By using RAGE-AI™ automated system more customer service data could be analyzed in new data sources, including email exchange analysis, hand-offs and internal activities.
Analyze customer service interactions to assess drivers of dissatisfaction. Identification of top drivers for customer calls and customer dissatisfaction by semantically analyzing the unstructured customer service text and attachments (emails/pdfs). Automated framework to analyze this data continuously across 7 countries in 7 languages
- What are the top reasons why customers are calling us? Where do customers think we are difficult do business with?
- Are the reason codes in CRM system and the actual reason (from the description text) why customer is calling aligned?
- Which processes can we transform or automate so that it is easier for customers to business with us?
- Intelligent Machine to monitor customer service data continuously across seven countries in seven different languages.
Extract and interpret customer service data from company’s CRM; email threads; customer service-driven PDF attachments; and service response and fulfillment data from the internal ERP.
- Identified top reasons why customer is call by semantically analyzing the unstructured customer service text and attachments (emails/pdfs). Created a automated framework to analyze this data continuously across 7 countries in 7 languages.
- Identified significant customer facing process complexities and automation opportunities by analyzing customer interaction data, such as email exchange analysis, elapse time, hand offs, internal activities.
- Automated customer service request handling which will significantly reduce the cost of handling and increase the customer response quality