This case study presents how Jetson Electric (ridejetson.com) adopted analytics to become a data driven decision maker based, better understanding its customers’ mindsets and producing analytical use cases focused on operational efficiency.

In late 2021, Jetson was facing challenges in its after sales operations, especially in the brick-and-mortar retail sales channel. The company decided to try to use analytics to address the challenge. There were three pitfalls to pursue this agenda: Jetson’s vision of analytics was technical, lacking business insights; the data lake was nonexistent; and the data  was siloed and on the retailer’s websites.

The Business Challenge drove the team and the technical evolution. The project analytically redesigned Jetson relationship with retailers regarding After Sales, focusing on total P&L, evaluating root cause analysis and better negotiating chargebacks and cashflow. The team combined techniques, using descriptive analytics, regressions and machine learning to structure the recommendation models. In parallel, the analytics tech operation was evolved by adding Databricks as the collaboration layer and structuring the data lake architecture and governance.

The results were impressive: double digit cost savings in after sales, a never-before achieved comprehension of end-customer mindsets, and data much more ready to use.