Developing a bot that can answer support queries and aid in decision making with analytics
Responding to repetitive queries from customers can overload the support team. Developing the capability to handle such repetitive queries can significantly enhance the productivity of support agents and they can utilise their time in resolving problems that are more challenging and involved.
This talk will focus on the modelling approach that we at Freshworks took to develop a bot that has the ability to understand natural language, respond to customer messages and aid agents in understanding the kind of content that needs to be created to enable self service. In addition, we’ll also throw light on deployment and challenges involved in scaling a system such as this.
- Motivation - Why should this problem be solved
- Problem Statement - Definition of the end objective
- Modelling Methodology - An overview of the available data, ML algorithms and validation metrics that define success
- Deployment in Production - An overview of the pipelines for data extraction, training models, serving predictions and feedback consumption
- Challenges - scaling and dealing with streaming data
I joined the data science team of Freshworks in May 2018 and since then have been working on problems in the NLP space. Prior to this, I have experience of solving problems including risk modelling, churn modelling, life time customer value prediction, campaign analytics, creating networks from social data to decipher relationships and market mix modelling.