People
Accounting, Customers, Customer Success, Executive Management
Apps
email, ServiceNow
Challenge
A nationwide supplier receives an average of 25,000 inquiries via email each month. These inquiries are delivered to 75 different email addresses and inboxes covering various customer service, billing, and delivery topics. Reading, routing, answering, and responding to each email is time-consuming and often results in errors and poor customer satisfaction. Additionally, this process is quite expensive with each email costing about $8 on average to fully handle. Management sought a faster, more cost-effective approach to improve customer service and lower costs. One potential solution is using AI to read, categorize, and quickly answer inquiries or route them to the appropriate expert. An automated system would reduce the time spent on each inquiry and provide quicker, more accurate responses to improve operational efficiency and customer satisfaction.
Solution
The company implemented Krista Cognitive Issue Resolution, an AI-led intelligent automation solution. Krista uses natural language understanding (NLU) to read each inbound email, extract data from the content, and provide the next-best-action based on each request. To begin, the company provided one month of email as training data for machine learning. With only a single month of data, Krista learned to respond to 85% of the questions. She routed the remaining 15% in ServiceNow to the appropriate service representative and asked them to respond to the request. In responding to the remaining emails, the service reps train Krista to handle similar questions in the future.
Results
After implementing Krista, the company saw a significant reduction in time spent on each inquiry. Many times issues would take days to complete. With Krista, the company can respond almost immediately. Automating intelligent email responses with Krista resulted in over 2 million dollars per year of labor cost savings by redirecting labor from answering emails to more critical customer service work. As work progresses and more inquiries provide more training data, Krista and her powerful machine learning will answer more and more inquiries. The savings will only continue to grow, and the company plans to reinvest what it learned to find more opportunities to automate processes outside of customer service. Every business will use machine learning and AI to automate tasks in the future. Thanks to Krista, this company provides better, more timely service to its customers at a fraction of the cost.
