A scooter company uses zoba to increase rides per day in a key market.

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Demand Modeling

A scooter company was having difficulty increasing rides per day in a key market. They were experiencing high levels of competition in the city and were not adjusting their morning drop off locations based on fluctuating demand or things like weather.

Data scientists at this company used the Zoba Platform to decouple supply from their utilization data to see exactly where their customers would use their vehicles if they could have them anywhere.

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Predicting Demand

This company then used the Zoba Platform to model the relationship between demand for their service and environmental factors like weather to create demand models that fit different scenarios.

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Optimizing the Fleet

After connecting the Zoba Network Design Optimizer to their rebalancing backend, they were able to automatically recommend the distribution of their fleet on a daily basis based on things like seasonality and weather.

Because they used Zoba’s free floating optimizer, they place vehicles in locations that would allow them to move with demand throughout the day, drastically increasing their rides per day in this key market.