Discussion Regarding Gaussian Mixture Model to Acquire New Customers in Non Native Territories
Duke Energy wants to acquire new non residential commercial customers outside of its native footprint who would be interested in buying energy efficiency programs like HVAC, Lighting, Refrigeration and other appliances. The current leads provided by Business energy advisors through their business relationship are very low in number. The challenge is to model the behavior of this population and find out others who may be interested in energy efficiency programs. Gaussian mixture model is a probabilistic clustering approach and may help us in finding patterns within data. This can help the Business energy advisors by providing them with more effective lead generation.
Key Takeaways: • Business case study understanding • Lead Generation with unsupervised M in any industry • Technical knowledge of Gaussian Mixture model
Dipjyoti Das is an experienced Data Scientist and end-to-end solution provider having worked in various industries – Energy & Utilities, Financial Services & Insurance, Logistics and Automotive manufacturing. Das has managed clients in cross functional business units – Marketing, Sales, Distribution, Product and Operations. His AI/ML models have contributed to millions of dollars in incremental revenue and cost savings across different industries. His current work in the natural gas business at Duke Energy acted as a foundation and led to development of multiple analytics projects in other areas. He has a MS in Materials Science and Engineering, University of Florida.