Customer Insights with AI Voice Analytics
https://dataplatr.com/ai-voice-analytics/
1. Revolutionizing Customer Insights with AI Voice Analytics
In recent years, the landscape of artificial intelligence has been dramatically reshaped by the advent of Generative AI and Large Language Models (LLMs). Generative AI, powered by advanced neural networks, can create human-like text, translate languages, and even write creative content. LLMs, such as GPT (Generative Pre-trained Transformer) models, have taken this a step further by understanding and generating human-like text with unprecedented accuracy and contextual awareness.
AI Voice Analytics, built on these foundations, is not just about converting speech to text. It’s about understanding the nuances, emotions, and intentions behind spoken words. When combined with LLMs, AI Voice Analytics can extract meaningful insights from conversations, opening up new possibilities for businesses to understand and serve their customers better.
2. Challenges of Voice AI
1. Manual Call Review Process
Traditionally, contact centers rely on manual review of call recordings, which is time-consuming, labor-intensive, and prone to human error and bias.
2. Real-time Insights and Decision Making
Contact centers often struggle to derive actionable insights from customer interactions in a timely manner, leading to delayed responses to emerging issues or trends.
3. Scalability of Quality Monitoring
As call volumes grow, it becomes increasingly difficult and costly to maintain comprehensive quality monitoring.
4. Identifying Customer Sentiment Trends
Detecting and understanding shifts in customer sentiment over time can be challenging, especially when dealing with large volumes of interactions.
5. Agent Performance and Training
Providing timely, specific feedback to agents for continuous improvement can be difficult, especially in large contact centers.
6. Compliance and Risk Management
Ensuring compliance with industry regulations and identifying potential risk factors in customer interactions can be challenging at scale.
7. Subjectivity in Analysis:
Human analysis of voice calls can be subjective and prone to biases.
3. Dataplatr’s Voice to Analytics Dashboard: A Game-Changing Solution
At Dataplatr, we’ve harnessed the power of the cutting-edge technologies to create a Voice to Analytics Dashboard solution that transforms how businesses interact with and understand their customer conversations. Our solution leverages the latest advancements in Generative AI, AI Voice Analytics, backed by state-of-the-art Open Source LLMs, to provide real-time, in-depth analysis of voice interactions.
3.1 Voice Call Processing:
1. Speech-to-Text Conversion
Using WhisperX, we convert voice data into timestamped, speaker-diarized transcripts with high accuracy.
2. Conversation Summarization
Falconsai/Text_summarization model distills lengthy conversations into concise, actionable summaries.
3. Topic Extraction
BERTopic identifies the main topics discussed in each call, allowing for easy categorization and trend analysis.
4. Sentiment Analysis
CardiffNLP/Twitter-Roberta-Base-Sentiment-Latest model analyzes the sentiment trends of each speaker throughout the call.
5. Agent Coaching
Gemini-Pro provides coaching points based on the conversation, helping improve customer experience.
Our solution is built on a robust infrastructure, with voice data processing done on Google Cloud Platform (GCP) and the final analytics reports hosted on Looker for easy access and visualization.
3.2 Voice KPIs Dashboard Development:
The KPIs are categorized into two sets:
1. Inter Call KPIs
These are Call metrics which are calculated across all the AI voice analytics within a business defined time window. These KPIs help understand the Overall outlook of the Calls and Calls data management.
Total Calls: The total number of calls analyzed.
Time Spoken: The total time spent speaking during the calls.
Time Spoken per Employee: The average time spent speaking per employee.
Sentiment Insights: Analysis of the overall sentiment expressed during the calls, including the most positive and most negative captions.
2. Intra Call KPIs
These are Call metrics that are calculated for each Call. These KPIs help drill down into each call for more granular insights on the Call.
Total Calls: The total number of calls analyzed.
Time Spoken: The total time spent speaking during the calls.
Time Spoken per Employee: The average time spent speaking per employee.
Sentiment Insights: Analysis of the overall sentiment expressed during the calls, including the most positive and most negative captions.
Call Transcript: A detailed transcript of the call, including timestamps, speaker information, and sentiment analysis.
Sentiment Trend (Overall): A visual representation of the overall sentiment trend over time.
Sentiment Trend (Per Employee): A visual representation of the sentiment trend for each individual agent.
Agent Mentoring Guidelines: Guidelines for agents on how to improve their interactions with customers.
4. Business Impact: Transforming Customer Interactions
1. Automated Voice AI Insights
Our Voice to Analytics Dashboard provides automated, real-time insights from every customer interaction. This eliminates the need for manual call reviews and allows businesses to quickly identify trends, issues, and opportunities across thousands of conversations.
2. Improved Agent Monitoring
With our solution, supervisors can efficiently monitor agent performance at scale. The system provides coaching points and identifies areas for improvement, enabling targeted training and continuous improvement of customer service quality.
3. Understanding Customer Sentiments
By analyzing sentiment trends throughout each call, businesses can gain a deeper understanding of customer emotions and reactions. This invaluable insight is particularly beneficial for large voice analytics call centers, and can drive improvements in product development, service delivery, and customer satisfaction strategies.
4. Visualization of Call KPIs and Sentiment Trends
Our Looker-based dashboard presents key performance indicators (KPIs) and sentiment trends in easy-to-understand visualizations. This allows managers to quickly grasp the overall performance of their customer service operations and make data-driven decisions.
5. Conclusion: Empowering Businesses with AI-Driven Insights
At Dataplatr, we believe that the future of customer service lies in the intelligent application of AI technologies. Our Voice to Analytics Dashboard solution represents a significant leap forward in how businesses can understand and respond to their customers’ needs.
By leveraging the power of AI Voice Analytics, LLMs, and advanced speech analytics we’re not just providing a tool – we’re offering a transformation. A transformation that turns every customer interaction into an opportunity for improvement, innovation, and enhanced customer satisfaction.
As experts in Data & Analytics Solutions with over 15+ years of experience, we at Dataplatr are committed to helping businesses harness the power of their data. Our AI Voice Analytics Dashboard is just one example of how we’re driving transformation, growth, and efficiency through tailored, cutting-edge solutions.
Ready to revolutionize your customer insights? Contact Dataplatr today and step into the future of AI-powered call center analytics. Curious and would like to hear more about this article ?
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