Machine Learning Model for Predicting Service Session Metrics
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Solution Overview
Problem
Traditional service environments face challenges in obtaining objective and accurate assessments of customer experience due to incomplete survey data, as not all individuals complete surveys, leading to a skewed view of service quality and difficulty in developing objective knowledge about service quality.
Innovation Solution
Implementing machine learning techniques, such as deep learning, to develop models that predict survey results based on session records and survey data from previous service sessions, allowing for the determination of service session metrics even when individuals do not complete surveys, and enabling more accurate and comprehensive evaluation of service quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional survey methods are used to assess customer experience, then survey data can be collected from individuals who complete surveys, but the data becomes incomplete and skewed because not all individuals complete surveys
Solution Approach 1:
The patent introduces machine learning models as an intermediary between session records and survey results. These models are trained on historical survey data and session records to predict survey outcomes for individuals who did not complete surveys, thereby recovering the lost information and providing a more complete view of service quality
Solution Approach 2:
The patent creates predictive copies of survey results for individuals who did not complete actual surveys. By using machine learning models to generate predicted survey scores based on session characteristics, the system reconstructs the missing survey data without requiring the original survey completion
2Loss of information
If repeated survey requests are sent to individuals to obtain complete data, then more survey responses may be obtained, but resource expenditure increases and individuals may become frustrated
Solution Approach 1:
The patent enables the system to self-generate predictive survey results using machine learning models without requiring additional survey requests to individuals. The models automatically process session records and generate predicted survey outcomes, eliminating the need for repeated survey requests and the associated resource consumption
Solution Approach 2:
The patent performs preliminary training of machine learning models using historical survey data before actual service sessions. This preliminary action creates a ready-to-use predictive system that can immediately generate survey results for new sessions without requiring follow-up survey requests, thus preventing resource expenditure before it occurs
Data Source
AI summary
Techniques are described for generating metrics about an individual's experience. One of the method describes providing, by at least one processor, the session record as input to at least one computer-processable model that determines, based on the session record, at least one metric for the service session, the at least one model having been trained, using machine learning and based at least partly on survey data for previous service sessions, to provide the at least one metric associated with the individual's experience. The method includes associating, by at least one processor, the metric of the individual's experience with the individual. The method also includes communicating, by at least one processor, the at least one metric for presentation through a user interface of a computing device.