The Services Information and Analytics (SIA) team at IRL is involved in projects related to the application of natural language processing, information retrieval, text analytics, data mining, machine translation, and speech technologies for enhanced service delivery. We deal with unstructured, noisy, and multi-lingual information sources such as Web pages, documents, e-mail, ticketing systems, team rooms, and IT monitoring systems to generate business intelligence and insights that can improve the quality, productivity and efficiency of IT, application, and contact center services. We also build analytical models and solutions for knowledge process outsourcing, and tools for workforce optimization in IT services.
One of our focus areas is Services Analytics (SA). We are working on various applications of analytics for increasing efficiency as well as improving productivity and resource utilization in services businesses. Examples include improving the efficiency of resource utilization by automatically suggesting matches of people to projects, automatically filtering resumes of people applying for jobs, improved authentication through biometric fusion, among others.
In the area of contact center research, we are developing an array of technologies ranging from automatic evaluation of the linguistic abilities of applicants to customer satisfaction analysis involving heterogeneous contact center data sources, such as, audio conversations, agent logs, and feedback forms. The goal is to transform contact center services from a labor-based model to an asset-based model and help business analysts generate greater insights to improve the delivery quality. In the area of speech technologies, we are working on solutions that help people access information in various forms by overcoming barriers such as physical disabilities, lack of IT skills, and illiteracy. Examples of problems that we are addressing include information extraction from the World Wide Web using voice interactions and customer satisfaction (C-SAT) analysis to extract dissatisfaction drivers among customers.
|
 |
|
