Event Details

Registrations to date: 39

Enhancing Business Insights through Geo Testing: The Role of Geolift and Synthetic Control Methods

Geo testing plays a crucial role in assessing the effectiveness of localized business strategies, especially when traditional user-based testing is not feasible. This methodology enables companies to evaluate the success of marketing campaigns, pricing adjustments, or product introductions in specific geographic areas without relying on individual-level data. Tools like Geolift and synthetic control methods provide effective solutions to the complexities associated with geo testing, allowing for reliable and scalable estimations of causal impacts.

Geolift utilizes sophisticated statistical methods and machine learning techniques to discern the effects of geo-targeted initiatives. This is accomplished by contrasting treated geographic regions with a synthetic control group, formed from a carefully weighted selection of untreated areas that reflect the characteristics and trends of the treated regions before the intervention. This process enables businesses to accurately quantify the additional impact of their strategies without depending on user-specific information. The application of synthetic control methods within Geolift is particularly advantageous in contexts where privacy issues or limited data availability hinder user-based testing. By concentrating on aggregated geographic data, these methods uphold the integrity of the analysis while adhering to privacy regulations. This strategy not only enhances the capacity to extract actionable insights from geo testing but also facilitates the flexible adjustment of testing parameters in response to changing market dynamics.

About Our Speaker

Sanjay M. Tamrakar is a results-driven lead data scientist with over seven years of experience in the field. Holding a master’s degree in Statistics from Miami University, Ohio, Sanjay combines a robust foundation in statistical theory and experimental design with practical expertise in data analysis. Sanjay excels at leveraging advanced analytics and machine learning techniques to drive data-driven decision-making and deliver actionable insights. His technical skill set includes proficiency in R, Python, SQL, and cloud-computing platforms such as Amazon AWS. Throughout his career, Sanjay has held significant roles, including Lead Data Scientist and Senior Data Scientist at Safelite, where he has led projects that deliver impactful insights and drive business growth. His work involves statistical modeling, hypothesis testing, predictive modeling, and machine learning algorithms to solve complex business challenges. Sanjay is also an active volunteer, having received the President’s Volunteer Service Award – GOLD in May 2024, and he is dedicated to mentoring aspiring data scientists and supporting community initiatives.

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