Biometric Proximity Prediction for Customer Support Centers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack accuracy in predicting customer arrivals at customer support centers, leading to inefficient resource allocation and potential reputational harm due to unexpected visits, and may waste resources by preparing for non-arriving customers.
Innovation Solution
Methods involving biometric monitoring, customer sentiment analysis, and machine learning to determine the likelihood of customer arrival, providing instructions based on proximity and predicted emotional or physical state, and dynamically updating predictions to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are directed toward preparing for customer arrival at CSC, then customer service quality is improved, but resource efficiency deteriorates when customer does not arrive
Solution Approach 1:
The system performs preliminary actions by monitoring customer proximity and biometric data before actual arrival at CSC. This allows the system to predict customer intent and prepare appropriate resources in advance, avoiding both over-preparation for non-arriving customers and under-preparation for arriving customers.
Solution Approach 2:
The system changes parameters by using multiple data sources (proximity data, biometric functions, sentiment analysis) to dynamically adjust the prediction of customer arrival likelihood. This enables flexible resource allocation based on real-time customer state changes rather than static assumptions.
2Measurement precision
If multiple data sources are used to improve prediction accuracy, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the prediction process into distinct components: proximity detection, biometric monitoring, sentiment analysis, and arrival likelihood calculation. Each component processes specific data independently, and their results are combined to form the overall prediction, making the complex system manageable and maintainable.
Solution Approach 2:
The system employs multi-functional data processing where the same infrastructure handles multiple types of data (location, biometric, sentiment) and serves multiple purposes (arrival prediction, customer state assessment, resource allocation). This reduces overall system complexity by consolidating functions rather than creating separate systems for each function.
Data Source
AI summary
A system for determining a likelihood that a pre-determined customer will enter a selected customer support center (CSC) is provided. The system includes a customer-tracking system for determining whether the customer is within a predetermined distance of the CSC. The tracking system is set to a tracking state in response to receiving a customer tracking opt-in selection. The system also includes a biometric monitoring system that monitors, using an electronic device in close proximity to the customer, when the customer is determined to be within the pre-determined distance of the CSC. The biometric monitoring system monitors the customer's biometric functions to determine the likelihood of the customer entering the CSC. When a determination of the likelihood of the customer physically entering the CSC is higher than a threshold level, the system may provide the likelihood, as further influenced by the customer sentiment condition, of the customer entering the CSC.