Zagster increases rides per day by 74% in one month in Albuquerque.
Zagster provides best in class fleet management services and local operations to mobility operators in 250 markets and 35 states across the United States including Albuquerque, New Mexico.
Rides per scooter per day; weekly mean.
Percentage increases in rides per vehicle per day (weekly mean) in the month after Zoba intervention in Albuquerque. Percentages are changes week over week.
Zagster wanted to increase the utilization of its Spin Albuquerque market by optimizing dropoff locations and daily rebalancing efforts.
The City of Albuquerque had been directing the placement of most scooters in the downtown area of the city but had recently opened up the possibility of placing vehicles in other areas, allowing for a network optimization.
Zagster partnered with Zoba to do an in-depth market analysis of Albuquerque and to deploy a model that would inform the most optimal distribution of their fleet daily.
Zoba provides demand forecasting and optimization tools through an API to mobility operators. Zoba leverages proprietary demand forecasting models that predict true demand—where users would want vehicles in all conditions absent supply and other constraints. Zoba's optimization models use our demand forecast, understanding about future conditions, and how users behave in markets to maximize utilization. Using the Zoba API, operators deploy vehicles such that they get the most revenue per asset possible in a given market.
As a part of their work with Zoba, Zagster was able to:
Look at underserved areas of the city where they were experiencing demand, but were not dropping off vehicles. Zagster was able to use these demand forecast maps to choose new dropoff locations and to gain approval for these new locations from the city government.
Precisely allocate vehicles to their dropoff points each morning based on predicted demand and environmental features like the weather so as to increase the number of rides taken throughout the course of each day.
Conduct a macroscopic analysis of demand to understand that they were likely missing rides in the two hours following their evening pickups. Zagster then made the decision to pick up the vehicles later at night.