Order Experience Data Processing Across Multiple Structures
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Solution Overview
Problem
Conventional methods for measuring order experience in enterprises rely on human feedback, which is often incomplete, inaccurate, and costly, and do not effectively evaluate order experiences across multiple components, making it difficult to deliver a perfect order experience and capture data on a meaningful scale.
Innovation Solution
A computer-implemented method processes order data from multiple data structures without user input, extracts pre-defined attributes, calculates order experience scores using algorithms, and generates benchmark values to improve order experience monitoring and user experiences within enterprises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human feedback is used to measure order experience, then user satisfaction can be captured, but the feedback is incomplete, inaccurate, and costly to obtain
Solution Approach 1:
The system enables order experience measurement to be self-serve d by automatically collecting and processing order data from multiple data structures without requiring human user input or participation in surveys. The automated system extracts relevant attributes and calculates order experience scores independently.
Solution Approach 2:
The patent replaces the mechanical survey-based feedback collection system with an automated computational system that processes order data through algorithms. This substitution eliminates the need for manual survey distribution and response collection, thereby improving both accuracy and productivity.
2Loss of information
If customer satisfaction surveys are used, then some user feedback can be obtained, but participation is low and the cost is very high
Solution Approach 1:
The system extracts only the necessary attributes related to order experience from the comprehensive order data. By taking out only the relevant information needed for calculation, the system avoids the complexity of managing entire survey responses while ensuring complete order experience data capture.
Solution Approach 2:
Instead of relying on actual user survey responses, the system creates a computational model that copies and processes order data to generate order experience scores. This approach eliminates participation challenges while maintaining data completeness.
3Adaptability or versatility
If conventional survey tools are used, then user feedback can be collected, but the ability to evaluate across large sets of pre-defined order experience components is lacking
Solution Approach 1:
The system is designed to evaluate order experience across multiple pre-defined components and data structures simultaneously. This multi-functional approach allows comprehensive coverage of various order experience aspects while maintaining precise measurement through algorithmic processing of each component.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for processing order experience data across multiple data structures are provided herein. An example computer-implemented method includes processing data, obtained from a first set of data structures, pertaining to orders placed with an enterprise, wherein the first set of data structures contains data associated with distinct portions of order transactions; extracting information pertaining to pre-defined attributes from the processed data and processing the extracted information into a second set of data structures; calculating order experience scores for the orders by applying at least one algorithm to the extracted information in the second set of data structures; generating at least one benchmark order experience value, wherein each benchmark order experience value is based at least in part on the calculated order experience scores; and performing operations related to order experience within the enterprise based at least in part on the benchmark order experience values.