Improve Your Uber Clone App Services Using Big Data

Uber transformed the transportation business by allowing users to switch from traditional ride-hailing to booking a cab using their smartphone app. Even passengers prefer to use a ride-hailing app since it is more reliable in terms of getting a cab and provides a better user experience.

But the monumental success of Uber gave rise to multiple players who entered this industry with their own Uber clone app. So, how can you stand apart from the crowd and gain a substantial portion of the market?

By leveraging the power of Big Data.

This article will discuss how the data collected during the rides can be used to improve the car-hailing service. So, let’s get right into it!

Fare Estimates

Since this article deals with how you can leverage data to improve your Uber clone app, we will go through the process step by step to ensure maximum benefit.

The first thing a user sees after entering the drop location and before booking a cab is the fare. Estimating the fare for a particular ride is of utmost importance because if the fare is unreasonably high, users will abandon the app. And, if it is too low, it will prove to be disastrous for your business’ revenue. So, how do you come up with the perfect price? The answer is data.

You can estimate fares using a combination of internal and external data. Building an algorithm based on street traffic, GPS data, and the length of the route can help in automatically calculating the price. External data, such as public transportation routes, can also be analyzed to design various services.

Since the first step in improving your Uber service app is out of the way, let’s get to the next step.

Matching Algorithm

So, the user is satisfied with the fare and he presses the “Book Cab” button. Now, he has to wait for the app to find a driver and for the driver to accept the booking. The critical thing here is the wait time. Waiting for too long for the app to find a driver and then finding that it will take ages for the driver to reach the pick-up point can be a deal-breaker.

Let’s take a look at how big data takes care of this issue.

With the help of big data, your prediction models can forecast how long it will take a driver to reach a particular place if the pickup location, drop-off location, and time of day are provided. You can use advanced routing and matching algorithms to connect individuals with vehicles and destinations with people.

The routing engine and matching algorithms start operating as soon as the user opens the app until the cab arrives at the destination. These algorithms are able to work efficiently because of the huge amount of data that is at work and provides deep insights about the drivers and riders that help them make informed decisions.

Since you have a large database of drivers, as soon as riders order a car, the algorithm will get to work, matching them with the driver who is closest within seconds. The app stores data for every journey conducted, even if the driver has no passengers.

All of this data is stored and utilized to anticipate supply and demand, as well as to calculate fares. The algorithm will also examine how transportation is handled in different cities so you can address congestion and consider offering other services like bike delivery services.

Rating System

The customer is now satisfied with the fare and is delighted that he got matched with the driver within seconds of booking the cab. But how can you determine the experience of the actual ride? On the other hand, was the driver satisfied with the customer?

There are a lot of variables that might not be in the control of the algorithm. For example,

• Did the driver take the route shown by the app?
• How was the rider’s behavior towards the driver?

The best way to keep a tab on experience is the rating system. After every ride ends, the rider must have the option to rate the driver. It should give them different options to rate the ride and the driver.

Drivers, in particular, must be mindful of maintaining high standards since they are at risk of being fired or not receiving enough work if their ratings fall below a specific threshold. They also have to worry about another metric: their “acceptance rate.” This is the ratio of jobs they accept to ones they reject.

This rating system is one of the most important features of the Uber service and can help the driver understand where he needs to improve and will give you enough information to decide if the algorithm needs some tweaking or not.

But the rating shouldn’t be just used to determine the experience of the riders. You need to take care of the drivers too since they are the ones driving your business. Put too much pressure on them, and they will start looking for work elsewhere.

Therefore, they must also have the option to rate the riders. This will not only help other drivers determine if they want to accept booking for a particular rider or not, but it will also make them feel that you care about them.

The whole rating system can help in establishing confidence and allows both parties to make informed decisions about who they wish to share their ride with.

Surge Pricing

The above pricing technique might work during normal times. But what about the peak hours of the day or the holiday season when the demand is exceptionally high. In that case, you can use big data to alter the algorithm of your app like Uber for fare estimation.

Since the algorithms monitor traffic conditions and travel durations in real-time, rates can be modified when demand for rides fluctuates, and traffic circumstances imply that journeys will take a longer time. High demand and the high fare will encourage more drivers to get on roads, thus, fulfilling the demand.

One thing you must ensure is that you shouldn’t flip the switch on surge pricing whenever you want. It must be reasonable, otherwise, the riders will see through the deception and change the service provider.

In Conclusion

The success of your Uber clone app depends entirely on how you use the data from it to improve your app. If you have any questions about Uber clone app development, and how you can take advantage of the data generated from it, feel free to contact us.