Increased Chatbot Adoption using Conversational Intent Detection

Case Study

Increased Chatbot Adoption using Conversational Intent Detection 

Business Objective

Our client is a leading financial institution, offering banking, wealth management, and brokerage services to its customers.

The client has deployed a chatbot on Google Cloud™. The intelligent assistant enables their customers to perform trading and banking activities like getting a quote, getting market updates, placing a trade, transferring money, investment FAQs, etc.

The objective was to build a scalable engine within the chatbot to detect customer’s intent and offer appropriate solutions, thereby increasing chatbot accuracy.

Challenges

  • Difficult to tag and identify good quality chat instances that can be used as bot training data
  • Each user transcript needs to be manually reviewed to check for classification accuracy
  • The off-the-shelf platform does not provide any user adoption metrics or agent performance metrics. The data required to track these metrics are scattered across different sources

Solution Methodology 

  • Created models to identify the set of users who have a higher chance of using the intelligent assistant
  • Used NLP and ML algorithms on the vectorized user transcripts to create an initial prediction of intent along with their confidence score and entropy (uncertainty)
  • Created sampling schemes based on entropy to create an entropy sample to manually review and for classification of true negatives and false positives
  • Iteratively enhanced the models on Google Cloud™ Dialogflow™ API with enriched data to create a self-learning feedback loop

Business Impact

 

  • Increased user adoption and engagement with chatbot by increasing first contact resolution
  • Better routing of engaged clients for targeted upsell opportunity
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