The service industry is experiencing exponential growth through a multitude of different services offered to customers, along with simultaneously expensive distribution channels and intense price competition. The challenge of attracting new customers is more pronounced than ever. Ultimately, the most significant asset of a company becomes their current customer base.
Retaining customers necessitates a deep understanding of their needs, and an effective tool for achieving this is the marketing theory associated with Customer Relationship Management (CRM). CRM is a strategic methodology that highlights the necessity of comprehending and anticipating the complete range of customer behaviors and requirements. In the operational aspect of CRM, the primary point of interaction between customers and companies is the contact center, which guarantees the essential Quality of Service (QoE).
Call center vs. contact center
Initially, CRM was largely focused on call centers, which companies utilized not only for sales, but also for post-sales services to assist users of their products. Typically, a call center is defined as a collection of resources, including personnel or agents, computers, and telecommunications equipment, required to facilitate service delivery via telephone lines. This definition has evolved continuously with technological advancements, transforming a simple phone call into communication through fax and email.
These centers were mainly designed to handle calls from dedicated facilities that addressed users’ inquiries regarding specific information, complaints, or suggestions. The further advancement of communication technologies has led to the evolution of call centers into contact centers.
Today, the modern contact center serves as a single point of contact for executing the entire external communication strategy and the company’s business approach concerning its customers. They offer workspace where staff engage with users through various communication channels, including telephone, email, websites, live chat, and social media.
Recent developments in technology are transforming how businesses interact with their customers. There is an increasing interest in innovative products and software designed for customer support that facilitate processes and communication. These offerings are typically enhanced with intelligent tools that provide integrated data and knowledge tailored to customer requirements.
Incoming and outgoing calls
While a distinct evolution from call centers to contemporary contact centers is evident, both terms continue to be utilized in literature and industry discussions. In terms of direction of the communication between interested parties, there are two types of the call initiation:
- Incoming calls are primarily utilized to furnish information to callers, address their issues, and enhance service quality
- Outgoing calls aim to engage in proactive communication with current or prospective clients and customers to boost sales, manage billing, or gather new information.
Omnichannel approach
To comprehensively grasp user needs, it is insufficient to merely establish a contact center that offers multiple access channels. Enhancing the customer experience necessitates the implementation of the omnichannel approach, which entails the complete integration of all access channels:
- Phone
- SMS/Messaging
- Website / Web forms
- Knowledge base
- Live chat
- Social networks.
When the communication process is effectively designed and executed, agents have access to a vast array of customer data, along with a more extensive and enriched environment.
This approach also enables customers to concentrate on their objectives rather than the communication channel, allowing them to prioritize their values and requirements. Customers typically seek prompt and clear responses to their inquiries and needs, as well as the personalized support. Consequently, they should have the option to select the most suitable channel for their specific requirement.
Companies are placing a strong emphasis on omnichannel strategies that incorporate search-based self-service and the utilization of knowledge bases. Nevertheless, a notable paradox arises, as self-service frequently results in an increase in incoming phone calls by as much as 10-15%.
Quality of service in contact center
Customers are becoming increasingly discerning and attentive to the quality of service they receive. Individuals anticipate accurate and contextual service in real-time, irrespective of the device they are utilizing. Failure to meet these expectations can result in a decline in trust. Conversely, when customer service or support successfully addresses issues, it cultivates a favorable perception of both the product and the company, ultimately bolstering customer loyalty.
The quality of service can be enhanced by adhering to two complementary models:
- Qualitative or psychological, which pertain to the manner in which services are delivered and perceived, i.e., whether the customer is satisfied with the response and whether the agent is approachable. These models are more empirical and derive from social sciences and marketing.
- Quantitative or analytical, implementing standard analytical techniques, such as the queuing theory, text mining, and data mining models.
All models strive to achieve key performance indicators – contact center KPIs, which are subsequently employed to assess agent performance and overall productivity.
The following performance metrics provide quantitative data that can be used to assess and improve the service quality:
- Average Handle Time: The average duration of a customer interaction, including the entire communication, until request resolution.
- First Call Resolution: The percentage of inquiries resolved during the first interaction. A call can be an interaction initiated through any channel, e.g. email, etc.
- Customer Satisfaction: A measure of how satisfied customers are with the service received.
- Net Promoter Score: Gauges customer loyalty based on their likelihood to recommend the service.
- Emotional Sentiment Score: Uses AI‑driven sentiment analysis to gauge emotional tone during and after interactions.
