Optimization of Agent-User Matching Process Using A Machine Learning Algorithms
Contemporary dynamic market conditions are characterized by high levels of competition. Businesses are forced to make continuous changes in order to maintain a high level of customer’s satisfaction. Contact centers are part of the global economy and a key channel for communication with customers. Access to the contact centers is realized through several channels (telephone, e-mail, fax, web forms, etc.), but the primary method is still a phone call. Most call centers use Interactive Voice Response (IVR) to route users with a particular problem for directing the user to an agent with appropriate capabilities (Skill-based routing – SBR). This type of contact center operation can have a negative impact on the quality considering customer’s experience, due to the complexity and length of the process, which often results in directing calls to the inadequate agent and describing the problem multiple times to reach the right agent. This paper aims at applying machine learning methods based on prior experience with matching customers and agents in order to reduce time and target agents with adequate problem-solving abilities.