Millions of tickets arrive at Uber’s customer service department every week from its riders, drivers, eaters, etc. It is important for Uber to handle these tickets in a quick and efficient manner to retain its customers and fuel the companies growth. For this purpose, Uber has designed COTA or ‘Customer Obsession Ticket Assistant’.

COTA is a Machine Learning and NLP powered tool that enables quick and efficient issue resolution of more than 90 per cent of Uber’s inbound support tickets.

Overview of COTA system architecture

COTA System Architecture with seven-step workflow from Uber

The general COTA architecture follows a seven-step workflow as shown below:

  1. Once a new ticket enters the customer support platform (CSP), the back-end service collects all relevant features of the ticket.
  2. The back-end service then sends these features to the machine learning model in Michelangelo.
  3. The model predicts scores for each possible solution.
  4. The back-end service receives the predictions and scores and saves them to a schema-less data store.
  5. Once an agent opens a given ticket, the front-end service triggers the back-end service to check if there are any updates to the ticket. If there are no updates, the back-end service will retrieve the saved predictions; if there are updates, it will fetch the updated features and go through steps 2-4 again.
  6. The back-end service returns the list of solutions ranked by the predicted score to the frontend.
  7. The top three ranked solutions are suggested to agents; from there, agents make a selection and resolve the support ticket.

Overview of NLP Pipeline used by Uber

A diagram showing NLP pipeline for ticket issue identification and solution selection by Uber

For detailed information about different processes in the pipeline, please refer to this article by Uber.

In Conclusion

Uber is known to organize its processes using Machine Learning to achieve high speed and accuracy. What do you think about Uber’s COTA?

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