Interaction Pacing Engine for Contact Center Abandonment Reduction
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Solution Overview
Problem
Contact centers face challenges in managing customer interactions efficiently, leading to high abandonment rates due to long wait times and unscheduled callbacks, which negatively impact customer satisfaction and agent occupancy.
Innovation Solution
Implementing an interaction pacing engine that determines preferred wait times for inbound calls, offers callback options, and schedules callbacks using various communication channels, optimizing wait times and agent allocation to reduce abandonment rates and improve customer engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual contact management is used for incoming and proactive interaction campaigns, then customer service coverage is maintained, but customer abandonment rates increase due to long wait times
Solution Approach 1:
The system performs preliminary actions by predicting customer abandonment risk before the interaction is fully processed. The risk prediction model analyzes interaction characteristics in real-time to identify high-risk customers early, allowing the system to preemptively adjust routing decisions and reduce wait times before abandonment occurs.
Solution Approach 2:
The system dynamically adjusts routing decisions based on real-time risk predictions. Instead of static routing rules, the system continuously adapts the routing of proactive interactions based on the predicted abandonment risk of each customer, optimizing the balance between service coverage and wait time reduction.
2Productivity
If more agents are allocated to handle incoming calls, then customer service capacity increases, but agent occupancy efficiency decreases due to imbalanced workload distribution
Solution Approach 1:
The system performs preliminary risk assessment and routing optimization before interactions reach agents. By predicting abandonment risk in advance and pre-optimizing the routing of proactive interactions, the system ensures that agents receive a balanced flow of interactions, improving both service capacity and occupancy efficiency without requiring additional agents.
Solution Approach 2:
The system changes the routing parameters of interactions based on predicted abandonment risk. High-risk interactions are routed differently from low-risk ones, creating a more balanced workload distribution across agents. This parameter-based routing optimization improves agent occupancy efficiency while maintaining overall service capacity.
3Adaptability or versatility
If proactive interactions are increased to improve customer engagement, then customer satisfaction may improve, but customer abandonment rates increase due to system overload
Solution Approach 1:
The system performs preliminary risk prediction on proactive interactions before they are executed. By identifying high-risk interactions early in the process, the system can adjust routing or timing to prevent system overload and reduce abandonment rates, while still maintaining high customer engagement through targeted proactive interactions.
Solution Approach 2:
The system changes routing parameters for proactive interactions based on predicted abandonment risk. High-risk proactive interactions are routed differently or delayed, while low-risk interactions proceed as planned. This dynamic parameter adjustment allows the system to maintain high customer engagement while preventing overload and reducing abandonment rates.
Data Source
AI summary
A system includes a processor and a memory. The memory stores instructions, which when executed by the processor, causes the processor to receive a request for an interaction with an agent device. The processor determines a patience time threshold for the customer in response to the request for an interaction. The processor suggests to return to the interaction at a time based on the patience time threshold, and performs the return interaction by the agent device based on the determination.