How Ride-Hailing Companies Can Reduce Operational Costs and Improve Fare Accuracy

In India, where last-mile connectivity is still a pain point, ride-hailing platforms have experienced tremendous growth and reshaped the dynamics of urban transportation. But with the growth of the on-demand economy, shrinking physical spaces, high congestion and weak infrastructure, they face a unique set of business problems that only enterprise-grade spatial data platforms can solve.

Let’s dive right in.

The looming problem with on-demand

India’s mobility market is expected to reach $90 billion by 2030, states Frost & Sullivan report. The total shared mobility fleet is expected to touch 4.7 million units and revenue from ride-hailing is expected to hit $43.3 billion by 2025. The reason for this growth is that ride-hailing companies offer reliable service and wide spatial coverage with shorter wait times and at a lower cost than traditional taxi services and, perhaps, public transit.

The other side of this growth is the rising demands of customers that expect seamless service, instant gratification, quick results and constant access at low cost. Ride-hailing companies can’t meet these demands with customer-centric location and mapping solutions. Using a plug-and-play map’s API as a geospatial solution poses several challenges.

Consumer-centric maps lack the ability to update local serviceability conditions in real-time without the map provider’s decision. This means avoidable problems such as road closures and other incidents cannot be sidestepped by the team itself. This can lead to several inaccuracies in the predicted trip distance.

Some of other issues include:

● Inaccuracy in estimating the fare prior to the trip

● Miscalculating the ETAs

● Inability to account for time constraints to calculate surge pricing

● Inability to account for driver behaviour and preferences

● Inability to infuse real-time location data updates

● Revenue losses and client disputes rising from inaccurate routing

● Increasing prices (almost 10X hike) of consumer-centric API based solutions

For low-margin high-volume businesses, these challenges can severely impact the bottom line and eat into profits.

The pragmatic solution to make service meet demand in the on-demand economy

This is where the relevance of enterprise-centric location data platforms come into play. Ride-hailing companies should look at tailored mapping solutions, and promote long-term sustainable mobility with location data from their own platform.

The AI-powered new-age solutions require minimum integration effort and also offer active support at every stage of the process to minimize the time to market. They can be tailored to match the needs of every industry, use-case and business problem.

This is how spatial data platforms help ride-hailing companies to improve fare accuracy, ETA accuracy and reduce operating costs:

● By leveraging historical data: Evaluating an enterprise’s historical data can help to gain insights on past problems, unique challenges and evolution patterns. By mining internal data of 3-6 months, data platforms are able to look deeper into problems and discover areas that are affecting various performance metrics.

● By monitoring driver behaviour: Identifying patterns about a ride-hailing company’s fleet of drivers’ behaviour helps in determining the most efficient route. Right from figuring out why drivers frequently deviate from computed routes to learning about safe parking spots, data insights can be leveraged to strengthen the route optimization and the planning process.

● By creating a custom map layer: Performing gap analysis enables companies to track down the blind spots in their existing geospatial solutions. Enterprise-grade data management platforms can integrate the historical data findings and updated open-source data into a new tailored map stack by adding a custom layer.

● By creating DIY tools: Besides historical and open-source data, adding hyperlocal nuances like real-time serviceability conditions into the map stack also improves fare and ETA accuracy. DIY mapping tools allow the enterprise’s team to incorporate real-time changes without waiting for assistance from the mapping platform.

● By deploying seamless data migration strategy: Mapping platforms built for enterprises can tailor APIs that closely replicate the development principles of enterprise’s existing solution. This ensures that the data integration from expensive plug-and-play APIs to tailored APIs is faster, seamless and cost-effective.

The three Cs that can improve fare accuracy and reduce operational costs

For the ride-hailing industry, poor map and navigation capabilities often result in unfeasibly high last-mile delivery costs. A staggering 62% of the feedback that ride-sharing companies receive is related to maps, according to an Accenture report. From driver’s inability to find the right location to high wait time, from incorrect ETAs to poor route planning, users’ feedback highlights the negative impact of low-quality maps and location data on customer experience and consequently on the bottom line.

Ride-hailing companies should look for location data platforms that offer the three Cs:

Control - Through a DIY tool that can capture and allow real-time changes to be made to the map

Customization - Through a custom-tailored map that pulls insights from historic data as well as real-time data

Cost - Through a tailor-made solution that can minimize API costs, integration expenses and predict more accurate fare estimates and ETAs

With this golden trio in place, ride-hailing companies can successfully reduce their operational costs by 60% and improve fare accuracy by 12%.

Author: Gaurav Bubna, Co-founder of Nextbillion.ai