Behind the Scenes: Leveraging SLU to Improve Customer Service
In this age of information, the most important asset that enterprises rely on is data. With rapid improvements in data analysis and visualization techniques, it has become the norm for enterprises to leverage the power of data for streamlining and improving business processes.
However, what we don’t often realize is that contact centers can prove to be one of the most important sources of data for enterprises. The thousands of hours of call recordings are a storehouse of information for consumer attitudes, complaints, and feedback that enterprises can use to gain valuable insights.
But how to go about it? The answer lies in the burgeoning field of speech analytics.
Gartner says “Audio mining/speech analytics embrace keyword, phonetic or transcription technologies to extract insights from prerecorded voice streams. This insight can then be used to classify calls, trigger alerts/workflows, and drive operational and employee performance across the enterprise.”
Intelligent solutions like Skit’s Digital Voice Agent can not only handle customer service calls but perform advanced speech analytics in the very near future.
Using advanced Spoken Language Understanding (SLU) algorithms, the recorded speech in contact centers can be analyzed to extract crucial insights that can help enterprises streamline their performance.
Read on to know more about the three ways in which speech analytics with SLU can help your enterprise.
Provide personalized services
With a continuous focus on innovation, Skit.ai has added the revolutionary “idiolect” layer to existing cutting-edge capabilities. In the world of linguistics, “idiolect” simply means the unique speech style of a group of people that differentiates them from other groups.
The state-of-the-art technology in the idiolect layer will enable VASR to perform advanced speech recognition and analytics to uncover more information about the speaker such as gender, age, language, and accent- and build a unique speaker profile.
Moreover, the application of certain SLU algorithms can help can further insight into the customer’s attitude and state of mind:
- Sentiment Analysis: These algorithms can detect whether the customer’s attitude is positive, negative, or neutral during the call.
- Emotion Detection: Such algorithms can help determine the emotions of a customer and their state of mind during the call.
With the combined help of unique customer profiles and SLU-enabled analysis of customer’s speech, it becomes easier to deliver personalized services to the customer- depending on their characteristics and current state of mind.
Research by Epsilon has indicated that 80% of consumers are more likely to make a purchase from a brand that provides personalized experiences.
With hyper-personalized customer service experiences, you can keep your customers satisfied and reduce customer retention costs in the future.
Gather consumer insights
With multiple agents handling multiple customers in a day, it is not possible for agents to always correctly determine what consumers want or expect. Moreover, customers themselves might often be confused as to what they expect from a brand and what improvements they want in the service or product they receive. As Steve Jobs had once famously quoted:
“It’s not the customer’s job to know what they want !”
However, it is crucial for any enterprise to determine the needs of their consumers to provide better services. With COVID changing consumer behavior and expectations, analyzing consumer insights can prove crucial to the path ahead.
“Businesses need to understand how this new world affects all of their touchpoints with the customer if they are to actively reinvent their own future and not be at the mercy of external events.” (PwC)
The advancements in research in Spoken Language Understanding have made it possible to use different techniques to derive important information from analyzing customer service calls. Some algorithms that can be used to derive such insights are:
- Topic Modeling: This is a technique in SLU with which customer calls can be analyzed to create a list of natural topics that frequently occur in service calls and can help companies realize what services/products frequently need troubleshooting and have scope for improvement.
- Text Summarization: The duration of calls might often be extremely long. With summarization algorithms, it can become easier to create summaries of calls that can be easily read through/analyzed for consumer insights.
- Aspect Mining: It refers to a class of SLU algorithms that discovers different aspects or features in data, and along with sentiment analysis, can be used to determine the different sentiments associated with those features. For example, in a customer call, the customer may express a positive sentiment when it comes to pricing but a negative opinion on customer service quality.
With easy access to consumer insights with SLU, enterprises can easily leverage them to make crucial decisions on how to improve business processes and products in a way that makes their customers happier.
Improve automated quality assurance
By harnessing the power of SLU, it not only becomes possible for Voice AI platforms to provide quality service but also to ensure that call quality is maintained at all times in contact centers- be it a service agent or a virtual agent.
Traditional QA teams depend on the right data to correctly analyze service quality and with contact centers handling an immense amount of calls, the process is bound to become time-consuming and even inefficient.
The use of SLU and speech analytics algorithms can provide structured insights by analyzing calls, which makes it easier for QA teams to act on those insights to streamline contact center processes for increased KPI metrics.
As brands continue to explore innovative ways of connecting with customers, they need to plug in AI technologies into their business processes to glean consumer insights that can be the driver to elevating customer experiences
Indeed, the future is undoubtedly bright for Voice AI platforms that can truly harness the power of Spoken Language Understanding. Even as we talk about these improvements, researchers are working to improve SLU and develop newer techniques that can have an even greater impact on Voice AI systems.
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