Verint is focused on driving multilingual CX outcomes with AI Interaction Analytics. Anil Chawla, Managing Director, Customer Engagement Solutions, Verint India, speaks exclusively to Rajneesh De, Group Editor, CXO Media & APAC Media, on how interaction analytics are part of a unified CX Automation Platform that connects insights directly to action.
What are the AI-based interaction analytics solutions available in the Verint India portfolio?
Verint offers AI-powered interaction analytics through its Customer Engagement Cloud Platform in India. These solutions analyse customer interactions across voice, chat, email, social, and messaging channels using speech and text analytics, NLP, and machine learning.
At the heart of this is Verint Da Vinci AI, which powers features like transcription, intent and topic detection, silence and overtalk analysis, compliance monitoring, and GenAI-driven call summaries. It also predicts churn, fraud, and CX gaps.
Interaction Analytics integrates seamlessly with Quality Management, Workforce Engagement, and Real-Time Agent Assist, enabling businesses to turn insights into action instantly. These solutions are designed for large-scale Indian enterprises and can be deployed on cloud, hybrid, or on-premise models.
How are these solutions helping enterprises analyse conversations?
Verint’s Interaction Analytics moves businesses from small sample-based reviews to comprehensive AI-driven analysis of customer conversations. It detects intent, sentiment, effort, and emotion using advanced NLP and machine learning.
This means recurring issues, compliance risks, and service failures can be flagged early—no more waiting for lagging indicators. Real-time analytics via Agent Assist guide agents during calls with next-best actions, while post-call summaries powered by GenAI boost productivity.
By embedding conversation intelligence into processes like quality, coaching, and workforce management, Verint helps organisations improve resolution times, customer satisfaction, and compliance—all while delivering measurable CX outcomes.
How many Indian languages does Verint’s interaction analytics solution cover? Can it handle hybrid languages?
Verint supports 11 Indian languages—Hindi, Punjabi, Marathi, Gujarati, Bengali, Tamil, Telugu, Kannada, Malayalam, Indian English, and Hinglish—with ongoing expansion based on customer needs.
The platform is built for code-mixed conversations like Hinglish, common in Indian contact centres. Context-aware NLP models focus on intent rather than rigid grammar, ensuring accurate sentiment and intent detection even when customers switch languages mid-sentence.
Continuous learning from real-world interactions makes Verint ideal for BFSI, telecom, and e-commerce sectors where multilingual conversations dominate.
Which sectors see the most traction for AI-integrated interaction analytics, and what are the use cases?
In India, BFSI, telecom, large BPOs, e-commerce, and utilities lead the adoption of AI-driven analytics. BFSI uses it for fraud detection, grievance analysis, and compliance. Telecom leverages it to reduce churn and improve upselling. BPOs use analytics to boost agent performance and meet SLAs.
For example, Tech Mahindra used Verint Speech Analytics and Quality Bots for Bank of Baroda, evaluating 100% of calls, improving quality scores to 92%, raising NPS above 50, and increasing sales conversions.
E-commerce and utilities apply analytics to fix delivery and billing issues proactively. The trend is clear: businesses are moving from reactive reporting to proactive CX improvement.
What are Verint’s unique differentiators over competitors?
Verint stands out with its outcome-driven CX automation approach—not just analytics. Interaction analytics are part of a unified CX Automation Platform that connects insights directly to action.
Powered by Da Vinci AI, trained on billions of real interactions, Verint offers “glass-box AI” for transparency and compliance—critical for regulated industries. Workflow automation is another edge: Verint automates call summaries, agent coaching, compliance checks, and quality scoring via Verint Bots.
Flexible deployment (cloud, hybrid, on-prem) and deep expertise in BFSI, telecom, and BPO further strengthen Verint’s position.
What are the key pillars of Verint’s GTM strategy in India?
Verint’s GTM strategy focuses on:
- Outcome-led AI adoption
- Vertical specialisation (BFSI, Telecom, BPO)
- Ecosystem partnerships (SIs, cloud vendors, outsourcers)
- Deployment flexibility
Instead of massive transformations, Verint promotes targeted automation of high-impact workflows and pilot-driven rollouts to prove ROI. Support for cloud, hybrid, and on-prem models aligns with India’s regulatory and data residency needs.
What challenges exist in deploying AI-integrated interaction analytics in India?
Key challenges include:
- Data readiness for AI
- Security and privacy concerns
- Infrastructure maturity (legacy systems, slow integration)
- Talent gap (AI adoption outpaces skilled resources)
Solutions: Elastic architectures for easy scaling, AI models that learn continuously, strong data governance, and embedding analytics into daily workflows. Upskilling teams and automating processes can make adoption practical and sustainable.
