This article will analyze the Uber artificial intelligence customer service system from data preprocessing to sorting algorithm. In order to create the best user experience, Uber continuously optimizes the customer support process and designs COTA to help customer service improve the speed and accuracy of problem solving.
A few days ago, an article on Uber's official website detailed the NLP-based and machine learning build? COTA customer service system. Utilizing this system, Uber can solve more than 90% of customer service problems quickly and efficiently.
To create the best user experience, Uber continues to optimize the customer support process to make customer support services easier and more convenient.
To achieve this goal, the Uber Customer Obsession team provides five different customer service channels based on its internal platform (application built-in customer support, official website customer support, local driver network, telephone customer service, offline service outlets). This internal platform has a customer service ticket function to track and resolve problems. There are hundreds of thousands of customer service orders submitted to this platform every day, covering more than 400 cities around the world. Customer Obsession? The team must ensure that customer service representatives can resolve problems as accurately and quickly as possible.
Based on this, Uber has created an artificial intelligence customer service assistant platform (COTA (Customer Obsession TIcket Assistant), which uses machine learning and natural language processing (NLP) technology to help customer service representatives provide better customer support services.
On the Uber customer support platform, with the machine learning service of the Michelangelo platform, COTA can solve more than 90% of customer service problems quickly and efficiently.
Below, we will detail the motivation to create COTA, COTA? Back-end architecture and show how to leverage this powerful tool to increase customer satisfaction.
Customer support without COTAWhen customers contact Uber for support, we must provide them with the best solution in a timely manner.
We can let users click on the category to which the question belongs and fill in the details when submitting a problem report. This process provides a lot of background information for the customer service, so that the problem can be solved faster, as shown in Figure 1:
Figure 1: Uber's built-in customer support service provides users with an intuitive and easy-to-use interface that first provides the type of problem possible and then highlights the details of the trip.
Uber's built-in customer support service reflects the background information of the problem. Although this information is important, it is not enough to solve the customer's problem alone, especially if there is multiple work order answers for a problem. In addition, the same problem, the customer can be described in a variety of ways, so the problem solving process is more complicated.
As Uber's services continue to expand, customer service must respond to growing customer support issues, and problems become more diverse, including technical failures and cost adjustments. In fact, when customer service solves customer-submitted questions, the first thing they need to do is to determine the type of problem from thousands of categories, which is no easy task!
It's important to shorten the time it takes to identify the type of problem, which can reduce the total time that customer service solves user problems.
After determining the type of problem, the next step is to give the right solution. Each type of work order has different protocols and solutions. In addition, choosing from thousands of possible solutions is also a time consuming process.
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