The swift progress of developments in technologies such as AI and ML is bringing bizarre favorable circumstances to intensify the productivity of several industries and businesses, counting the transport sector.
The innovation introduced by such technologies incorporate highly state-of-the-art computational methods that imitates the human mind and covers complex algorithms.
One of the leading business companies in transportation is “Uber”, it uses tech that aims to defeat the challenges of increasing travelling demand, severe gases, safety concerns, and unavoidable environmental impacts.
“For every Tesla or Uber, there's a Valeant Pharmaceuticals or Theranos - two story stocks that seduced an astounding array of prominent investors and supporters based on stories that did turn out to be too good to be true. -James B. Stewart”
To line up with this blog, you will discover how Uber is exploiting Machine Learning, And Artificial intelligence for Uber services optimization and serves multiple benefits in context of enhanced and exceptional customer experience and satisfaction.
(Similar blog: How is Tesla using AI?)
With implementing machine learning at core, decision making of Uber is completely data-driven. Uber leverages several ML models in several ways for making exceptional customer experience and seamless Uber’s services.
For example, an individual taps a destination place, the app suggests options based on ride history and recently traveled destination. Following are some interesting means by which Uber utilizes ML to facilitate the business process analysis;
Looking at the archival data, the Uber team can estimate the time and location of demand. The system adopts these estimations to aware drivers of the particular region with the leads of demand. Through this, Uber ensures that there must be enough cabs in the demanded area and fill the gap amid route and supply.
Demand forecasting systems allows the app to hike the prices marginally midst peak hours that augment profit and demand ultimately.
For plausibility, customer retention is crucial for several services, sometimes customers don’t want to delay their daily rides when cabs are not available instantly, they just book a cab from another service.
Even Though, getting new customers demands more effort than retaining existing customers. The supply-demand gap can affect customer retention to some extent that can be prevented via machine learning based forecasts, it also aids Uber from losing customers to its rivals.
(Also read: Top AI and ML Trends in 2021)
“Expected time arrival” is mainly considered when you book a ride, it’s extremely frustrating when much time is wasted in traffic jams, especially in urbanized areas. This situation gets worse when a cab takes longer time than expected to reach a pickup location.
Specifying the machine learning specialties, it also addresses this issue which Uber utilizes. By forecasting demand and putting cabs ready, Uber can decrease the expected time arrival (ETA) when customers book rides.
Uber always makes the experience superior by reducing waiting time a lot. The flawless fusing of customer satisfaction, adhere schemes, and fruitful services leads to colossal augmentation in the Uber.
5 ways ML helps Uber
Conventional ride-calling systems expect the drivers to choose routes based on presumption and availability which is not stable due to travel span might get changed due to huge traffic on the same route, weather conditions, road construction set up, etc.
But, twist here, the ML system of Uber upgrades the app with the possibilities in each route and recommends the agile route to drivers.
(Similar read: Role of Big Data Analytics in weather forecasting)
Through this process, Uber assists drivers to avert crowding areas and enables speedy rides. It not only makes customers pleased but also offers drivers extra time to conduct additional rides.
You, as a rider, used to message drivers while waiting for the cab, even most of the time, the rider does this to know the condition when the cab will be available, or when he sees the cab scarcely roaming in the app. But on the same note, it is difficult for cab drivers to type texts while driving.
So again, Uber came up with the solution as an AI-based idea known as “one-click-chat” that uses Natural Language Processing and Machine Learning technique to frame answers to common messages. By this way, cab drivers could respond effortlessly by just clicking on one of the suggested responses.
(Must watch: L1 and L2 regularization in machine learning)
It is ridiculously difficult to make cab available in rush hour for everyone, but shared riding makes it possible when Uber Pool was introduced. With ease share riding solves issues by coordinating riders heading in the same direction. Plus, features of pooling makes riding more economical via decreasing cost for riders.
Through the ML algorithms, it can be decided which rider to drop first on the basis of data accumulated from maps. Additionally, Uber app harnesses prior data and patterns in order to understand peak hours and escalate prices appropriately.
(Recommended blog: Machine Learning Applications in daily life)
AI techniques and models enable Uber to relocate the needle around the multiple verticals, from maneuverability and transportation to customer assistance and drive-partner navigation. More research in AI yields to momentous augmentation in trade prediction and more smooth pick-up experiences.
Uber deploys AI for ;
Fraud detection and risk estimation,
Marketing consumption and distribution,
Meeting drivers and riders demand, and
Route optimization and cab-driver onboarding
One of the main successes of AI in uber is “ Customer Support Systems” , while handing millions of customers Uber provides a lot of services, AI aids agents with sustained responses to customer queries. It not only improves Uber efficiency but also steadily hikes customer satisfaction.
(While counting AI idiosyncrasies , catch first article as Role of AI in TikTok, in Hindi)
Basically, AI helps in understanding customers, by obtaining their needs, habits, and preferences, and to solve their problems, it becomes crucial to handle customer segments precisely.
Finally, moving towards blog ending, so, before conclusion, have a sneak at Customer Behavioral Analytics, afterall ;
“Customer service shouldn't just be a department, it should be the entire company." -Tony Hsieh
How simple process is that in an Uber app, press a button, a car comes up, you go for a ride and reach your destination, and again press a button to pay the driver. But it’s not as simple as it appears, there are various things behind the scenes on which the Uber team is continuously working for improvement.
When you think about Uber and Airbnb and the other companies that are turning things upside down, Uber isn't big 'cause they ran a lot of ads. They're big because someone took out their iPhone and said to their friend, watch this, and pressed a button and a car pulled up. -Seth Godin
Some of the enhancement can be observed in this blog along with how Uber leverages data exploitation with the technology either it is Machine Learning or Artificial Intelligence.
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