Nowadays, companies face numerous challenges to operate successfully and remain competitive in the market. Globalization is increasing competition in the market, allowing many companies to operate in foreign markets. Increasing competition has made companies constantly trying to increase their productivity while reducing costs. To meet all the requirements, and at the same time function in the best possible way, companies must be organized adequately, taking into account the whole set of processes from the company's internal organization to the company's appearance on the market. It means, if the end-user is to be satisfied, the company must implement quality in all phases of business, ie business ethics and company culture, through the quality of technology, personnel, etc. In other words, it must be satisfied the system quality of the company, known in the literature as QMS (Quality Management System). The main purpose of this paper is to review the multiple understanding of the concept of QMS and its different underpinning theories in a business to customer context. The design of this research is based on mere documentary analysis and some observations.

– 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 .