Conversation Analytics

Conversation Analytics

See how Finarb is partnering with Pharma Clients to help them achieve Call Center Compliance

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Engage with your customers with AI driven insights and state of the art models for higher customer satisfaction and efficient and effective internal processes. Speech to Text models that convert conversations in real time or through batch processing, and AI scoring systems that guide next-best actions, help drive higher NPS scores and better call centre agent performances

Using SOTA deep learning models to identify the latent intent of phrases and text in general. This sets the path for real time conversational AI

Intent Detection

Using MFCC features identify and classify the different languages, accents, tonality of speakers. This sets the tone for further sentiment and emotion analysis

Accent And Language Model

Post de-noising and diarization, context based models of entities and surrounding sentiments around the entities help us capture the speakers mood.

Sentiment & Emotion Analysis

Create scorecards for post-hoc analysis, compliance and regulatory requirements. Also needed to alter set call scripts and refine processes

Call Scorecard

Using reinforcement/transfer learning a self learning algorithm is deployed with feedback that rewards itself on a decision resulting in higher NPS and penalises otherwise.

Reinforcement Learning

Steps to Conversational AI

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Why?

AI based conversation are entering all areas of customer touch points today to automate entire work-flows. Be it in lead generation and analysis, churn, recommendation, grievance addressal, AI is helping to decide the most optimised message to put across for a better customer relation. With 'deep' expertise in audio analysis and NLP we have helped clients to deliver value to customers through conversational AI

Infrastructure Design

The infrastructure of the system is heavily dependant on the client calling/interaction systems. The main framework should be built around the content delivery system beacuse in this use case apart from the obviously important AI part, minimum latency is of utmost importance. Spark, scala, spark-NLP are some of the scalable combinations we use for low latency.

 
 
 
 
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DL and ML Pipeline

Use custom built deep learning models to analyse audio and text feeds on various aspects like compliance , customer satisfaction, grievance addressal, faq search etc. This is created in a distributed set up with gpu computing enabled to ensure smooth functioning of the flow. Also we use scalable architectures to keep the cost at a minimum.

Strategy Delivery

The strategy of delivery is very tricky here as for real time strategy and call script creation the agent must have insights instantaneously delivered. To enable very curated content delivered at real time we employ streaming models and api in a distributed computing set up and completely automate the AI flow through integration with calling systems.

 
 
 
 
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