Modern executives can access more data than ever before. Yet, doing so is not always easy.
Consider the contact center; many struggle with capturing and processing customer voice calls.
The inability to do so and analyze voice interactions limits the capacity of organizations to understand customer struggles, sentiment, and intent.
Luckily, speech analytics software is improving, turning call recordings into a well of business insight.
Now, the technology can even work in real-time, enabling quick, data-driven decisions.
A Definition of Speech Analytics Technology
Speech analytics utilizes automatic speech recognition, natural language processing (NLP), and other AI technologies to convert audio recordings into text and then actionable insights.
Some variants of speech analytics technology can also work in real-time to generate insights even as the call is ongoing.
Yet, no matter whether the speech analytics technology offers real-time or historical analysis (or both), the technology consists of three central components:
- The phonetic technique employs the phoneme as the fundamental recognition unit to transform unstructured conversations into a stream of language constructs that can be searched.
- Large-vocabulary continuous speech recognition (LVCSR) uses words or a collection of terms to match the audio and identify problems – LVCSR commonly drives speech-to-text conversion.
- Extended speech emotion recognition is the most complex of the three, employing neural network classifiers to assess emotion and provide predictions.
According to Reports and Data, the technology will have a $4.24 billion global market by 2026 as businesses seek new opportunities to better understand their customers.
How Does Speech Analytics Technology Work?
Speech analytics harnesses AI to perform the following tasks to transform raw audio into actionable insight.
- First, the solution gathers audio conversations from a data source – such as a call recorder or VOIP stream – and collects relevant metadata. This may include the agent and customer’s names, the time of the call, its duration etc.
- The audio passes through the speech recognition process, where sounds are converted to text.
- Simultaneously, the solution detects audio cues such as tension and silence while transforming text transcripts into a uniform format.
- Some solutions may consolidate data from multiple channels and voice information. As such, they may then assess 100 percent of contact center interactions.
- Finally, the system automatically analyses the interactions for specific language patterns to classify or tag contacts as possessing a particular language or set of features. This enables automatic contact scoring within quality management systems, paving the way for the measurement and visualization of several KPIs. Customer sentiment, agent quality, and compliance are all excellent examples.
Through this process, speech analytics may ultimately extract vital business knowledge that the company would lose via random call sampling.
Key Features of Speech Analytics Technology
Various speech analytics vendors provide many value-added features that boost contact center performance. These often include:
- A Data-Visualization Dashboard – Users can typically view several conversational friction points, including supervisor escalations, compliance infractions, and periods of silence. By dashboarding these, supervisors can zoom in on learning opportunities.
- Automation Opportunities – Speech analytics may provide opportunities for automation. For instance, lengthy intervals of inactivity during calls can trigger agent prompts.
- Upselling and Cross-Selling – Speech analytics reveals how often agents attempt to upsell and cross-sell. It may also estimate success rates. Moreover, many real-time systems can detect ideal upselling and cross-selling opportunities and notify the agent.
- Phrase Detection – In many operations, agents often say particular statements to ensure compliance, and speech analytics can track whether they do so. Moreover, brands can use phrase detection to look for patterns in positive interactions. They can then highlight best practices and coach these to the broader contact center population.
- Talk Time Reports – This feature examines trends within talk times – alongside hold time and transfers. In doing so, it can identify patterns between agents, teams, and call types, highlighting where coaching and increased support is most necessary.
- Real-Time Prompting – Automated speech analytics may provide real-time desktop instructions throughout every conversation. These can include next best actions – such as upselling suggestions – and customer insights, possibly including sentiment. When integrated with a knowledge base or CRM, it can even surface customer information at relevant points to save agents from scouring contact center systems.
Benefits of Using Speech Analytics
By deploying speech analytics technologies, businesses may secure the following benefits.
- Targeted Agent Coaching – Traditionally, contact centers evaluate two to six conversations per agent every month to assess their performance. They select these randomly. However, with speech analytics, evaluators can zoom in on the conversations likely to deliver the best learning and praise opportunities. Speech analytics-driven indicators such as customer sentiment, periods of silence, and transfers are helpful here.
- Insights Into 100 Percent of Customer Conversations – Automated call transcription and analysis deliver insights into every customer conversation. Harnessing all this data, contact centers can gain a complete view of contact center performance and make better-informed decisions.
- Enhance Customer Service Experiences – Sentiment analysis enables teams to examine the drivers of positive customer interactions. Noting these and building them into induction training and coaching manuals will help drive better customer conversations.
- Assess the Performance of Automated Systems – Speech analytics can look for concerning trends in bot-driven conversations through customer sentiment analysis, shedding light on dead-ends and other issues.
- Monitoring Agent Wellbeing – Alongside measuring customer sentiment and stress levels, speech analytics can detect trends in agent sentiment. It may even alert supervisors, who can address the issue with a quick intervention or by offering the agent a break.
Getting Started with Speech Analytics Technology
Speech analytics deals with sophisticated AI algorithms, which most businesses cannot develop in-house. Luckily, leading providers – whether contact center software vendors or point solution specialists – increasingly offer speech analytics tools.
Indeed, leading vendors like Genesys, NICE inContact, and Talkdesk have a speech analytics module. Meanwhile, some AI specialists like CallMiner and Contexta360 offer interoperable speech analytics technology that can sit on top of any existing contact center stack.
Finally, brands can partner with their carrier (e.g., Verizon) to couple speech analytics with telephony.
Eager to unearth more use cases of speech analytics technology? Watch our video: 10 Speech Analytics Use Cases for the Contact Centre and Beyond
