Predictive Call Routing for Value-Based Queue Segmentation
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Solution Overview
Problem
Conventional call center routing methods often result in high business value callers experiencing long waits, while low business value calls are prioritized, leading to customer dissatisfaction and inefficient allocation of resources.
Innovation Solution
A system and method that automatically route customer calls to appropriate call queues based on predicted call value by using predictive models to classify callers into value groups and assign them to specific agent pools, optimizing resource allocation and wait times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional first-come-first-served routing is used, then all callers are treated equally, but high business value callers experience long waits causing dissatisfaction
Solution Approach 1:
The system performs preliminary classification of callers into value segments before routing them to queues. By analyzing caller data and predicting call value in advance, the system assigns callers to appropriate priority queues beforehand, ensuring high-value callers are routed to priority queues with shorter wait times while low-value callers are routed to standard queues.
2Device complexity
If all callers are routed to the same queue, then resource allocation is simple, but high business value calls do not receive prioritized service
Solution Approach 1:
The system segments the single caller queue into multiple priority-based queues (e.g., priority queue for high-value callers, standard queue for low-value callers). This segmentation allows differential service levels where high-value callers are routed to priority queues that provide faster service, while maintaining separate queue management for each segment.
3Productivity
If predictive modeling is implemented to classify callers by value, then high value callers can be prioritized, but system complexity increases
Solution Approach 1:
The system introduces an intermediary predictive modeling component that sits between call reception and queue routing. This intermediary analyzes caller data, predicts call value using machine learning models, and generates routing recommendations. The intermediary handles the complexity of predictive analysis separately, allowing the core routing system to remain relatively simple while still achieving intelligent prioritization.
Data Source
AI summary
A call management system of a call center retrieves from a customer database enterprise customer data associated with an identified customer in a customer call, which may include customer event data, attributions data, and activity event data. The customer database tracks prospects, leads, new business, and purchasers of an enterprise. The system retrieves customer demographic data associated with the identified customer. A predictive model is selected from a plurality of predictive models based on retrieved enterprise customer data. The selected predictive model, including a logistic regression model, and tree-based model, determines a value prediction signal for the identified customer, then classifies the identified customer into a first value group or a second value group. The system routes a customer call classified in the first value group to a first call queue assignment, and routes a customer call classified in the second value group to a second call queue assignment.