5 Trends Shaping the Future of A/B Testing and Experimentation

Discover how A/B testing is evolving and transforming digital product strategies due to new technological developments.

February 28, 2024
Image of Ken Kutyn
Ken Kutyn
Senior Solutions Consultant, Amplitude
Abstract image of A/B testing different layouts

A/B testing in digital product management is like a chess game. Teams must be calculated, precise, and always a few steps ahead. It’s a strategic process where combining data with intuition leads to more successful outcomes—and it’s constantly evolving.

Several key trends are emerging, painting a picture of a future where experimentation is unified across product, engineering, and marketing teams to build better digital experiences that are data-driven and user-centric.

The following explores five key trends in experimentation and how technology advancements, like artificial intelligence (AI), are changing A/B testing. Also, get insights into adapting your team’s approach and investing in tech to stay ahead of digital product innovation.

Key takeaways
  • Non-technical teams will be more involved in experimentation with help from developers.
  • Companies are breaking down silos between marketing and product teams.
  • You can expect more investments in and attention to statistics, enabling teams to better understand the results of their experiments
  • Teams will continue using AI in experimentation while acknowledging its limitations.
  • The merging of analytics solutions and data warehouses significantly changes how experimentation is done.

1. Less developer (and more non-tech team) involvement

The role of developers in experimentation has always been cyclical. Historically, engineering teams led experimentation efforts, but around 2010, the rise of visual editors enabled non-technical marketing teams to play a more significant role in testing.

In 2017, issues related to web cookies, performance challenges, and the desire to apply experimentation in broader scenarios brought developers back into the fold. Product teams, as well as marketing teams equipped with coding skills, began to retake the helm.

Now, developer roles are shifting once more. Nearly every marketing automation, content management system (CMS), and customer relationship management (CRM) platform has some experimentation and personalization capabilities built in. Looking ahead at A/B testing, key trends are emerging that shift experimentation back to marketers and lessen the demand on developers:

  • Increased automation: The growing use of automation can streamline the testing process, reducing some of the manual effort that goes into experimentation.
  • Enhanced usability: There’s a continued focus on developing user-friendly interfaces with intuitive design elements that simplify complex testing scenarios.
  • Enhanced collaboration across teams and workflows: Emerging platforms that improve collaboration between technical and non-technical teams help increase learning velocity and facilitate sharing of insights and results.
  • Open platforms and integration: Previously, if non-technical teams needed to experiment, they had to use the A/B testing features built into their marketing tools (think CMS, orchestration, messaging, ads, etc.). However, because these capabilities were outside the core competency of most marketing point solutions, these features were often simplistic. The result? Teams leaned on developers to build experiments in a more robust product experimentation solution. Now, best-in-class experimentation programs bring marketing and product teams together to scale experimentation with more open and extensible solutions to meet both teams’ needs.

These developments have opened doors for experimentation without heavy developer involvement. Even those without extensive programming skills can engage more directly in experimentation.

2. Convergence of product and marketing

Marketing and product teams are merging and working more closely than ever in the context of experimentation and beyond, mirroring the evolution in analytics platforms. Amplitude has been at the forefront of this convergence, offering analytics that encompasses product and marketing capabilities to quickly understand the entire customer journey.

In experimentation, this means testing use cases extending across various stages of the user journey, from initial engagement on landing pages to sign-up, activation, engagement, retention, and even upsell opportunities—all within a single platform. This approach enables a more comprehensive understanding of the user experience, as it no longer segregates insights based on whether the user is interacting with marketing content or product features.

As Amplitude Vice President of Product, Partner, and Customer Marketing Courtney Burry points out: “Optimizing your customer journey requires a deep understanding of what customers are doing and where they’re facing challenges. It requires diving into every customer interaction across every channel, platform, and touchpoint.” Simply put, product and marketing teams must work together to truly deliver exceptional digital experiences—no matter where customers are.

3. Growing emphasis on statistical savviness in experimentation

There's an ongoing debate about how well-versed product managers and marketers need to be in statistics to run their own experiments. On the one hand, software vendors are simplifying test interpretation and minimizing the need for product and marketing teams to dive deep into statistical calculations. On the other hand, industry veterans like Ron Kohavi raise concerns about the misunderstanding of results due to over-reliance on auto-generated reports.

This tug of war points toward a few emerging trends in the future of A/B testing:

  • Using advanced statistical tools: Teams will likely adopt more sophisticated statistical tools to complement their existing software. These tools will provide a more comprehensive analysis of experiment data, enabling a clearer understanding of the results.
  • Investment in statistical training: Expect organizations to invest in statistical training for their teams. This training will empower team members to understand the nuances of A/B testing and make more informed decisions based on the data.
  • Hiring for statistical expertise: The growing demand for deeper statistical knowledge in experimentation may lead to a surge in hiring professionals with strong backgrounds in statistics.

The increasing focus on statistical understanding is crucial for designing, interpreting, and acting on experiments. Leading companies now aim for a practical approach, providing all users with clear, consistent data while enabling deeper insights beyond the basics.

4. Cautious use of AI in experimentation

AI’s capabilities in automating various aspects of experimentation are undeniable, from generating messaging copy to qualifying and analyzing data. These advancements are enabling teams to conduct more experiments with greater efficiency.

The primary use case for AI in experimentation is quickly generating user experience variants. However, it’s essential to acknowledge the limitations that come with AI in this context. The main drawbacks include poor quality of experiences and sample sizes that are too small to be statistically significant.

When you use AI for tasks like content creation and data qualification, try to maintain a balanced approach, using both the speed of AI and your team’s expertise. This thoughtful approach uses AI’s advantages while ensuring accurate, reliable, and useful results.

5. Advancements in warehouse-native A/B testing

The integration of analytics solutions and data warehouses is reshaping how organizations conduct experiments and bringing several key advancements to A/B testing experiments:

  • Richer targeting: With more comprehensive access to data, experimentation can become highly targeted. The ability to segment and test specific user groups based on more detailed data profiles leads to more effective and personalized experiments.
  • Better alignment across teams: A unified data source reduces data discrepancies between different teams, such as product and business intelligence (BI) teams. This helps ensure consistency in experiment design and analysis, driving better collaboration and decision-making.
  • Quicker experimentation cycles: Accessing tagged events and data points in one place accelerates experimentation. Teams can rapidly iterate and adapt experiments based on real-time data insights.

Despite these promising developments, there are still some unaddressed challenges. Integrating warehouse data to support functionalities like sticky bucketing, real-time targeting, and identity resolution is still a work in progress. As experimentation platforms improve, they’ll likely have features that work well with data warehouses’ functions and structures.

By leaning into these trends, your team can achieve more precise targeting, deeper user insights, and efficient decision-making—ultimately leading to products your customers value and use regularly.

As experimentation and A/B testing evolve, partnering with vendors at the forefront of new trends is crucial. Amplitude Experiment enables you to effectively test, analyze, and enhance your product on a large scale.

Read our comprehensive guide on using Amplitude for effective A/B testing and unlock the full potential of your experimentation strategies.

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About the Author
Image of Ken Kutyn
Ken Kutyn
Senior Solutions Consultant, Amplitude
Ken has 8 years experience in the analytics, experimentation, and personalization space. Originally from Vancouver Canada, he has lived and worked in London, Amsterdam, and San Francisco and is now based in Singapore. Ken has a passion for experimentation and data-driven decision-making and has spoken at several product development conferences. In his free time, he likes to travel around South East Asia with his family, bake bread, and explore the Singapore food scene.