Call center agents listen to a lot of information from customers. This information is not stored or used to improve customer service, increase sales or create new profitable products and services. Though most of the call center calls are recorded, accessing to and making sense of the information is difficult because it requires to listen to the recordings, which takes a lot of time and resources.
Sigma’s Conversational Labeler transcribes conversations, extracts information and stores it in a structured format, so it can be analyzed and exploited.
Sigma’s Conversational Labeler uses NLP & NLU technology to extract structured information out of the voice calls. This information is stored in a database that can be later used to provide better service when customers call, and estimate the propensity of customers to buy a new product or service, to design personalized campaigns and offers, etc.
Sigma’s team analyzed a sample of the calls and collected and discussed with the client the categories, topics, entities, keywords, expressions and collocations to be extracted and analyzed as well as the data base structure. This information was used to adapt the Conversational Labeler to the specific call center domain.