The Past, Present, and Future of Experimentation | Bhavik Patel
Jul 15, 2024
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Bhavik Patel, a strategic planner dedicated to innovation in experimentation, dives into the thrilling future of experimentation. He discusses the evolution of A/B testing and the necessity for thoughtful experimental design. Patel highlights the transformation in marketing, advocating for collaboration between data and marketing teams. He emphasizes the importance of adapting to market changes, accepting failure, and focusing on meaningful results. The conversation also touches on the potential of AI and the balance between incremental and disruptive innovation for a vibrant experimentation culture.
The evolution of experimentation emphasizes the importance of collaborating across disciplines, transitioning from traditional A/B testing to more systematic, data-driven approaches.
Bhavik Patel highlights the critical need for integrating qualitative insights with quantitative data to derive actionable business impact from experimentation outcomes.
Deep dives
The Birth of CRAP Talks
CRAP Talks, founded by Bhavik Patel, emerged from his dissatisfaction with existing industry events that were overly focused on marketing or sales. He wanted to create a platform for data-driven professionals that emphasized collaboration between different disciplines, such as marketing, analytics, and product management. The acronym CRAP stands for Consultants, Researchers, Analysts, and Product professionals, highlighting the importance of diverse voices in the discussions. With a growing membership of over three thousand in London and high approval ratings after each event, CRAP Talks has successfully fostered a community that encourages fresh perspectives and collaboration.
The Evolution of Experimentation
The conversation highlights the evolution of experimentation from its roots in A/B testing towards a broader view of product analytics. Historically, A/B testing practices have been driven by marketing teams looking for quick wins, often leading to poor methodologies like stopping tests prematurely or testing convenience-based metrics. However, there's a shift occurring as data analytics teams begin to take center stage in designing and analyzing experiments, using advanced modeling techniques informed by historical data. This evolution aims to create a more systematic approach to experimentation, focusing on planning and critical thinking before tests are conducted.
The Importance of Statistical Meaningfulness
A key focus of the discussion is the distinction between statistical significance and statistical meaningfulness in A/B testing outcomes. Bhavik emphasizes that while achieving statistically significant results is important, it is equally vital to ensure that these results lead to actionable and meaningful business insights, particularly in terms of revenue impact. The conversation stresses that metrics should not only be viewed numerically but should also incorporate qualitative factors that inform decision-making. By shifting the mindset from seeking only uplifts to understanding the broader implications, organizations can derive more value from their experimentation efforts.
Future Directions in Experimentation with Technology
The future of experimentation is predicted to involve enhanced analytics through data integration and technological advancements, particularly AI. Bhavik suggests that platforms could evolve to operate on a warehouse-centric model, allowing for richer datasets from various sources beyond just A/B testing tools. This model would enhance the analysis of experiments, making it possible to identify more nuanced insights and ultimately refine strategies. He envisions a future where AI not only aids in building and implementing experiments but also continuously iterates and optimizes based on real-time data, further elevating experimentation practices.