AI Use Case
Optimise call routing based on customer characteristics potentially including expressed intent
Call routing (i.e. determining wait times) based on caller id history, time of day, call volumes, products owned, churn risk, LTV, etc. Route calls to most capable agent available and ideally leading to fewer agent-handled calls - hopefully leading to increased customer satisfaction and reduced handling costs.
Operational - Customer wait times,Operational - Faster response times ,Revenue - Customer experience
Acer~Acer America improves service by decreasing repeat caller rate by 15% with responses powered by natural language speech recognition.,Marks & Spencer~Marks & Spencer plans to automate all customer call routing with 90% accuracy using machine learning
Audio,Structured / Semi-structured
Machine Learning (ML),Product - Customer Service,Product Type - Speech - Recognition,Model Architecture - Deep Neural Networks,ML Task - Prediction - Multi-class Classification,ML Task - Prediction - Binary Classification,ML Task - Prediction - Data Translation/Transformation,Traditional AI,Product Type - Speech - Voice to text,Product Type - Natural Language Processing (NLP),ML Task - Prediction - Regression