How does ADP automate 55% of its inbound calls with AlloBot?

Executive Summary

Groupe ADP, managing four major European airports, was facing an unsustainable operational burden from its customer service infrastructure. With 1.8 million calls annually at an average handling cost of €1.50 per call, the company needed a solution that could scale efficiently without compromising service quality. By deploying AlloBrain's AI, ADP automated 60% of its call volume, leading to annual savings of over €1,620,000 and a drastic reduction in customer wait times, proving a clear and powerful return on investment.

The Challenge: A €2.7 Million Problem

ADP's primary challenges were directly impacting both their bottom line and their customer satisfaction scores:

• High Operational Cost: With 1.8 million calls per year costing €1.50 each when handled by a human agent, the total annual cost was approximately €2.7 million.
• Massive Call Volume: The infrastructure struggled to handle over 5,000 daily calls, resulting in a low answer rate and significant customer frustration.
• Poor Customer Experience: Passengers faced an average wait time of 4 minutes to speak with an agent, a critical point of friction in the fast-paced travel industry.
• Inefficient Resource Allocation: Agents were spending the majority of their time on repetitive, low-value inquiries (e.g., flight status, parking info), preventing them from focusing on complex, high-value passenger issues.

The Solution: A Strategic, Data-Driven Approach

AlloBrain implemented a two-phased solution designed for maximum impact and long-term value.

Phase 1: Diagnosis with AlloIntelligence‍

AlloBrain began by analyzing a comprehensive set of ADP's call recordings with it's AlloIntelligence solution. This data-driven approach allowed us to:

• Identify and categorize over 60 distinct reasons for calls.
• Pinpoint the highest-volume, most repetitive inquiries that were ideal candidates for automation.
• Build a custom AI language model trained specifically on ADP's unique terminology and customer phrasing.

Phase 2: Implementation of Allobot‍

Based on the analysis, we deployed our Allobot solution to:

• Automate End-to-End: Fully handle all identified high-volume, low-complexity calls without any human intervention.
• Triage Intelligently: Instantly pre-qualify complex calls and route them to the correct human advisor, providing the agent with the full context of the conversation to ensure a faster, more effective resolution.
• Integrate Seamlessly: Connect directly to ADP's internal systems and real-time knowledge bases to provide accurate, up-to-the-minute information.

The Results: A Clear & Compounding Return on Investment

The impact was both immediate and grew over time, demonstrating the power of a learning AI system.

• Immediate Financial Impact (Year 1):
• 40% of all incoming calls were fully automated.
• This delivered an initial annual saving of €1,080,000.
• Sustained & Improved Performance (Year 3):
• The Allobot's efficiency increased to 55% automation as the model continued to learn.
• Annual savings grew to over €1,485,000
• Transformative Operational Efficiency:
• Average customer wait time plummeted from 4 minutes to 1 minute 20 seconds.
• Calls handled by Allobot had a near-zero wait time.
• Empowered Human Agents:
• Freed from repetitive tasks, ADP's customer service agents could now focus on resolving complex passenger issues, dramatically increasing job satisfaction and overall service quality.