Cases and Engagements

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Managing Director

Ph.D. in Economics, Northeastern University

B.A. in Economics, University of California at Los Angeles

Econ One, January 2018 – Present

UCLA Department of Sociology, 2015 – Presen

Independent Economic Consultant, 2015 – 2018

EY (Formerly Ernst & Young), Transfer Pricing, 2013 – 2014, Advisory Services, 2014 – 2015

UCLA for Int’l Science, Technology, and Cultural Policy, 2005 – 2017

Northeastern University, 2007 – 2009

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May 1, 2024

Sample Estimation and Survey Creation for a Major Agricultural Firm

In 2021, Econ One was engaged by a major agricultural firm to enhance their long-term investment planning strategies. The firm aimed to optimize plant varietals based on customer satisfaction with fruit variants. Dr Amarita Natt who is an expert in developing advanced statistical models using client data to optimize business outcomes and inform decision-making, and her team spearheaded the development of a predictive model capable of assessing customer satisfaction by analyzing fruit characteristics such as soil type, field location, storage and transport characteristics, and other relevant farming and supply chain elements. Given the complexity of the model with hundreds of variables, Dr. Natt’s team conducted a thorough analysis to determine the required number of customer satisfaction survey responses for statistical validity and predictive power.

Adapting standard methodologies for calculating observation requirements to accommodate the extensive variable range, the team identified a range of potential observations to support various modelling scenarios. Leveraging their expertise in loyalty marketing, they collaborated with the marketing department to craft survey questions aimed at maximizing information while minimizing user effort. Furthermore, they organized the questions strategically to ensure critical models could be executed, even if users did not complete the entire survey—a common issue in survey research.

Throughout the engagement, Dr Natt’s team elucidated to stakeholders the potential applications of statistical modelling in long-term capital investment, operations, and distribution decisions. By highlighting the broader utility of data and analytics beyond basic business intelligence, they provided stakeholders with a comprehensive understanding of how these tools could drive strategic decision-making in the agricultural sector.

Industries: Agriculture

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