What is Speech Analytics?
Speech Analytics is a technology that transcribes and analyzes phone calls between customers and call center agents. This technology has the ability to scan transcriptions using natural language processing for keywords and phrases while listening for the sentiment behind those words, such as anger or frustration, often referred to as Sentiment Analysis.
There are two types of Speech Analytics: Post-Call and Real-Time. Depending on the use case, these can function independently or together to improve business outcomes and customer satisfaction. If there isn’t a business need to track in real time, call recordings can be transcribed post-call and used for both business intelligence and quality management purposes.
Speech Analytics Tools
Speech Analytics can be used across omnichannel organizations - those supporting multiple communication channels like voice, chat, and email. Speech Analytics Solutions can even be used in self-service applications, known as chat bots.
Text Analytics
Text Analytics can be applied to any written communication, including email, chat, and social media. Chat bots can also use text analytics, the process of translating unstructured text into data, to both understand customer inquiries and post reply messages to customers. Text analytics can also incorporate sentiment analysis based on the words being used.
Voice Analytics
Voice Analytics functions similarly to text analytics but first translates audio speech to text. Some applications can perform sentiment analysis by identifying speaker emotion and intent by analyzing audio patterns. Voice Analytics is also often referred to as Speech Recognition.
Most contact centers already have a telephony platform or ACD (Automatic Call Distributor) that assigns the inbound calls to agents through the use of an IVR (Interactive Voice Response) which may utilize Voice Analytics in limited capacity. While these systems typically provide call analytics, call center metrics like AHT, Service Level, etc., and can be used to produce a basic scorecard to managers, this data pales in comparison to the insights that can be gained through a full-fledged voice analytics tool.
How Can Speech Analytics Be Used?
Artificial Intelligence can be used to automate many functions that were performed by managers or team leaders. These functions can include agent performance and quality assurance which can save management teams time. As you can imagine, the benefits of speech analytics can scale with the business needs and call volume, as it doesn’t require the same amount of human resources to scale along side to account for the additional volume.
Traditionally, Voice of the Customer (VoC) has been gauged by customer surveys. This method is often unreliable because it requires the customer to spend time providing feedback, often with low response rates. If the survey is lengthy, the customer may just end up checking an answer, rather than reading the entire question all the way through, or give up on the survey entirely. This makes VoC data somewhat unreliable. Automated sentiment analysis that tracks real customer sentiment does not share these weaknesses and can quickly identify trends in customer needs and even shed light on the root causes behind customer churn.
It’s even possible to build Speech Analytics into CRMs to reduce agent workload, ultimately improving efficiency.
Speech Analytics for Smaller Contact Centers
Smaller Contact Centers often have more limited resources - that also includes human resources. Often supervisors in call centers have to perform tasks allocated to dedicated teams in larger centers, including sifting through calls to be graded for call quality. This time spent listening to calls is time not spent coaching agents for better performance.
Speech Analytics Software can analyze every customer interaction to improve the customer experience by automatically searching through customer calls and providing actionable insights to optimize the customer journey.
Speech Analytics for Larger Contact Centers
Many of the same features that benefit smaller Contact Centers also benefit larger Contact Centers. Scaling up with AI and Speech Analytics is both easy and efficient. AI can account for the larger volume efficiently without needing to have additional human resources to listen and grade calls manually.
Larger contact center organizations often have more initiatives scaling at the same time and need to scan for more insights than a smaller operations. Having the ability to set up AI to automatically surface performance insights on upselling or cross-selling initiatives could save both a lot of time and manual labor.
AI also has the ability to scan 100% of the call volume, which is not feasible with traditional methods.
Using sentiment analysis in a larger organization can help determine customer and workforce engagement. Understanding how satisfied both customers and employees are fosters an environment that is focused on retention. Using sentiment analysis to scan for negative or positive sentiments can track how customers and employees are feeling. Knowing how your customers are feeling is important but it’s worth noting that how your employees are feeling is just as important, if not more important. Effective employee engagement creates a better work culture, increases productivity, reduces turnover, and builds better customer relationships. Building better customer relationships boosts brand loyalty. Ultimately focusing on retention builds a better customer experience which leads to business growth.
How can Happitu Help your Contact Center?
See how Happitu Vision can help your Contact Center, big or small, with our Conversation Analysis tools and schedule your free demo today!
Related Posts
