Speech analytics is a technology that analyzes telephone calls between customers and call center agents, providing intelligence to organizations that can improve CX and drive efficiency.
This technology has the ability to listen for keywords, phrases and voice attributes -- such as anger and frustration. It then analyzes the information against a specific set of rules to identify calls to flag for follow-up action or additional analysis. This can help businesses improve products and services, improve internal processes and enhance agent skills.
However, speech analytics is not just a call center tool. It is an enterprise tool that enables businesses to get complete look at their products and services to benefit many departments across an organization.
Types of speech analytics
There are two types of speech analytics: post-call and real-time. While organizations can use each independently, a combination of the two can improve CX and efficiency.
Post-call analytics performs analysis on interactions that have already occurred. It can analyze specific details of an individual call or can analyze a group of calls, looking for specific patterns and trends.
For example, post-call analytics may analyze a large volume of calls and listen for the use of specific keywords, such as, "I cannot connect," which may help identify the root cause of a product/service issue.
A benefit of post-call analytics is that businesses can increase the number of calls monitored and reviewed without a corresponding increase in quality assurance staff.
Real-time analytics performs analysis on interactions as the call is happening. This technology listens for keywords and phrases, or for specific voice tones and inflections. Businesses also tend use this form of speech analytics less frequently than its post-call counterpart.
Businesses can use real-time analytics software to identify calls where a customer uses specific keywords -- such as, "This is the third time I am calling regarding this issue" -- or has a voice inflection that identifies they are frustrated with the interaction. The call can then be proactively escalated to an individual who may be better suited to resolve the customer issue.
A benefit of real-time analytics is the potential reduction of compliance risk as a result of identifying calls where agents aren't following specific scripts and rectifying the issue before the completion of the phone conversation.
Improves customer experience
A big benefit of speech analytics in call centers is the improvement of CX. This technology points to areas of weaknesses and enables contact center managers to work with agents to better serve customers, which can then increase customer retention, loyalty and spending, and lead to improved revenue in an organization. Speech analytics can improve customer experience in the following ways:
- increase quality of products and services leading to higher levels of customer satisfaction;
- increase customer trust, driving the likelihood of expanding purchase behavior with an organization;
- improve customer satisfaction from higher first contact resolution;
- reduce customer frustration with the ability to route calls to an individual who can resolve the issue;
- improve breadth of product and service offerings as a result of knowledge of the competitor landscape; and
- improve up-sell opportunities as a result of understanding a caller's attitude during a phone interaction.
Speech analytics can also improve efficiency in call centers by reducing expenses and lead to improved business profitability. Examples of improved efficiency include:
- increased volume of automated call monitors;
- reduced volume of phone calls as a result of root cause analysis;
- reduced compliance risk from agents not following required scripts or providing incorrect information on critical transactions;
- focused process improvements to help allocate resources where most effective;
- improved access to knowledge management and assisting agents to respond to customer inquiries more effectively; and
- identification of situations where self-service is not working as planned and identifying additional opportunities to provide self-service.
Speech analytics is becoming more common across organizations and enable the entire enterprise to listen to the actual voice of the customer.