Speech analytics in call centers is a powerful tool used across industries. This practice uses speech analytics to analyze customer-agent conversations for an improved understanding of customer pain points and potential call center improvements. While speech analytics has been used since the early 2000s, recent AI capabilities have introduced more efficient and accurate data analytics based on live call speech analytics. 

Because we know that customers largely prefer call-to-chat, this level of detailed analysis is necessary for call center optimization due to the complexity of spoken human language. The speech patterns, region-specific words and phrases, and emotional cadence of a conversation greatly impact the success or failure of a customer service interaction. The more quickly a conversation can be analyzed for quality assurance, proper call routing, and ensuring regulatory compliance, the better equipped your call center will be to continue serving customers with efficiency.

Understanding Speech Analytics Benefits

Understanding and optimizing contact center analytics is a huge consideration for every organization with a contact center. Contact center analytics considerations range from optimizing your infrastructure, using Business Intelligence tools, and keeping track of essential contact center metrics for accurate reporting. However, all of these analytics components will be obsolete if your contact center speech analytics capabilities are insufficient. If you want to accurately gather contact center data, a robust speech analytics foundation is the way to achieve it. 

Additional speech analytics benefits include:

• Enhancing Customer Experience: Positive customer experience is the center of all contact center success, which is why real-time insights for adjusting interactions in the moment are so important. The more responsive to customer needs your agents can be through AI speech analytics, the quicker calls will be resolved and satisfaction will improve. 
• Improving Agent Performance: While speech analytics are used primarily to assess the satisfaction of customers, agent performance analysis is a lesser-known but equally important use of speech analytics. Speech analytics can identify team member training needs, monitor compliance, and give agents the tools to consistently perform to the best of their ability.
• Providing Actionable Business Insights: When customer experience and agent performance are enhanced with speech analytics, your business will experience greater success overall. When speech analytics capabilities are used consistently and accurately, broad business data can be gathered and used for marketing strategy and product offering improvement based on customer feedback trends.

Key Speech Analytics Metrics and Features

Speech analytics needs are different for every organization based on company size, current business goals, and specific customer needs. There is plenty of room for customization after establishing a speech analytics foundation, but this list details some of the most important KPIs for businesses to track with their speech analytics capabilities:

Common KPIs to Track Using Speech Analytics

• Call Sentiment: This KPI measures emotional tone to understand customer satisfaction—are customers quiet, irritated, upbeat, or talkative? 
• Script Adherence: Many call centers have scripts that are used by call center employees to ensure they adhere to the organization’s voice and tone to maintain consistency across customer service interactions. Speech analytics can track how often this script is followed or departed from during calls.
• Keyword Frequency: In addition to scripts, companies have certain keywords and phrases that are important for customers to hear and associate with the organization’s customer service. Speech analytics is a helpful way to measure the frequency with which these keywords are used, and how well call center employees are able to incorporate those words and phrases into regular speech patterns.
• Call Silence: While the words used on a call are important, what goes unsaid is equally important. Long moments of silence on a contact center call may indicate a lack of understanding between the representative and customer, slow technology processes, or additional issues that can contribute to a slow or negative customer experience. 
• Customer Effort Score (CES): This score gauges the effort customers put in to resolve their queries—do your customers interact with your contact center from a posture of resignation, assuming their problems can’t be solved? If so, this highlights a need for your call center to communicate its capabilities more effectively from the outset.  

These KPIs are clearly useful, but how does speech analytics actually track these performance measures?

Important Speech Analytics Features

• Keyword Spotting: KPIs are determined by tracking specific keywords or phrases that indicate issues or opportunities. If a certain keyword is used consistently in the same type of customer instance, it will be flagged by your speech analytics software as a signal that it may be time to rework that aspect of your contact center process. 
• Emotional Tone Analysis: This feature is directly related to the call sentiment KPI, in which your AI speech analytics service is able to analyze changes in tone to detect customer frustrations or satisfaction.
• Real-Time Alerts: Customers interact with contact centers for hundreds of reasons and in hundreds of scenarios. Due to the variables at play, calls can escalate unexpectedly, and agents may need assistance in diffusing a situation or work through compliance issues. Real-time alerts from your speech analytics software will ensure your agents are never left stranded in a tough situation, and that your speech analytics call center management is always made aware of potential issues when they occur, not after the fact.

Implementing Speech Analytics in Call Centers 

Speech analytics in call centers is clearly crucial, but beginning the process of implementation can feel daunting. The good news is that the implementation process only requires a look at the existing elements of your business—and on the other side, your contact center speech analytics will be headed toward more efficiency and optimization. 

Step-by-Step Guide to Integration

• Assess: Gain a clear understanding of your contact center’s current state before you make plans for where you want to be. Evaluate your current technology stack to determine current compatibility with available speech analytics tools. Ideally, your contact center speech analytics software will easily integrate with your existing tech stack. 
• Define: Your leadership team and other stakeholders will then define clear objectives for contact center speech analytics success, as well as top-priority KPIs.
• Implement: Before fully jumping into your new speech analytics tool, test the chosen solution in phases for smoother adoption.
• Train: Contact center teams will be interacting with the chosen speech analytics service day-in-and-day-out. Ensure buy-in by engaging agents and supervisors throughout training and education. 

Once speech analytics is fully integrated into your contact center, make a plan for regular system evaluation and optimization. This is not a set-it-and-forget-it system, but rather one that requires attention and monitoring to ensure every piece of data is being tracked efficiently and accurately. Plan to regularly evaluate performance data to ensure alignment with overall business goals and make any necessary adjustments. For example, you may find that while your speech analytics platform is serving your organization well, there is space to explore further AI integration and capabilities.

Contact Center Speech Analytics Challenges 

Speech analytics is a service nearly every industry has adopted for their contact centers. This service provides tangible improvement for customers, agents, and overall business goals for operational efficiency. In its ideal state, speech analytics give contact centers the ability to run smoothly and seamlessly. But roadblocks can occur as a result of speech analytics:

• Data security: Gathering data from live conversations is one of the most effective ways to understand customers, but it also introduces additional data security concerns that must be taken into account. When you’re shopping for a speech analytics service, work with your IT and Security teams to prioritize a service with stringent security standards, and conduct regular security checks to ensure customer information is never compromised. 
• Integration issues: Choosing a speech analytics for call center service without full awareness of its integration capabilities can lead to dead ends during implementation. Your speech analytics tool will only be effective if it enables your existing systems to continue running seamlessly. 
• Data storage requirements: Using speech analytics means your contact center will be collecting and storing large amounts of data every single day. Without proper planning, data storage can become a concern quickly. 
• Compliance requirements: While storage space is a concern, so is data storage compliance. Considerations for maximum storage time and redacting personally identifiable information or sensitive personal information from your records is crucial for compliance.
• Staff training: Your team’s ability to effectively interact with the speech analytics service can make or break the success of your data reporting. Mitigate this risk by conducting thorough and regular staff training.

Future Trends in Speech Analytics

Speech analytics is only growing more popular and advanced by the day, and contact centers are eager to see what new capabilities may emerge to increase productivity in the future. 

Current emerging technologies in speech analytics include sentiment analysis advancements, natural language processing (NLP) and integration with AI-driven solutions. Contact centers will continue to benefit from new capabilities that are highly focused on improving customer sentiment understanding, creating a more personalized experience for customers from the start to end of a call. 

In the future, contact center experts are predicting further potential for enhanced customer personalization, predictive analytics to aid agents during calls, and real-time feedback for quick decision-making.

Conclusion

The future of speech analytics for contact centers is bright. With an emphasis on live AI-feedback and increased customer sentiment understanding, the Kenway team sees a clear path for every organization in any sector to optimize their capabilities with contact center speech analytics. 

No matter the platform or method of implementation your organization chooses, remember that prioritizing enhanced customer experience and agent performance will set your company apart and keep you ahead of the pack time and time again. 

The Kenway team is proud to provide tailored contact center strategies for organizations at any stage of contact center optimization, with expert insight from both contact center and Artificial Intelligence experts. Whether you are just beginning to consider speech analytics for your contact center or you simply need to revamp your existing service, reach out today to start a conversation! 

FAQs

What are speech analytics call centers? 

Speech analytics in call centers is the use of AI and call monitoring to analyze conversations between customers and service agents. 

What are the metrics of speech analytics?

Speech analytics metrics include call sentiment, call duration, call resolution rates, and customer effort score (CES).

What is the difference between voice and speech analytics?

While voice analytics technically refers to the cadence and tone of a voice and speech analytics refers to the actual words spoken, the term speech analytics is commonly used to refer to both.