Introduction
Karthik Mallapragada Gurunadha is a dynamic force in the world of data-driven marketing and retail analytics. With a career spanning over 7 years at AB InBev, the world’s largest brewer, Karthik has been at the forefront of revolutionizing marketing analytics and retail media measurement. From pioneering in-house eRetail Media measurement to driving media performance improvements worth over $1 million, Karthik’s journey is a testament to the power of actionable insights in the modern business landscape. As we dive into our conversation with this skilled analytics professional, we’re excited to uncover the strategies, challenges, and visions that have shaped his impressive career. Let’s explore the intersection of data, marketing, and innovation with one of the industry’s rising stars.
How has your experience as a founding member of AB InBev’s Marketing Analytics team shaped your approach to data-driven decision making in the beverage industry?
One of the biggest takeaways from joining a startup-like environment within a global company is that adoption is the ultimate indicator of success for an analytics team. It doesn’t matter how robust the backend algorithms are if the outputs aren’t used. Building trust with stakeholders and showing them that data-driven insights are designed to augment their experience, not replace it, is critical. Education and evidence play a pivotal role in this. It starts with understanding your business partner’s challenges, priorities, and focus areas, while also helping them see the value analytics brings. This sets a foundation for rapport and engagement. However, sharing results isn’t enough; actionable insights that track adoption over time are key to building trust, accountability, and transparency on both sides. Additionally, putting analytics in the hands of the business—whether through web tools, dashboards, or apps—makes data-driven decision-making an integral part of the organization’s workflow.
Can you walk us through the process of pioneering the eCommerce Media measurement solution for AB InBev? What were the main challenges and how did you overcome them?
During the pandemic, the surge in online beer sales created a need for greater eCommerce investment. However, one of the main challenges was the non-standardized media performance data provided by retailers. Varying granularities, frequencies, and measurement methods meant there was no consistent way to compare media performance across retailers and markets. This led us to develop an in-house standardized measurement solution.
Two major challenges emerged: finding a reliable, scalable measurement method and acquiring media data. To solve the data issue, I worked closely with account managers, showing them the roadmap and potential insights, which secured their buy-in for manual data tracking where retailers wouldn’t or couldn’t share data. For the methodology, my team leveraged prior experience and tested various hypotheses before settling on an MMM-based approach. Customizing it for our business required further trial and error, and working closely with account managers allowed us to refine the final reports to ensure they provided actionable insights.
You’ve led measurement for over 100 brands across 25+ retailers and 10+ markets. How do you manage the complexity of such diverse data sets and ensure consistency in your analyses?
Standardizing datasets is crucial for maintaining consistency, though it often requires working closely with local data teams to ensure smooth data integration. This is a long process, and variations in data, such as sales being reported monthly in one market and weekly in another, need to be accounted for in scalable solutions.
Consistency in analysis comes down to rigor and frameworks. Grouping questions at both global and local levels ensures the focus remains on addressing the right issues with the appropriate data. Tracking the implementation of recommendations and ensuring we don’t contradict prior analyses, unless justified, also contributes to analytical consistency.
In your role as a Product Manager for Retail Media Measurement, how do you balance the technical aspects of analytics with the business needs of stakeholders?
It’s crucial to continuously ask what business problem needs solving. Often, some requests aren’t immediately solvable due to data limitations or time constraints. In such cases, focusing on marginal gains is essential. If a fully technical solution isn’t feasible, directional insights through lighter analyses or research can still add value. This iterative improvement often leads to more robust models later on. Adopting this consultant-like mindset ensures the analytics team can deliver useful insights even without perfect data, and it helps secure business buy-in for more technically advanced solutions over time.
You’ve mentioned driving media performance improvement of over $1M through actionable insights. Can you share a specific example of how you translated data into tangible business value?
A recent example involved a global brand in a major UK-based retailer, where the eCommerce investment focused on a particular SKU. Through analytics, I identified a similar SKU that had the potential to drive higher ROI for a similar media investment. We shifted media spend to the new SKU, and the return on investment doubled compared to the same period the previous year.
In another instance, I discovered a geography with low ROI due to a disproportionate investment in a particular ad format. Working with the media teams to adjust ad formats and buying objectives led to a 20% improvement in ROI across multiple markets.
As you’re building the retail analytics ecosystem for AB InBev, what emerging technologies or methodologies are you most excited about incorporating?
AI is an exciting space, particularly in scaling insights generation. Instead of manually investigating data, AI can accelerate the process and improve the quality of insights. I’m also interested in better understanding the interaction between online and offline channels in retail. Capturing phenomena like ROPO (Research Online, Purchase Offline) presents challenges but could yield powerful insights.
How has your experience across different roles at AB InBev – from Business Analyst to Product Manager – influenced your understanding of the end-to-end analytics process?
Each role has provided different insights into the elements that drive successful analytics. Early on, as a Business Analyst, my focus was on building solutions and reporting to stakeholders. Over time, the emphasis shifted to understanding the business impact of those solutions. I realized that technical teams often focus on technology for its own sake, without fully grasping the “why” behind their work. Ensuring that everyone understands the purpose behind what they’re building leads to better analytics products—ones that people actually want to use in their day-to-day operations.
With your background in Market Mix Modeling, how do you see this technique evolving in the age of digital marketing and real-time data?
I anticipate a shift toward quicker, more agile MMMs. Traditionally, these models are data-heavy and run every 3-6 months, but the frequency will likely increase. Bayesian MMMs could offer a solution here. Additionally, MMMs will serve as foundational models, augmenting other analyses such as digital attribution, price elasticity, and logistics optimization.
You’ve worked across multiple geographies, including the EU and South America. How do you adapt your analytics approach to account for regional differences in consumer behavior and market dynamics?
Every region and brand is unique, particularly in the alcohol industry, where cultural outlooks and regulations are significant factors. Our approach involves building scalable solutions that allow for local customization. We first focus on understanding the legal and cultural restrictions surrounding alcohol sales and marketing. Next, we identify regional consumption patterns, such as Dry January in the UK or spikes in sales around the Super Bowl in the US. Finally, building relationships with local teams is essential. Understanding their challenges and communication preferences ensures a more effective partnership. For instance, I’ve learned to adjust my communication style depending on the region—speaking more slowly and using simpler English when working with non-native speakers in South America versus Europe.
Looking ahead, what do you believe will be the next big challenge or opportunity in retail analytics for the beverage industry, and how are you preparing to address it?
Maximizing shopper occasions is a critical focus for the industry. Identifying the moments when consumers are most likely to purchase beverages is key. Additionally, omnichannel consumer behavior—how online and offline interactions influence purchasing decisions—holds significant potential. We’re working to expand our analytics solutions to better capture these behaviors, giving us a head start in understanding and optimizing for these shopper occasions.
