Predictive Call-Back System for High Value Customer Prioritization
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Solution Overview
Problem
Conventional call centers face inefficiencies in managing agent call-backs, leading to high business value callers experiencing long waits and repeated callbacks, while low business value callers receive prompter attention, causing dissatisfaction and inefficient resource allocation.
Innovation Solution
An automated call-back system using predictive machine learning to identify high business value callers and prioritize their call-backs by classifying them into preferred and subordinate groups based on call-back metrics, ensuring timely and effective initial handling of their calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional call-back systems are used to return abandoned calls, then call center resources are utilized, but high business value callers experience extended wait times and repeated call-backs while low business value callers receive prompter attention
Solution Approach 1:
The system performs preliminary classification of callers into high and low business value groups using predictive machine learning models before the call-back occurs. This preliminary action enables differentiated handling where high value callers are prioritized for immediate call-back while low value callers are scheduled, preventing the time loss contradiction by establishing priority before resource allocation.
Solution Approach 2:
The system applies different quality levels of service to different segments of callers based on their predicted business value. High value callers receive premium service with immediate call-back and dedicated agent assignment, while low value callers receive standard scheduled call-back service. This local quality differentiation resolves the contradiction by optimizing resource efficiency through segmentation while ensuring high value customers receive timely attention.
2Ease of operation
If repeated call-backs are used to handle customer issues, then customer service is provided, but customer satisfaction decreases and call center resources are wasted
Solution Approach 1:
The system performs preliminary assessment of customer issues and agent-customer match suitability before the call-back. Predictive models identify the most appropriate agent for each high value caller based on historical interaction data and issue complexity. This preliminary action reduces the need for repeated call-backs by ensuring the right agent handles the customer from the first call-back, thereby reducing resource consumption while maintaining service quality.
Solution Approach 2:
The system implements feedback mechanisms where customer interaction outcomes are fed back into the predictive models. When an issue is resolved successfully on the first call-back, this feedback is recorded. The system learns from these patterns to improve future agent assignments and issue routing, reducing the likelihood of repeated call-backs and optimizing resource utilization over time.
3Productivity
If conventional call routing is used to connect callers to agents, then calls are handled, but high business value callers are not identified and prioritized appropriately
Solution Approach 1:
The system performs preliminary identification and classification of caller business value using predictive machine learning models before the call is routed to an agent. This preliminary action extracts and analyzes customer information, transaction history, and interaction patterns to determine business value. The identified high value customers are then prioritized in the routing process, preventing loss of critical customer value information and ensuring appropriate handling efficiency.
Solution Approach 2:
The predictive machine learning system acts as an intermediary between the conventional call routing system and the agent pool. This intermediary layer analyzes caller information, determines business value, and provides prioritized routing recommendations. The intermediary preserves and utilizes customer business value information that would otherwise be lost in conventional routing, while improving overall call handling efficiency through intelligent prioritization.
Data Source
AI summary
System and method for automatically calling back a customer via a predictive model determines a plurality of call-back metrics for a plurality of advisor records. The predictive model is applied to call-back data to identify customers that are likely to require a series of call-backs, and automatically generates a preferred call-back to such customers to reduce this risk. The automated call-back may follow termination of an identified customer's inbound call, or at some time after completion of a previous call interaction of the identified customer with an advisor. In the predictive model, a first compilation of call-back metrics record is representative of an overall likelihood of call-backs associated with each advisor record, and a second compilation of the plurality of call-back metrics is representative of a likelihood of call-backs for each of the plurality of products of the enterprise associated with the advisor record.