Speech Analytics: What It Is, Applications, and Examples

What is Speech Analytics?

Speech analytics is a process of analyzing voice recordings, customer calls, etc. Speech recognition software can analyze these calls and recordings. Speech analytics software detects stress and emotion in the speaker's voice. Speech analytics can improve customer experience through the correct software.

Types of Speech Analytics

The two types of speech analytics are discussed here in detail.

Real-Time Speech Analytics

Real-time speech analytics analyzes live voice calls and audio. Agents can access actionable insights, trends, and metrics to improve customer interaction. Real-time analytics help agents enhance the customer experience by knowing their tone and sentiments.

Post-Call Speech Analysis

Post-call speech analytics provides details of a call after it ends. The insights include the keywords used in the conversation. The insights also help to manage custom text classification models for making processes and strategies to improve customer experience.

How does Speech Analytics work?

Speech analytics uses three steps to work discussed below ?

Data Processing

Speech analytics combines different artificial intelligence technologies listed below ?

• Automatic Speech recognition
• Natural Language Processing
• Machine learning
• Transcription
• Tonality-based Sentiment Analysis
• Algorithms

All these technologies analyze and process human speech.

Analysis

After the data processing, speech analytics works on customer sentiment, which can be positive, negative, or neutral. Sensitive information like bank account numbers, credit or debit card numbers, social security numbers, etc., are hidden.

Insights Generation

Reports are created through various parameters like agent performance, call quality, compliance monitoring, sentiment, etc. These insights help to ensure that business goals are met.

Speech Analytics vs. Voice Analytics

Speech analytics and voice analytics have a few differences available in the table below ?

Speech Analytics Use Cases

The use cases of speech analytics are as follows ?

Know about competitors and customers and get insights

Call centers may not be able to handle the calls of all the customers, but considering these calls can provide data about customers because ?

• They compare prices
• They use products
• Efficiency and accessibility of customer service
• Speech analytics reduces average handling time

Speech analytics helps remove delays and bottlenecks for handling calls without any delay. It also helps to categorize the calls and prioritize the critical calls.

Agent performance improvement

Speech analytics software helps analyze the areas where the agents need training. These insights help to arrange personal coaching to enhance the skills and performance of the agents.

Voice analytics monitors the performance of the agents. Call centers use key performance indicators to ensure that agents complete their targets.

Gathering Feedback

Speech analytics analyzes the calls and gathers data on what people say during the call. This data helps to provide feedback to the agents without any questionnaire. The combination of AI and speech analytics helps detect the tone of customers to analyze whether they are happy or not satisfied.

Identify options for selling

Speech analytics helps to look for opportunities for upsell and cross-sell. The conversation analysis with the customers helps detect keywords to create interest so that customers purchase products or services.

Quality Management

Speech analytics software applications help call centers ensure that agents attend the call and provide services for customer satisfaction. The supervisors listen to the recorded calls for quality assurance.

Benefits of Speech Analytics

The benefits of speech analytics are listed below ?

• Improved customer experience
• Better understanding of customer needs
• Monitoring customer interactions for immediate issue resolution
• Identify areas for agents' training
• Provide feedback and coaching for agents
• Automating processes to resolve call issues
• Identify customers' trends and preferences
• Process automation reduces operational costs

Conclusion

Speech analytics is a technology that helps to improve customer satisfaction. Call analysis lets the authorities know whether customers are satisfied or not. The analysis also tells about the areas of agents' improvement and training. Speech analytics uses artificial intelligence to perform all the tasks. Speech analytics also examines customer interaction and extracts meaningful information.

Speech Analytics FAQs

1. What are the four parts of speech Analytics?

The four parts of speech analytics include supervisors, reporting, agents, and automation.

2. Is speech analytics compatible with multiple languages?

Yes! Many speech analytics systems are there that can work with multiple languages. Accuracy depends on the recording of a specific language.

3. What factors should be considered before choosing speech analytics software?

Consider the following factors before choosing the speech analytics software ?

• Accuracy and performance
• Analysis capabilities
• Flexibility
• Customization
• Data security
• Cost of the software
• Return on investment

4. Why speech analytics is important for call centers?

Speech analytics is important for call centers due to the following reasons ?

• The entire process of transcription to analysis is automated
• Continuous improvement in voice call transcriptions
• Detailed reports related to the performance of the call center
• Analysis of each agent's performance through a scorecard
• Supervisors can organize training sessions to improve the performance of agents