Wanderlift

As a freshman at DU, I was eager to jump into the tech community in Denver and the resources at the university to support student founders. After a few projects where I was finding my footing, I joined the team for Wanderlift, a campus founded ride share startup.

The idea behind Wanderlift was to help students access the mountains by letting drivers sell empty seats on drives they were already making. This model allowed drivers, on average, to earn 2X more than an Uber or Lyft while riders to pay 75% less. 

The initial founding team was made up of two engineers and two designers, and I was approached to lead the business operations and growth. 

In just 8 weeks, over 12,000 miles were shared on the platform, we represented the United States in the finals of the Global Grand Challenges Summit business model competition in DC, and attracted the attention of Zipcar, Vail Resorts and the Colorado Department of Transportation’s RoadX program for partnerships. 

After succumbing to legal issues brought on by Lyft and the PUC, we moved on, having learned a great deal. So how did all of this happen and what did we learn?

When we launched, we saw Wanderlift as a way to get more students to the mountains on the weekend. After launching in Downtown Denver however, we began to get emails from municipalities interested in using the system for commuters, trips to the doctor and more. 

With indication of product-market fit outside of students, the team began considering it’s application for cities to solve the last mile problem: basically helping residents in rural areas to connect to places of work or hospitals and as a potential benefit provided by employers. As we were preparing to grow into these new areas, legal action from Lyft forced us to close our doors. In short, as students we didn't have the money to fight it and decided it was on to the next one. 

Wanderlift was our my first true foray into agile business modeling and the experience has taught me how to quickly and effectively iterate both product and operations to find the most reliable path forward.