Carpooling is a major industry in India. Due to overloaded traffic, people often prefer sharing their vehicle with people going in the same way to the office. It not only helps in lowering the traffic but also helps in side earning.
Problem Statement
The client charged a certain amount per km and shared 30% with the drivers. The client needed to optimize its pricing to enhance profit.
How we contributed
We built a pricing model for a cab-sharing company in Bangalore. It increased its profit by 18%. The pricing was set city-wise across 5 major cities in India.
Each city has different pricing per km which increases with the km range (like tax slab). The pricing model was made in 2 steps. In our first step, we calculated the per km price in each slab, in each city.
In cities where our brand is established we kept the prices on the higher side and in cities where we are starting the business, we kept on lower bound.
After that in the second step, we dynamically varied our prices based on the price elasticity of customers which we calculated based upon our historical customer data, time of booking, weather condition, place of booking, availability of public transport from that place and many other features using machine learning model.
