HOW UBER USES MACHINE LEARNING TO REINVENT TRANSPORTATION?
Uber is one of the most successful startups of all time. The idea of Uber was born when its co-founder Garrett Camp had to pay $800 to hire a private driver on a New Year’s Eve. The idea was converted into a business called UberCabs in 2009. Initially Uber offered rides only in black luxury cars, and three years later, UberX was rolled out. UberX allowed people to drive for Uber using their own cars. The business took off, and the startup joined the eight-figure decacorn club being valued at over $70 billion in less than 10 years. Uber gives about 1 million rides per day and 14,000 rides per minute and adds about 50,000 drivers per month. Giving customers exactly what they want at a reasonable cost is one of the important reasons for Uber’s success. Uber’s machine learning algorithms play a crucial role in helping the company predict customer needs.
Let us discuss how Uber uses machine learning to deliver exceptional customer experiences.
What is Machine Learning?
Traditionally, humans played a key role in analyzing data patterns and building systems on top of these patterns. When the volumes of data surpassed the ability to manually process it, the need for automation was triggered. This need eventually gave birth to machine learning. In a nutshell, machine learning is about making computers answer questions based on data patterns.
We see machine learning in almost all the systems around us today. For example, when you watch videos of a particular celebrity on YouTube, the system understands that you enjoy content related to that celebrity. The system then recommends similar videos. Here, YouTube’s machine learning systems learn from your browsing data and predicts your likes. The same logic is applied in systems like Facebook’s photo tagging recommendations, auto-correct and text prediction in mobile phone keyboards, and so much more.
Machine learning is at the core of Uber
Uber is undebatably the biggest cab service provider globally. Uber has been leveraging futuristic technologies for optimizing processes and enhancing customer service. Uber engineering is dedicatedly exploring methods to provide better services for maintaining the lead in market share.
Uber Engineering is always working on figuring the use of machine learning (ML), artificial intelligence (AI), and other advanced technologies to serve their customers better. Uber needs to gather masses of data to make predictions about market demand, find the best routes for drivers, quickly respond to support issues, keep updating its knowledge of changing roads and even detect and respond to potential fraud.
Uber is using ML to enable an efficient ride-sharing marketplace. Machine learning algorithms identify suspicious or fraudulent accounts along with the suggestion for convenient pickup and dropoff points. It is the machine learning that facilitates the UberEATS delivery by recommending restaurants to the users and predicting wait times etc so that user get their food on time.
Uber uses Machine learning to optimize their maps. Maps hold high importance for Uber. Right from the destination search and prediction, generation of map tiles, ETAs, routing, and up-front fare estimates, maps are integral to every element of our logistics network. Even maps cover more than 95 percent of pixels on the rider and driver app UIs.
Uber leverages machine learning for growing its marketplace. A variety of teams such as Forecasting, Dispatch, Personalization, Demand Modeling, and Dynamic Pricing uses ML algorithms. Machine Learning enables precise coordination, real-time decision making, and learning needed to monitor the movement of the transportation network.
Machine learning algorithms enable to “see into the future” as accurately as possible across both space and time. ML enables us to generate spatiotemporal forecasts of supply, demand, and other quantities in real time for up to several weeks ahead.
Uber uses techniques such as long short-term memory (LSTM) networks, to help predict the future of the Marketplace and predicts the occurrence of extreme events even before they occur!
Uber uses ML-enabled Natural Language Processing (NLP) platform, generates actionable responses for customer support tickets, chatbots to make driver onboarding easier, and suggested in-app replies. With the commitment to driver partners, Uber has been using NLP platform along with deep learning models to optimize the recommended actions and turnaround times for our support tickets.
Uber Bridges the supply-demand gap by Leveraging machine learning. Uber predicts the time and areas of demand based on historical data. The system uses these predictions to alert drivers of the regions with future demand. Uber makes sure that there are always enough cabs present in the predicted areas of demand and thus bridges the supply-demand gap. Demand prediction systems help uber to slightly increase the prices during peak hours that result in more profitability.
Reduction in ETA
The time wasted in road traffic is one of the most frustrating problems in the urbanized areas. This gets worse when cabs take longer to reach the pickup point. But, Uber’s machine learning algorithms have a solution for this issue too. By predicting demand and keeping cabs ready, Uber reduces the expected time arrival (ETA) when a customer makes a booking. By reducing a lot of wait time, Uber always makes the customer experience better. The perfect blend of customer satisfaction, loyalty programs, and referral bonuses has resulted in a massive expansion in the customer base just through word of mouth.
Route optimization
Conventional ride-hailing systems require the driver to make assumption-based route choices. This method is not reliable because the travel duration through the same route might change based on traffic jams, weather conditions, and road maintenance schedules.
But Uber’s machine learning system updates the app with the conditions in every route and suggests the fastest route to the driver. This way, Uber helps its drivers avoid congestion and enables faster rides. Besides making the customers happy, faster rides also enable drivers to get additional time to take on more rides.
Uber Pool
During rush hours, it is difficult to make individual cabs available for everyone. But ridesharing solves this problem by matching the riders heading in the same direction. Also, the pooling feature makes the rides more economical by reducing the fares by 25 percent to 40 percent. Machine learning algorithms decide which rider to drop first based on the data gathered from maps. Also, the app uses historical data and patterns to understand peak hours and surge prices accordingly.
AI-based one-click chat
Riders tend to message drivers while they wait for the cab. Most of the time, riders do this to check the status when they see the cab barely moving in the app. It is difficult for drivers to type a reply while driving. So Uber came up with an artificial intelligence-based concept called the “one-click chat.” The one-click chat leverages natural language processing and machine learning techniques to predict responses to common messages. This way, drivers can easily respond to the messages by just clicking on one of the suggested replies.
Uber understands the importance of customer retention. Customers, they book a ride from a different service in case of unavailability of the cabs. Getting a new customer can take up to six to seven times more effort than retaining an existing customer. The supply-demand gap can make customer retention difficult. All thanks to Uber’s machine learning based demand predictions savs Uber from losing customers to its competitors.
Uber has truly shown the world how technology can be leveraged to optimize various business processes.
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