Alexander Isakov

Alexander is a Director in KPMG Lighthouse, KPMG’s Data Center of Excellence. He is focused on developing strategic analytics solutions to transform the Life Sciences and Consumer and Retail spaces. An entrepreneurial executive, Alexander has co-founded successful start-ups in healthcare and fintech. As a consultant, he has worked with executives in major companies to help them make data-driven decisions that impact strategy, operations, and customer experience. He is a constant innovator who predicts future customer needs and markets, and is backed by a history of inventing solutions, attracting investment, and growing initiatives to sustainability and exit. Alexander completed a PhD in physics at Harvard as an NDSEG Fellow and a postdoctoral fellowship at the Yale Institute for Network Science.

Relevant Experience

Alexander is a true game changer, with a proven talent for developing innovative concepts, aligning high-performing teams around a broad vision, and solving complex business challenges. His experiences also include:

• Predicting innovation trends – led effort to identify, collect, and analyze multi-faceted data sources for helping a major corporate VC fund in the retirement space identify a strategic focus for early-stage investments. Created innovative solution for analyzing co-investment networks to show where to focus for generating partnerships in the venture capital community.
• Data strategy – created and deployed a full solution for analyzing data confidence and the impact on strategy for major companies in the retirement space. The goal was to enable the full senior leadership team to understand the importance data quality across the organization (from “individual customer” up through “intermediaries” and “competitive intelligence”) in order to increase the probability of success of planned initiatives.
• Customer retention – created a model and factor analysis to predict which customers were likely to churn from a cloud-based document compliance company. The model incorporated behavioral data (such as log-ins, end-user compliance statuses, contacting the help center) and was able to identify customers at risk of leaving three months in advance. It was also able to identify four key levers that management could pull to increase customer retention.