Featured Investors
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January 1, 2021

Featured Investors | January 2021

By

Michelle Moon, Principal at LG Tech Ventures

Michelle Moon is a Principal at LG Technology Ventures, an early stage corporate venture firm. Michelle spends her time investing across multiple industries, especially AI/ML, enterprise software, healthcare and consumer. Michelle’s latest investments include: H2O.ai (autoML), Amwell (telehealth), OTI Lumionics (material discovery) and Syte (visual AI). Michelle has been in the investment industry since 2011, and worked in investment banking and private equity prior to joining LG Technology Ventures in 2019. In her personal time, Michelle enjoys running with her dog Taffy, reading, and writing for her blog.

EVCA: Describe a defining moment in your career and how it shaped where you are today.

Michelle: Business school comes to my mind as the most pivotal experience that shaped my career today. Not only was I surrounded by unbelievably bright classmates who broadened and challenged my world view, but it was also an intensely intellectual experience. I am grateful for the wide range of knowledge-building opportunities that I enjoyed, including spending a week with government and business leaders in Rwanda, doing a consulting project for the Philadelphia Flyers, and learning about the art of influence and persuasion from Professor Cade Massey. Business school equipped me with a better tool kit for success.

EVCA: What is your most contrarian view on an existing or emerging technology trend?

Michelle: I will re-interpret this question as what is something that I look at very differently from others. I look at customer economics very differently from most investors. Most investors use point-in-time estimates to calculate CAC, LTV, etc. This is linear and overly simplistic. Influenced by my training in statistics and business analytics, I try to look at everything in the world on a statistical distribution. By peeling a layer of the onion and understanding the latent underlying customer behavior, one can draw unique and sometimes contrarian conclusions from the same data set that other investors are looking at.