The DXP Catalyst Update - Apr 23, 2025

INTRO
Welcome to This Week’s DXP Catalyst Update

This edition of the DXP Catalyst Update explores a topic that challenges even the most digitally mature organizations: closing the gap between the promise of experimentation platforms and the reality of how they’re used. While both DXPs and independent vendors offer powerful experimentation capabilities, companies often struggle to embed a true test-and-learn culture that drives real business impact.

We break down why that gap exists and what high-performing teams do differently.

LEADERSHIP GUIDANCE
The Experimentation Gap: What’s Holding Back Test-and-Learn Culture?

Many enterprise organizations have made investments in experimentation and personalization tools over the last several years. These platforms are positioned as enablers of data-driven decision-making, continuous learning, and measurable improvements to the customer experience.

However, after onboarding and initial enablement, many teams find themselves facing a different reality. Experimentation is limited to a few A/B tests, mostly superficial in nature. Results are inconsistent, shared sporadically, and often disconnected from broader business goals. In many cases, experimentation fails to become a repeatable, embedded practice. Instead, teams revert to familiar habits, making decisions based on instinct, opinion, or internal politics.

The experimentation gap refers to the difference between what these platforms are designed to deliver and how they are actually used in practice. The root cause is not a limitation of the technology, but a breakdown in ownership, process, and culture.

The Strategic Promise of Experimentation

Experimentation is not simply a way to test headlines or button colors. In a strategic context, it is a method for reducing risk, accelerating learning, and continuously optimizing digital experiences. Organizations often invest in these platforms with the expectation that they will:

• Support data-driven decision-making across teams

• Improve personalization and audience targeting

• Provide measurable insights into customer behavior

• Encourage a culture of iteration and continuous improvement

These outcomes are achievable. However, reaching them requires more than just buying a platform. It requires embedding experimentation into how decisions are made and how success is measured across the business.

Where Most Organizations Fall Short

Despite the potential, many companies struggle to operationalize experimentation. Here are six of the most common barriers that prevent organizations from realizing the full value of their investment.

1. Lack of Clear Ownership

Experimentation often exists in a gray area between marketing, product, IT, and analytics teams. When no single person or team is responsible for driving the strategy, maintaining momentum, and enabling usage, experimentation remains an afterthought. Without ownership, consistency breaks down, and the platform fails to evolve into a shared capability.

2. Limited Executive Support

Experimentation programs that lack visible leadership support rarely gain traction. Teams need to see that executives value testing as a means of improving decision-making, not just as a tactical exercise. Without leadership signaling its importance, experimentation is deprioritized in favor of short-term deadlines or stakeholder preferences.

3. Insufficient Data Infrastructure

Experiments are only as good as the data that powers them. If the organization lacks unified audience data, clear segmentation strategies, or robust tracking, even simple experiments become difficult to design and interpret. Many platforms depend on integrations with CDPs, analytics platforms, and content systems. Without those connections, the experimentation platform cannot function at full capacity.

4. Low Platform Enablement

Even mature platforms will underperform if teams are not trained and empowered to use them. In many cases, only one or two users know how to configure experiments, and those individuals become bottlenecks. If platform usage is overly technical, or if documentation is lacking, teams will avoid engaging with the tool altogether.

5. No Prioritization Framework

In the absence of a clear process for identifying, prioritizing, and tracking tests, experimentation efforts remain reactive. Teams may run a handful of one-off experiments with limited business impact, often without defining hypotheses or success criteria. Over time, this leads to skepticism about the value of experimentation and discourages further investment.

6. Resistance to Failure

In organizations where failure is punished or avoided, experimentation cannot thrive. Many meaningful experiments will produce negative or inconclusive results. These outcomes should not be viewed as wasted effort as they can provide valuable learning that can inform future strategies. However, without leadership that normalizes failed tests, teams will avoid taking risks. Experimentation becomes limited to safe, low-impact ideas that do not challenge assumptions or drive real insight.

What High-Performing Teams Do Differently

In organizations where experimentation is embedded into the culture, the difference is clear. These teams treat experimentation as a core operating principle rather than a peripheral activity. They exhibit the following characteristics:

• A centralized or distributed ownership model that ensures accountability

• A structured backlog of prioritized hypotheses tied to business goals

• Clearly defined metrics and success criteria for each experiment

• A regular cadence for sharing results and refining hypotheses

• Integration of experimentation into campaign planning, personalization strategies, and product roadmaps

• Leadership that supports learning, rewards initiative, and embraces failure as part of the process

Importantly, these organizations do not rely on a single team to do all the work. They enable contributors across departments to run tests, interpret results, and make data-informed decisions. They document experiments systematically and share both successes and failures widely.

How To Start Closing the Gap

If your organization has invested in an experimentation platform but is not seeing meaningful impact, the path forward does not require a complete reset. A few focused actions can build the foundation for long-term success:

• Assign a dedicated owner. Ensure someone is accountable for experimentation strategy, enablement, and performance. This may be a platform lead, marketing ops manager, or a cross-functional product owner.

• Start with manageable goals. Commit to running one well-scoped experiment each month. Focus on consistency before expanding volume.

• Create psychological safety. Encourage teams to view experimentation as a mechanism for learning, not as a pass/fail test of competence. Normalize failure as part of the process.

• Integrate testing into planning. Include experimentation in campaign briefs, content calendars, and sprint cycles. Avoid treating it as a separate or optional track.

• Establish a reporting structure. Use simple templates or dashboards to track hypotheses, setup, results, and lessons learned. Share findings across teams to maximize value.

Final Thoughts

Experimentation is not a technical capability. It’s a strategic discipline that requires the right combination of process, enablement, leadership, and culture. The platform you choose is only part of the equation. Without the organizational conditions that support continuous learning, no experimentation tool will deliver on its promise.

Closing the experimentation gap does not require a new vendor or a massive reorganization. It requires a shift in how decisions are made and how teams are empowered to challenge assumptions, test ideas, and learn from outcomes. Organizations that make this shift will be positioned not only to maximize the value of their platform investments, but also to build a more resilient, data-driven operating model.