Gratuity Analytics System Normalizing Customer Satisfaction Scores
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Solution Overview
Problem
Current methods for analyzing customer satisfaction through gratuity data lack accuracy, as they rely on average tip amounts that do not account for individual customer tipping habits, leading to misleading indicators of service quality and satisfaction.
Innovation Solution
A gratuity analytics system that processes transaction data to generate analytics and calculate customer satisfaction scores by normalizing tip data over time, considering historical tipping patterns and real-time feedback, enabling businesses to assess service quality and customer happiness more accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If average tip amounts are used to analyze customer satisfaction, then objective benchmark information is provided, but the measurement precision of actual customer satisfaction level deteriorates
Solution Approach 1:
The patent segments the analysis from aggregate-level average tips to individual customer-level tipping behavior. By processing transactions individually and tracking each customer's historical tipping pattern separately, the system preserves individual customer information while providing personalized satisfaction metrics, thus resolving the contradiction between objective benchmarking and individual precision.
Solution Approach 2:
The patent implements dynamic analysis by continuously updating each customer's historical tipping data over time. Instead of using static average tips, the system dynamically adjusts each customer's satisfaction score based on their evolving tipping behavior pattern, allowing for accurate real-time measurement that adapts to individual customer changes.
2Reliability
If average gratuity data is analyzed, then business performance benchmarking is enabled, but the accuracy of service quality assessment deteriorates
Solution Approach 1:
The patent enables the system to automatically process and analyze transaction data, matching authorization messages with clearing messages to extract tip information. The system self-updates customer profiles and satisfaction scores without manual intervention, maintaining high reliability in service quality assessment while managing complexity through automated self-service processing.
Solution Approach 2:
The patent implements feedback loops where the system continuously monitors tip data, compares it against historical patterns, and updates satisfaction scores in real-time. This feedback mechanism ensures reliable service quality assessment by constantly adapting to actual customer behavior, while the automated feedback process manages system complexity through structured iterative processing.
3Measurement precision
If historical tipping patterns are considered, then individual customer satisfaction accuracy is improved, but the processing time and data volume increase
Solution Approach 1:
The patent performs preliminary action by pre-processing and storing historical transaction data in an organized structure. By preparing and indexing historical tipping patterns in advance, the system can quickly retrieve and compare past data during satisfaction score calculations, improving measurement precision while minimizing real-time processing time through pre-computed historical benchmarks.
Data Source
AI summary
A gratuity analytics computing system, for generating gratuity analytics for a plurality of transactions of a customer at a service provider within a date range, is in communication with an electronic device of the service provider over an electronic network. The system includes a gratuity analytics computing device, a database including a memory in operable electronic communication with the gratuity analytics computing device, and a processor configured to: receive transaction data for the plurality of customer transactions occurring within the date range, match a plurality of authorization messages with a respective plurality of clearing messages, generate gratuity analytics for the plurality of customer transactions over the date range based on average tip data from the customer, and calculate a customer satisfaction score for one customer transaction of the plurality of customer transactions based on the generated gratuity analytics.