measuring the impact of an experiment in a product UI is much harder than tracking a click-through rate on a landing page. Product interactions are nuanced, involving:

• Multi-step user journeys
• Engagement metrics that aren’t always tied to conversions
• External factors like seasonality and user intent.

Since attribution is more complex, it’s harder to determine whether an experiment truly worked. This makes it difficult to justify the investment in testing.

7. Most companies lack the right tooling

Traditional AB testing platforms are built for websites and marketing campaigns, not product UIs.

While feature flagging tools exist, they often require developer involvement, which limits the autonomy of product and growth teams. To experiment effectively in product UI, teams need tools that:

• Allow them to modify UI elements dynamically
• Support complex, multi-step user journeys
• Provide detailed analytics tailored to product interactions
• Enable non-technical teams to launch tests without developer support.

Unfortunately, most companies don’t have these tools in place, making experimentation cumbersome and slow.

How to fix it and unlock autonomous product experimentation

Despite these challenges, product experimentation is possible. It just requires a shift in both mindset and infrastructure.

Here’s what companies can do to empower product teams:

1. Invest in experimentation-ready infrastructure

   • Build modular, flexible UIs that allow dynamic content updates
   • Adopt component CMS, feature flagging, and controlled rollout systems
   • Ensure analytics tools can track product interactions effectively.

2. Give product teams more autonomy

   • Reduce dependence on engineering by providing low-code experiment tools
   • Define clear ownership over experimentation within the product organization.

3. Reduce risk with better experimentation practices

   • Implement guardrails like automatic rollbacks and traffic ramp-ups
   • Use progressive delivery to minimize disruption
   • Ensure experiments align with long-term user experience goals.

4. Make experimentation a habit, not an afterthought

   • Encourage a culture where experimentation is expected, not an exception
   • Align experimentation with business objectives, so teams see its value.

Final thoughts

Conversion rate optimization and experimentation are much more complex in product UIs than in marketing campaigns, but they don’t have to be impossible.

The key is removing the technical and organizational barriers that prevent product teams from operating autonomously. By investing in the right infrastructure, ownership structures, and tooling, companies can unlock continuous, high-impact experimentation—without bottlenecking innovation.

Product experimentation should be as seamless as AB testing a landing page. If it’s not, something needs to change.