Many of today's leading companies rely on A/B testing to measure the impact of product changes and remove guesswork from decision-making, allowing teams to make data-driven decisions rather than relying on intuition. Changing from an intuition-based process to an experimentation-driven can be difficult, especially without buy-in from leadership. Getting buy-in from leadership is the single largest determinant for a successful experimentation program. This post outlines a strategic approach to gain buy-in from leadership, starting small and demonstrating measurable impact.
Highlight the Value of A/B Testing
The first step in convincing leadership is to present A/B testing as a tool for continuous improvement rather than an extra burden. Here are a few key points to emphasize:
- Data-Driven Decision Making: A/B testing replaces guesswork with data, allowing the company to make informed decisions based on user behavior and preferences. It’s an objective way to measure the impact of product changes.
- Reduced Risk of Launching Ineffective Features: By testing new features on a small subset of users before rolling them out widely, you minimize the risk of launching something that doesn't resonate with users or hurts performance.
- Impact on Metrics that Matter: Connect A/B testing to the company’s core KPIs (e.g., conversion rates, user retention, revenue growth). Demonstrate how it can directly impact the metrics that leadership cares about most.
Start Small and Show Results
It’s often easier to get buy-in for something new when the initial investment is low. Start by running a few small-scale tests that are low-risk but have the potential for noticeable results. This strategy builds momentum and shows leadership the tangible benefits of A/B testing without requiring a major overhaul of the existing product development process.
Example: Improving Conversions with a Website Change
In one of our recent experiments, we tested a reordering of elements on our Getting Started page. The goal was to see if changing the layout could improve engagement, specifically how many new accounts created an organization after sign-up.
We ran the A/B test for three weeks, comparing the old design to the new one. The result? A 25% increase in the number of accounts that created an organization. More excitingly, those accounts were 200% more likely to convert to paying customers than those with the older layout. This experiment was part of a series of iterative tests, demonstrating how small changes, backed by data, can have a large impact on core business metrics.
Step 1: Identify a Feature or Project
Select a feature or project where the impact is uncertain and where developers are open to testing. Ideally, this is a feature that has not yet started or there is some concern about its impact. Choose a feature that’s tied to an important KPI for the business and gets enough traffic to generate results within 2 weeks.
Step 2: Integrate Testing into the Product Development Workflow
Float the idea of running an A/B test for this project and have the test integrated into the launch plan. Pick the goal metrics and estimate experiment duration, given the power needed to detect the effect you expect. Let the team know that should the experiment fail, you will likely roll back the feature and try again (or move on). The goal is to demonstrate how seamlessly A/B testing can be integrated into the product development workflow, making smarter decisions without slowing the process down.
Step 3: Share Results
Once a few small tests have been completed and shipping decisions have been made based on the results, it's time to communicate this to leadership. Experiment review meetings can help demonstrate that the results, while sometimes counter-intuitive, are valuable. Be sure to communicate:
- The hypothesis behind the test and the variants (with screenshots if applicable).
- The results, including the impact on key metrics.
- What decisions did you make based on this data? (shipped, rolled back, reworked)
- How can these insights inform future product decisions?
Make sure to highlight wins and losses - use the language of 'saves' for features that have a negative impact on metrics. These projects were prioritized, and you might not have realized their negative effects without testing.
Address Leadership Concerns
Leadership may have concerns about adopting A/B testing. Here are some concerns and how to address them:
- Time and Resources: Leaders might worry that A/B testing will slow down product development or require too many resources. Reassure them that, when implemented strategically, A/B testing can streamline decision-making and lead to more effective use of resources by focusing on rapid iterations and MVPs to test that either verify or contradict a hypothesis.
- Concern with Iterative Development: Some leaders may believe that truly innovative products are not created with iterative processes like experimentation-driven development. Counter this by emphasizing that A/B testing allows the company to take calculated risks and learn quickly before investing heavily. There are no projects that cannot be tested in some ways to measure interest, even if they are wildly innovative.
- Cultural Resistance: Sometimes, leadership may resist shifting from intuition-driven decisions to a data-driven culture. In these situations, positioning A/B testing as a tool to enhance rather than replace intuition can help. A/B testing provides a feedback loop that sharpens decision-making.
Build a Culture of Experimentation
Once you’ve successfully demonstrated the value of A/B testing on a small scale, the next step is to foster a culture of experimentation. Encourage leadership to see A/B testing as an ongoing process that fuels innovation and continuous improvement. Over time, teams will become more comfortable using A/B testing to validate decisions and optimize the user experience.
- Make Testing Routine: Incorporate A/B testing into every product development cycle as a natural part of the process.
- Encourage Cross-Department Collaboration: A/B testing should be embraced by product teams and marketing, design, and engineering. When different departments are aligned around experimentation, the results are more impactful.
- Celebrate Wins and Learn from Losses: Recognize successful tests and the insights gained from failed ones. A/B testing is about learning, not just about winning.
Quantifying the ROI of A/B Testing
Leadership often wants a clear financial justification for investing in an experimentation program. Unfortunately, the ROI for experimentation is a complicated number. A/B testing can be used for optimizations or more straightforward A/B tests where the impact of the results is very clear (and definitely communicate those). However, it is hard to determine the ROI of spending 3 months building something that, through an iterative testing program, is more successful than if you had built based on intuition. It can be hard to quantify how much time was saved by not building a feature as well. Also, even failed tests provide value by preventing the launch of potentially harmful features, and allowing your team to learn what your users like. Remind leadership that every test provides insights that can drive smarter decisions in the future. Experimentation programs done well maximize learnings, the effects of which can be hard to put a number on.
Conclusion
Convincing leadership to adopt A/B testing requires a thoughtful, measured approach. By starting small, demonstrating clear impact, and integrating testing into the product development, you can build trust in the methodology. Over time, A/B testing can become an essential part of decision-making, leading to better products and stronger results.
If you’re looking to introduce A/B testing into your company, remember: start simple, stay aligned with business goals, and showcase results, no matter how small. In doing so, you’ll not only convince leadership but also set the foundation for a culture of data-driven innovation.
