As customer expectations shift towards personalization and customization, Persona 360, a data AI product built on top of your existing data platform (Azure, AWS, Databricks, etc.), unifies customer profiles and enhances them with digital behavior insights and unstructured data like call center recordings. With 1695+ prebuilt customer attributes, segments, and integration of your existing data components, Persona 360 helps you optimize data collection, storage, modeling and activation. Empowering you to personalize customer experiences effectively and scalably, with the flexibility and individual approach that enterprises need.

Key use cases of Persona360 are:

AI driven Call center

• Call monitoring and detecting agent performance issues.
• Augmenting agents by serving process book/guideline in a form of ChatGPT (“Virtual MS team” colleague)
• Automate calls by AI agent.
• Automated categorization/routing of inbound communication and tickets.

Hyper-perosnalization

• Using AI to create live/more relevant audiences for campaigns at scale.
• Detecting new leads/opportunities in communication with customers (chats, emails, call recordings, PDFs)
• Personalized chatbot experience by enriching chatbot’s knowledge base with individual customer data (interests, lifestages, predictions, ...)
• Using AI to notify agents about their portfolio of clients
• Augment agent by serving process book/guideline like ChatGPT (“Virtual MS team” colleague) and automated meeting preparations Predicting claim’s severity to optimize Costs (e.g. detect necessary on-site inspections)

Dynamic pricing

• Individual pricing engine using AI at scale.
• Enriching models by additional information extracted from communication with customers (chats, emails, call recordings, PDFs,…)
• AI governance framework for regulatory purposes (EIOPA, etc.)

AI-driven Fraud/Risk

• Enriching models by additional information extracted from digital & unstructured interactions with customers (web, chat, call recordings, emails, PDFs, client zone)
• Using advanced Graph/ML methods to detect fraud/AML
• Automatically scan and check all banker-client communication (calls, chats, emails) for compliance violations (e.g. MIFID)