AI Behavioral Insights Platform

Context

Modern enterprises rely on behavioral insights to understand user intent, reduce churn, detect risk, and improve agent performance. However, many organizations struggle to interpret unstructured data—such as tone, hesitation, sentiment, or conversation patterns—across customer interactions.

I led the product strategy for an AI Behavioral Insights Platform designed to analyze multimodal inputs and deliver real-time, actionable insights to operations, support, onboarding, and compliance teams.

The Problem

Teams lacked reliable, scalable ways to extract meaning from customer interactions. Key challenges included:

• Difficulty interpreting behavioral and emotional signals
  
• Reactive decision-making instead of predictive insight
  
• Inconsistent agent performance due to limited feedback
  
• Manual review of interactions resulting in slow resolution
  
• Insufficient visibility into sentiment, trends, and risk patterns
  

This created operational inefficiency and inconsistent customer experiences.

Users & Pain Points

Primary Users

• Customer support organizations
  
• Sales and onboarding teams
  
• Risk and compliance teams
  
• Product and operations leaders
  
  

Key Pain Points

• Fragmented signal sources
  
• No unified modeling framework
  
• Limited real-time insights
  
• Manual QA workflows
  
• No predictive indicators for churn or escalation
  

Root Causes

1. Lack of a unified behavioral signal taxonomy.
   
2. Fragmented audio, text, and metadata without integration.
   
3. Limited access to ML/AI tools for non-technical users.
   
4. Reactive workflows that created slow feedback loops.
   

Product Strategy & Approach

The product strategy focused on:

1. Defining a scalable multimodal behavioral signal framework.
   
2. Delivering real-time insights to support agents and leaders.
   
3. Ensuring transparency and explainability in AI outputs.
   
4. Integrating insights directly into existing enterprise workflows.
   
5. Empowering teams with dashboards, alerts, and pattern analysis.
   

Solution

Behavioral Signal Engine

Developed a framework for analyzing tone, pace, sentiment, hesitation, interruptions, and silence patterns using multimodal inputs.

Real-Time Agent Assist

Delivered in-call guidance, recommended responses, and next-best actions to agents.

Conversation Summaries

Provided structured summaries that highlighted key issues, decisions, and follow-up tasks.

Predictive Indicators

Introduced early-warning signals for escalation risk, churn likelihood, and potential fraud.

Insights Dashboard

Created a leadership dashboard for trends, agent performance, and operational patterns.

Execution

• Led cross-functional teams of ML engineers, data scientists, and UX designers
  
• Developed the multimodal taxonomy with behavioral science partners
  
• Prioritized MVP signals based on measurable business value
  
• Ran iterative evaluation cycles to refine model accuracy
  
• Partnered with early adopters to test and validate insights in real workflows
  
• Built dashboards and automated workflows to operationalize insights
  

Results

The platform produced measurable operational improvements:

• Reduced handle time through real-time guidance
  
• Improved customer satisfaction and sentiment outcomes
  
• Increased sales conversion rates
  
• Earlier detection of risk, escalation, and fraud
  
• Up to 40% reduction in manual QA review
  
• Leadership gained visibility into behavioral trends and performance patterns
  

Learnings

• AI insights must be contextual and actionable to drive behavior change.
• Multimodal analysis significantly improves accuracy over text-only approaches.
• Explainability increases trust and adoption in AI-driven systems.
• Real-time feedback loops produce faster performance improvements than static coaching.
  

Real-time feedback loops produce faster performance improvements than static coaching.