AI-Powered Call Analytics Solution Transforming Conversations into Actionable Insights
Introducing Vārtā
In today's data-centric business environment, quantifying customer interactions is crucial. Vārtā delivers empirical insights that yield measurable improvements in customer satisfaction metrics, operational KPIs, and overall business performance. By harnessing machine learning algorithms, we enable organisations to uncover latent opportunities, optimise processes, and gain a data-backed competitive advantage.
Key Components of Vārtā
Vārtā employs artificial intelligence to convert raw call and text data into quantifiable insights. By analysing each interaction, we provide businesses with a data-rich understanding of customer behaviour, agent performance metrics, and call centre efficiency indicators. Vārtā transcends traditional analytics by dissecting the nuances of human dialogue, empowering businesses to make evidence-based decisions that directly impact the bottom line.
Voice Analytics
Analyse audio for emotions, intent, and call quality
Text Analytics
Process written communication to understand customer needs.
Sentiment Analysis
Track customer and agent sentiment, identify shifts.
Executive Dashboard
Visualize key metrics through customizable dashboards.
Operational Efficiency
Automate tasks, improve agent performance, provide data-driven insights.
hOW IT WORKS?
Vārtā utilises a sophisticated algorithmic process to extract meaningful data from call recordings. By integrating speech-to-text conversion, sentiment analysis algorithms, and advanced topic modelling techniques, we generate a comprehensive dataset of customer interactions. Our predictive analytics capabilities leverage machine learning to forecast customer needs and recommend optimal next steps, while our NPS and multi-language sentiment analysis provide a global, quantitative view of customer satisfaction.
Speech-to-Text
: Converts audio to text for in-depth analysis.
Sentiment Analysis: Determines emotional tone and tracks sentiment shifts.
Advanced Topic Modeling: Identifies recurring themes and subjects.
Predictive Analytics: Anticipates customer needs and suggests optimal actions.
NPS & Multi-Language Sentiment Analysis: Measures customer loyalty and analyzes sentiment across languages.
How do we train our Ai model
Our AI models are trained on an extensive dataset of call recordings to ensure statistically significant and reliable insights. Through deep learning from vast conversation datasets, our models develop a nuanced understanding of human language, enabling them to identify patterns, trends, and customer sentiment with high accuracy. This robust training process ensures Vārtā delivers actionable, data-driven insights that yield tangible business results.
Caller Intent
Distinguish between sales leads, existing customers, etc
Caller Interest
Identify specific products or services of interest
Conversation outcome
Determine purchase, booking, quote, or cancellation
Call events
Recognize callbacks, escalations, and other key moments.
Voc Insights
Detect pricing inquiries, product discussions, competitor mentions, and complaints.
Business Outcome
By leveraging AI algorithms, Vārtā delivers quantifiable business outcomes. From increasing sales conversion rates and improving customer satisfaction scores to optimising operational efficiency metrics and reducing customer churn percentage. Vārtā empowers businesses to achieve data-driven goals. By uncovering statistically significant opportunities, identifying areas for improvement, and providing actionable, algorithm-based recommendations, we help businesses thrive in today's data-driven market.
The financial services sector faces complex customer interactions and stringent regulatory requirements. Our bespoke solutions address the unique challenges of financial institutions, enabling them to deliver exceptional customer experiences while mitigating risks through data-driven insights. By leveraging Vārtā, financial institutions can enhance customer loyalty metrics, and drive revenue growth through data-backed strategies.
Retail Banking
Enhance customer service improve cross-selling
NBFCs
Improve collections
Enhance retention
Mutual Funds
Provide personalized advice improve onboarding
Enhance compliance
