Article

5min read

Failing Forward for Experimentation Success | Shiva Manjunath

Shiva Manjunath shares how debunking best practices, embracing failure, and fostering a culture of learning can elevate experimentation to new heights.

In this episode of The 1000 Experiments Club, guest host and AB Tasty’s Head of Growth Marketing UK, John Hughes, sat down with Shiva Manjunath, Senior Web Product Manager of CRO at Motive and Host of the podcast From A to B. Shiva’s journey through roles at Gartner, Norwegian Cruise Line, Speero, Edible, and now Motive, has made him a passionate advocate for the transformative power of experimentation.

During their conversation, Shiva discussed the pitfalls of following “best practices” blindly, the importance of creating an environment where failure is seen as a step toward success, and how companies can truly build a culture of experimentation.

Here are some of the key takeaways.

The myth of ‘Best Practices’

Too often, the so-called experimentation best practices become a checkbox exercise, rather than a thoughtful strategy.

“If you’re focused on best practices, you’re likely missing the point of true optimization,” Shiva notes. 

He recounted a situation at Gartner where simplifying a form—typically hailed as a best practice—actually led to a sharp drop in conversions. His point? Understanding user motivation and context is far more important than relying on one-size-fits-all rules. It’s this deeper, more nuanced approach to experimentation that drives real results.

“If what you believe is this best practice checklist nonsense, all CRO is just a checklist of tasks to do on your site. And that’s so incorrect,” Shiva emphasized, urging practitioners to move beyond surface-level tactics and truly understand their audience.

Embracing failure in experimentation

A major theme of the discussion was the pivotal role failure plays in the journey to success. Shiva was candid about his early experiments, admitting that many didn’t go as planned. But these “failures” were crucial stepping stones in his development.

“My first ten tests were all terrible. They all sucked,” Shiva admitted, underscoring that even the most seasoned experts start with mistakes. He stressed that organizations must create an environment where employees can experiment freely, learn from their mistakes, and continue to improve.

“If you’re penalized for running a losing test, you’re not in a culture of experimentation,” Shiva insists.

Organizations that punish failure are stifling innovation. Instead, Shiva advocates for an environment where employees can test, learn, and iterate without fear. “The idea that you have the flexibility to discuss failures and focus on, ‘Well, I ran this test. It lost. Now, what do we do next?’—that’s a culture of experimentation.”

Scaling experimentation maturity

Shiva also explored the varying levels of experimentation maturity within organizations. Many companies claim to have a “culture of experimentation,” but few truly practice it at scale. Shiva emphasized the importance of making experimentation accessible to everyone in the organization, not just a select few.

Reflecting on the loss of Google Optimize, Shiva acknowledged its role as a gateway into the world of experimentation. “I got into experimentation through Google Optimize,” Shiva recalled, recognizing the tool’s importance in lowering the barrier to entry for newcomers. He urged companies to lower barriers to entry and enable more people to engage with experimentation, thereby fostering a more mature and widespread culture of testing.

The role of curiosity and data in experimentation

Another critical point Shiva raised was the importance of curiosity in experimentation. He believes that genuine curiosity drives the desire to ask “why” and dig deeper into user behavior, which is essential for effective experimentation.

“If you’re not genuinely curious about the why behind many things, I don’t know if experimentation is the field for you,” Shiva stated, underscoring curiosity as a crucial soft skill in the field.

Shiva also highlighted the foundational role of being data-driven in any experimentation strategy. However, he cautioned that having data isn’t enough—it must be effectively used to drive decisions.

“If you’re in a business setting and the business looks at your program and this is zero test wins, right? And then after two years, they would rightfully say ‘is this the way it’s supposed to go?’” Shiva remarked, pointing out that data-driven decisions are key to sustaining a culture of experimentation.

What else can you learn from our conversation with Shiva Manjunath?

  • Why it’s crucial to critically evaluate industry buzzwords and ensure they align with real practices.
  • How true personalization in experimentation goes beyond just adding a user’s name.
  • The need for thorough analysis to genuinely support data-driven decisions.
  • Shiva’s take on the future of experimentation after Google Optimize and how companies can adapt.

About Shiva Manjunath

Shiva Manjunath is the Senior Web Product Manager of CRO at Motive and Host of the podcast From A to B. His insatiable curiosity about user behavior and deep passion for digital marketing have made him a standout in the world of experimentation. With experience at top companies like Gartner, Norwegian Cruise Line, and Edible, Shiva is dedicated to demystifying CRO and pushing the boundaries of what’s possible in the field.

About 1,000 Experiments Club

The 1,000 Experiments Club is an AB Tasty-produced podcast hosted by Marylin Montoya, AB Tasty CMO. Join Marylin and the Marketing team as they sit down with the most knowledgeable experts in the world of experimentation to uncover their insights on what it takes to build and run successful experimentation programs.

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Article

6min read

Unify GA4 with BigQuery to Strengthen Experiments

In today’s digital landscape, data-driven choices are essential for staying competitive, with experimentation as a critical driver of innovation. To support this, we recently hosted a webinar with experts from Google Cloud and AdSwerve, focusing on how Google Analytics 4 (GA4) and BigQuery can enhance experimentation strategies. GA4 is essential for all marketing teams, providing advanced analytics that, when combined with BigQuery’s data consolidation capabilities, enables more effective testing, personalization, and digital optimization.

Meet the panel

Taige Eoff, Cloud Data AI Lead at Google, has been at Google for twelve years, leading data and AI initiatives for cloud marketing. Taige focuses on developing scalable solutions that support partners like AB Tasty and AdSwerve in optimizing digital experiences.

Alex Smolin, Senior Optimization Manager at AdSwerve, brings extensive experience in media, data, and technology. As a certified Google Premium Partner, AdSwerve provides data-driven brands with solutions ranging from A/B testing to advanced analytics.

Mary Kate, our roundtable host and Head of Growth Marketing for North America at AB Tasty, leads efforts to help companies create impactful digital experiences through AB Tasty’s suite of experimentation, personalization, product recommendations, and site search tools.

AB Tasty’s integration with GA4 & BigQuery

Connecting AB Tasty with GA4 gives marketing teams insights into visitor behavior through advanced analytics on CPA, conversion rate, bounce rate, SEO, and traffic. This integration allows teams to use data from either tool to measure the impact of experiments pre- and post-rollout, generating data-backed hypotheses and fostering innovation.

Google BigQuery, a fully managed cloud data warehouse solution, offers rapid data storage and analysis at scale. With its serverless, cost-effective structure, BigQuery allows businesses to analyze large datasets efficiently, making it easier to make well-informed decisions.

With Google BigQuery, users can effortlessly execute complex analytical SQL queries and leverage built-in machine-learning capabilities.

Why is data from GA4 foundational to any CRO program?

In experimentation, data is the catalyst that drives actionable insights. Data flows in from multiple sources, and businesses generate detailed reports by working with partners to integrate tracking and tagging. But the question then becomes: what comes next? That’s where experimentation enters. Using data from tools like GA4, teams can transform hypotheses into tests, uncovering which changes impact user engagement or conversions most effectively.

GA4’s role extends further by providing a consistent framework for testing across platforms. When integrated with BigQuery, GA4 allows teams to cross-reference test outcomes with other data points, revealing not just what worked but why it worked. As Alex noted, “We gather good data, run good tests, and then verify results across disparate sources like BigQuery to see if what we tested had the expected downstream impact.”

Data accessibility and agility are also important. Trends evolve quickly, with viral content or market shifts requiring rapid adaptability. “Having partners like Google, with all data in one place, and a platform like AB Tasty, where experiments can be quickly set up, is essential for staying competitive” Alex emphasized.

“Having partners like Google, with all data in one place, and a platform like AB Tasty, where experiments can be quickly set up, is essential for staying competitive.”

Alex Smolin, Senior Manager Optimization at Adswerve

How BigQuery powers scalable experimentation

With the growing volume of data, businesses need a way to consolidate and interpret it to drive impactful decisions. BigQuery, as Taige explained, is a robust cloud warehouse that streamlines data for meaningful insights, making it a key player in the experimentation ecosystem.

“Think of BigQuery as a filing cabinet for your organized data,” Taige noted. By consolidating disparate data sources, teams can create a unified view that informs testing and optimization efforts. Through this approach, tools like GA4 and BigQuery enable accurate decision-making that scales with the business. With BigQuery as the backbone, AB Tasty and AdSwerve can build on this structure to optimize user experiences through precise experimentation.

Beyond just data storage, BigQuery integrates with various Google Cloud tools and supports a wide range of use cases—from standard reporting to advanced machine learning. For marketers, this means fewer technical bottlenecks and quicker access to the data needed to stay agile. As Taige explained, “You may not need deep technical skills to access BigQuery’s benefits; the right partnerships and data structure can give you a powerful, accessible foundation.”

Leveraging BigQuery’s built-in AI and machine learning models

BigQuery offers an array of AI models for specific use cases—from translation and personalization to customer segmentation. These models add value by automating processes, such as localization or customer behavior prediction, allowing for smoother, more targeted marketing.

BigQuery’s flexibility means that companies can incorporate custom or third-party models, ensuring compatibility with a variety of AI solutions. This adaptability helps organizations innovate and iterate on experimentation programs, expanding what they can achieve with data.

Simplifying data access for marketing efficiency

For marketing teams, BigQuery’s role as a centralized data hub allows seamless data consolidation from platforms like Google Ads, Salesforce, and GA4. This integration ensures that marketers aren’t slowed down by fragmented data sources, freeing them to focus on insights and execution. As Taige highlighted, “The peace of mind that BigQuery provides comes from knowing that all data is consolidated and accessible, allowing teams to be nimble and creative.”

With BigQuery, marketers can view performance metrics, analyze customer journeys, and refine strategies—all within a unified environment. This lets teams optimize campaigns in real time as new data insights emerge.

Next-Generation capabilities enabled by Google Cloud

Looking ahead, digital  is paving the way for more advanced experimentation capabilities.  The conversation shifts to AI and machine learning, bringing new opportunities for personalization and optimization. As Mary Kate pointed out, while AI-driven insights can revolutionize customer experiences, many brands are still years away from realizing the full potential of these tools.

True value will come not from adopting every new tool but from understanding the foundational data supporting AI and asking the right questions about how these technologies can serve customer needs. Taige added, “If you don’t have a data strategy, you won’t have an AI strategy.” While AI amplifies data power, it requires organized, high-quality data to work effectively.

By consolidating and centralizing data through BigQuery, teams gain real-time insights and can make informed decisions. This data foundation enables the current wave of omnichannel strategies and sets the stage for future AI applications. Businesses that adopt this holistic approach—consolidating data, optimizing channels, and preparing teams for AI—will unlock new experimentation opportunities and drive impactful customer experiences.

With GA4 and BigQuery, businesses have the tools to streamline data consolidation and power next-generation experimentation. Ready to join your data and experimentation? Discover how AB Tasty can help bring data-driven optimization to life.