ML Customer Contact Scheduling Optimization
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Solution Overview
Problem
Current methods for contacting customers in financial institutions, such as banks, are random and do not consider customer preferences or interaction history, leading to inefficiencies in establishing successful contacts, which in turn increases credit losses and reduces revenue.
Innovation Solution
Implementing a machine learning-based scheduling strategy that analyzes customer data, including interaction history and personnel availability, to determine optimal times for contact attempts, using a trained machine learning model to generate a proposed schedule and assess willingness and ability to pay, while considering maximum call capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If random automated outbound dialing is used to contact customers, then the system is simple and easy to operate, but the success rate of establishing contact is low
Solution Approach 1:
The system performs preliminary analysis of customer data, interaction history, and personnel availability before scheduling contact attempts. The machine learning model pre-processes historical data to identify optimal contact times and customer preferences in advance, rather than making random calls. This preliminary action increases contact success rates by ensuring calls are made at appropriate times based on learned patterns.
Solution Approach 2:
The system incorporates feedback loops where outcomes of previous contact attempts are fed back into the machine learning model to continuously improve scheduling accuracy. The model learns from historical interaction data and adjusts future scheduling decisions based on what has worked successfully in the past, creating a self-improving system that balances complexity with effectiveness.
2Productivity
If machine learning-based scheduling is implemented to optimize contact timing, then the success rate of contact attempts increases, but the device complexity and computational requirements increase
Solution Approach 1:
The machine learning model operates autonomously to analyze customer data and generate optimized contact schedules without requiring manual intervention. The system serves itself by automatically training on historical data, identifying patterns in customer availability and preferences, and generating scheduling recommendations. This self-service capability reduces the need for complex manual configuration while maintaining high productivity.
Solution Approach 2:
The system changes key parameters of the contact strategy by transitioning from random timing to data-driven optimal timing based on multiple factors including customer preferences, interaction history, and personnel availability. The machine learning model dynamically adjusts scheduling parameters to maximize contact success rates while managing system complexity through automated decision-making.
3Measurement precision
If multiple data sets are analyzed including customer preferences and personnel availability, then the quality of scheduling decisions improves, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary analysis and preprocessing of multiple data sets including customer preferences, interaction history, and personnel availability before the actual scheduling decision is needed. By pre-processing and storing insights from these data sets, the system reduces the computational burden during real-time scheduling while maintaining high accuracy in contact recommendations.
Data Source
AI summary
A method and a system for using machine learning technology to optimize a scheduling strategy for establishing contact with a customer are provided. The method includes: receiving a data set that relates to a customer account; analyzing the data set to determine at least one proposed schedule for an attempt to contact the customer; and generating, based on a result of the analysis, a report that includes the proposed schedule. The analysis may be performed by applying a machine learning model that is trained by using historical data that relates to interactions associated with the customer.