Jai Ranganathan (KeepTruckin)
Jai is currently SVP Product at KeepTruckin, and was formerly VP of various AI and Data matters at Uber.
Uber's Customer Obsession Ticket Assistant (COTA)
A tool that uses machine learning and natural language processing techniques to help agents deliver better customer support.
Enables quick and efficient issue resolution for more than 90 percent of Uber's inbound support tickets.
Challenge
As Uber grows, so does the volume of support tickets
Millions of tickets from riders, drivers, and eaters per week
Global-scale of serving 600+ cities
Thousands of different types of issues users may encounter
Multilingual support
Customer Support Platform
Steps in the workflow
User → Select Flow Node → Write Message → Contact Ticket → Customer Support Representative → Select Contact Type → Lookup Info and Policies → Select Action → Write Response Using a Reply Template → Response → User
Problems to solve
Issue prediction
Issue categorization
Ticket routing
Ticket volume
Policy optimization
Auto-response
Exploration
Identify the right problems to solve
Use analytics to understand the value before all else
Know what metrics to optimize for
Understand whether Machine Learning is a good fit
Build with an eye on the probabilistic nature of Machine Learning solutions
Development
Many possible solutions including basic Machine Learning techniques
Understand the cost-benefit of compute time vs accuracy
Deep learning is a fast-evolving space - keep up with the literature to understand the latest advances
Validate your results with visualization
Deployment
Architecture complexity with feature engineering and training have special needs
Deep learning is still slow! Distributed deep learning can help a lot and is getting better
Good experiment design required to validate the models
Monitoring
Dynamic business problems require retraining strategies with well thought out safe deployment
Continuous improvement of labeling will make your models better
Look for edges where your models fail to find room for model improvements
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