Case Study: The Verizon Contact Center Improved Customer Experience While Strengthening Security Measures.

Executive Summary:

This study demonstrates how Verizon's contact center uses AI systems to lower fraud instances while increasing revenue streams, resulting in improved customer security and experience.

Product Manager Contributions:

As the Product Manager, I organized discovery sessions with business stakeholders at the contact center to understand their challenges and requirements. Throughout the project, I led cross-functional teams to maintain their cooperation and alignment towards shared goals. My design of IVR user flows improved customer engagement and interactions.

To define project requirements in detail, I generated epics alongside user stories and acceptance criteria. Through team collaboration with engineers and developers, I directed backlog management to achieve technical viability and feature delivery deadlines, which led to a successful AI-driven fraud detection system deployment.

Customer Flow for Voice Online Fraud Detection:

• Customer Initiation: Customers make contact through multiple channels, including phone calls and online methods.

• AI Analysis:

o Action: The AI system analyzes both transaction details and associated options, including flags and patterns.

o Outcome: Evaluate customer sentiment while identifying potential fraud indicators.

• Insights & Orchestration:

o Action: The system extracts actionable insights about customer interactions and possible fraudulent activities.

o Outcome: Tailor responses for personalized service.

• Agent Response:

o Action: Agents obtain direction through AI-generated insights and information about customer interactions.

o Outcome: Enhanced decision-making for fraud prevention.

• Feedback Loop:

o Action: Collect customer feedback post-interaction.

o Outcome: Improve AI model accuracy with real-time updates.

Data Flow for Voice Online Fraud Detection:

• Data Collection:

o Source: Compile information from phone calls, together with online transactions data and interactions with customers.

• Data Processing:

o Action: Process analyzed data to identify fraud patterns.

• Surveillance & Security:

o Action: Keep track of flagged interactions to detect suspicious behavior and confirm data accuracy.

• Alert & Insight Generation:

o Action: Create alerts to identify potential fraud cases along with insights that agents can use.

• Data Utilization:

o Action: Agent actions should be guided by information, which will enhance fraud prevention strategies.

Customer and Business Outcome:

• Reduced Fraud Losses: Data-driven AI-based insights enabled the organization to reduce annual fraud losses by $2 million, alongside a 15% increase in revenue.

• Improved Retention: The improvement of IVR and anti-fraud systems led to a rise in customer retention by 20%, along with a $1.5M enhancement in Annual Recurring Revenue (ARR).

Conclusion:

Verizon's contact center saw major improvements in fraud detection capabilities and customer satisfaction after AI implementation, which resulted in better security and operational effectiveness. The initiative achieved a $2M annual reduction in fraud losses while boosting revenue by 15% through AI-based data analysis, and it enhanced customer retention by 20% as well as increased ARR by $1.5M through upgrades to IVR and anti-fraud systems.

Reflection:

The example demonstrates AI's role in revolutionizing customer service and security operations through a proactive strategy for managing fraud in the telecommunications sector while exemplifying that most business problems can be solved with a teamwork approach.