Discrete Choice Modeling for Multi-Channel Traffic Forecasting
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Solution Overview
Problem
Current systems for collecting, forecasting, and displaying transaction traffic from various communication methods are unable to coordinate or forecast information across multiple communication types, such as voice, SIP, VoIP, email, and instant messaging, leading to ineffective predictions and resource allocation in Work Force Management systems.
Innovation Solution
The implementation of Discrete Choice Modeling (DCM) using Multinomial Logit Estimation, which collects data from multiple communication channels, performs numeric transformations, calculates discrete choice probabilities, and forecasts future usage values, allowing for more accurate predictions of communication channel usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a singular communication system collects information on only one type of communication method, then the system complexity is reduced, but the ability to forecast and coordinate information across multiple communication types is lost
Solution Approach 1:
The system implements a universal communication analytics platform that can collect, process, and analyze data from multiple communication types (voice, email, chat, social media) through a single integrated framework. The discrete choice modeling engine serves multiple functions: forecasting communication traffic, analyzing customer preferences, and optimizing resource allocation across diverse communication channels.
Solution Approach 2:
The system segments communication data by type and source while maintaining a unified analysis structure. Each communication channel (voice, email, chat) is processed through separate data collection modules but is then integrated into a common discrete choice model that handles all communication types consistently, allowing complex multi-channel analysis without overwhelming system complexity.
2Measurement precision
If Work Force Management systems perform predictions on a single communication type, then the prediction accuracy for that specific type may be maintained, but the overall ability to assess communication traffic across various formats is compromised
Solution Approach 1:
The system transforms communication data from multiple types into a unified set of parameters that can be processed by the discrete choice model. By converting diverse communication metrics into standardized choice probability parameters, the system maintains prediction accuracy while extending capability across multiple communication formats. The model adjusts parameters dynamically based on the specific communication type being analyzed.
3Ease of operation
If customers are allowed to switch between different communication methods based on their preferences, then customer satisfaction and communication effectiveness improve, but the complexity of tracking and predicting communication behavior increases
Solution Approach 1:
The discrete choice model continuously learns from customer communication patterns and provides feedback predictions about future communication choices. The system analyzes historical data to understand customer preferences and updates its models in real-time, enabling accurate prediction of communication method selection without requiring complex manual tracking. The feedback loop automatically adapts to changing customer behaviors.
Data Source
AI summary
The method for and system or apparatus for forecasting future communication transaction traffic from a customer include the steps of or structure for: collecting communication channel data on at least first and second communication channels; performing a numeric transformation to the data; calculating a discrete choice probability for each communication channel; and forecasting future period usage values for a user on a communication channel.