The call center is the backbone for delivering excellent customer service for most companies. However, in the post-COVID-19 era, it has been challenging for call centers to provide great customer service due to the record-high agent annual turnover of 38%, with over 85% of agents working from home (WFH) and not having the necessary technology for the WFH model..
After three years of customer service delivery, employee turnover, and absenteeism challenges, many leaders are embracing new call center technology to help improve performance. In addition, an increasing number of customers want to interact with companies through touchpoints such as self-service (e.g., IVR, website), email, social media, video, chatbots, live chat, and an agent. Furthermore, customers want a seamless customer journey when using multiple touchpoints to resolve the same inquiry or problem.
What is Call Center Technology?
Call center technology entails a wide range of hardware and software that does everything from automatic call distribution, computer telephony integration, intelligent callback, call recording, artificial intelligence, customer relation management, workforce management, interactive voice response, and video. In addition, call center technology is essential for improving customer service, employee job satisfaction, and lowering agent attrition and operating costs.
Call Center Technology Trends
SQM Group's research shows that 95% of companies believe they must implement new call center technology solutions to improve their customer service significantly. Furthermore, SQM's research shows that the call center technology trends companies consider the most important for improving customer service delivery in the next few years are the following percentage breakdown:
These call center technologies can help companies transform the way they deliver customer service more efficiently and effectively. Here are more insights into the four call center technology trends you should consider implementing to improve customer service, agent attrition, and lowering operating costs in the next few years.
1. Analytics and Reporting
Analytics and reporting are based on capturing and analyzing customer and agent data to discover insights for assessing and finding new insights for improving call center performance. Call center analytics and reporting tracks call data and agent performance for handling customer calls.
Data capturing and analyzing can be customized, automated, and condensed into reports that provide detailed insights into key performance indicators (KPIs) such as first call resolution, customer satisfaction, cost per call, average handle time, call volume, and hold time. Call center analytics and reporting use data from multiple sources and communication touchpoints such as:
- Call Center Data (e.g., AHT, Volume)
- Help Desk Data
- Customer Surveys
- Employee Surveys
- Chat Data
- Email Data
- CRM (Customer Relationship Management)
- IVR (Interactive Voice Response)
- Call Recordings
- Event logs
- Quality Assurance Scores
In addition, there are multiple ways to analyze call center performance. It all depends on the software and hardware that a call center employs. The different software and hardware methods have various benefits and features to use when analyzing and reporting call center data. Some common methods for analyzing and reporting call center data include the following:
- Speech (AI) Analytics: This method of analytics uses algorithms to assist in call monitoring in real-time or call recordings for observing customer sentiment and analyzing tone. Moreover, AI allows you to analyze and report on customer experience.
- Text Analytics: This method of analytics is essential for companies that interact with customers over touchpoints such as email, live chat, social media, or other text-based interactions.
- Performance Analytics: In this method of analytics, agents, and supervisors use dashboards with real-time and previous records of KPI performance. A call center dashboard provides data visualization reporting that displays KPIs and metrics to assist supervisors and agents in monitoring and improving their performance.
- Omnichannel Analytics: This method of analytics evaluates customers who use multiple touchpoints (e.g., website and call center) to resolve the same inquiry or problem. Omnichannel (sometimes called customer journey) analysis is essential for companies to make it a seamless experience or a better journey for customers resolving an inquiry or problem using two or more touchpoints.
2. Omnichannel Integration
At SQM, we define an omnichannel as when a customer experience (CX) is seamless across all touchpoints to resolve the same inquiry or problem. Put differently, when a customer used another touchpoint to resolve the same inquiry or problem, they could pick up from where they left off at the previous touchpoint and, as a result, did not have to start from the beginning.
Conversely, in a non-seamless experience, customers had to begin their interaction from the beginning each time they used another touchpoint to resolve the same inquiry or problem. When customers use two or more touchpoints and it was a non-seamless CX, the multichannel Csat (top box response) is 28%.
In other words, when customers use another touchpoint, they have to start their inquiry from the beginning, thus having to repeat their previous actions. As a result, most customers who use two or more touchpoints to resolve the same inquiry or problem do not consider their experience to be seamless.
Csat drops by 61% when two or more contact channels are used to resolve the same inquiry or problem compared to customers who experienced One Contact Resolution (OCR).
This 61% drop in Csat represents an excellent opportunity for call centers to improve individual touchpoint performance. Improving individual touchpoints' OCR performance would help customers avoid using two or more touchpoints to resolve the same inquiry or problem. However, if a customer wants to use two or more touchpoints or it is necessary, it should be an omnichannel experience because if it is not seamless, then Csat is significantly lower.
In most cases, when a customer uses two or more touchpoints to resolve the same inquiry or problem, it is due to a failure of a specific touchpoint. The bottom line is that when a customer uses two or more touchpoints to resolve the same inquiry or problem, most feel that they are starting over again with each subsequent channel used to resolve their inquiry or problem.
Even when those customers had a seamless experience in resolving their inquiry or problem, most of those customers had lower Csat because they wanted their inquiry or problem to be resolved using only one touchpoint.
Most SQM call center clients do not feel their touchpoints are fully integrated. However, 43% of their customers who use multiple touchpoints to resolve the same inquiry or problem thought it was a seamless experience. Given that the touchpoints are not fully integrated, SQM has concluded that the manner in which the agent handled the call made the customer feel it was a seamless experience.
However, the goal of the omnichannel experience is to have customers feel like it is a seamless experience when using two or more touchpoints to resolve the same inquiry or problem.
3. Self-service Touchpoints
Touchpoint, also known as a contact channel and is commonly used interchangeably. SQM's CX research shows that when customers use a touchpoint, 93% of customers expect their call to be resolved on the first contact. Moreover, our research data reveals that when customers use only one touchpoint (e.g., call center, website, IVR, chat, or email) to resolve their inquiry or problem, Csat's top box response is 73%. However, Csat drops 19% for each additional touchpoint used to resolve the same inquiry or problem.
The main reason why Csat is so much higher when there is only one touchpoint used is the customer effort was low for resolving their inquiry or problem compared to the customer effort required when using two or more touchpoints.
Improving Csat for customers using multiple touchpoints to resolve the same inquiry or problem will be one of the most significant improvement opportunities for call centers for many years to come.
Furthermore, the figure below shows multichannel versus one-channel CX for ease of effort in resolving an inquiry or problem. SQM Research data shows that only 27% of customers felt that very low effort was required for contact resolution when using two or more touchpoints to resolve the same inquiry or problem. However, when customers used only one channel to resolve an inquiry or problem, 42% felt very little effort was required to resolve their interactions.
For those customers who had a multichannel experience, 22% felt that the effort required to resolve their inquiry or problem was either high or very high. In addition, many customers feel that they need to take ownership to resolve their inquiry or problem because they do not think anyone working at the organization will take ownership. As a result, the effort required for the customer to resolve their inquiry or problem is high or very high.
CX Touchpoint Delivery Differences
There is much confusion in identifying the differences between one-channel, multichannel, and omnichannel from a CX point of view. Essentially, it comes down to how many touchpoints are used, the ease of effort, and the depth of the integration between touchpoints. However, to help clarify CX differences between the touchpoint operating practices, SQM has developed the following definitions:
- Omnichannel delivery is when a customer has a seamless experience using multiple touchpoints to resolve the same inquiry or problem. Put differently, when a customer used multiple touchpoints to resolve the same inquiry or problem, they were able to pick up from where they left off in the previous touchpoint and, as a result, did not have to start from the beginning.
- Multichannel delivery is when a customer does not have a seamless experience when using multiple touchpoints to resolve the same inquiry or problem. In other words, the customer had to start their interaction from the beginning each time they used another touchpoint to resolve the same inquiry or problem.
- One-channel delivery is when a customer resolves their inquiry or problem using only one channel. At SQM, we describe this scenario as One Contact Resolution (OCR). The OCR metric measures the percentage of customers who resolved their inquiry or problem on their first contact and did not use another touchpoint before or after using the call center. It is essential to understand using one touchpoint to resolve an interaction is what customers expect.
4. Artificial Intelligence
The emergence of call center artificial intelligence (AI) will drastically transform all aspects of operations and customer interactions through automated and personalized service. AI will help reduce operating costs while meeting customer needs for low-complexity interactions.
AI enables computers to recognize multiple human languages and processes the words expressed, and it then determines how to respond back in the most Natural Language Understanding (NLU) possible. Conversational AI begins when a customer interacts with a company's touchpoints, such as online, on social media, and over the phone.
In recent years conversational AI (sometimes called chatbots or virtual agents) has been implemented in many call centers and will likely be used in the majority of call centers in the near future. However, despite all the conversational AI advancements, it is still recommended when using chatbots to use it with agent support, especially for complex calls, versus using chatbots replacing agents for all call types.
AI can also be helpful in using the interactive voice response menu for routing calls to the right agent that can resolve their inquiry or problem. It can also be used for handling FAQs so agents can handle more complex inquiries and problems; however, if the chatbots can not answer the customer's question that an agent is available to help the customer. Put differently, conversational AI complements your agents, not totally replaces them.
AI is also used for sentiment analysis to determine Csat, customer retention, and agent coaching. For example, sentiment analytics flags negative customer interactions so that call center supervisors can jump in on the call to help an agent resolve an inquiry or problem to ensure it is a positive CX. Moreover, sentiment analytics can be used to coach agents to improve their CX delivery.