Self-Input Validation System for CRM Data Correction
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Solution Overview
Problem
Inefficient information validation and correction during online transactions lead to incorrect order delivery, reduced conversion rates, and increased customer inconvenience, as existing systems often disrupt the checkout process by prompting for corrections in real-time.
Innovation Solution
A computer system that validates and corrects self-input information post-transaction by checking inputs against a verification database, generating variations for incorrect entries, and allowing users to select corrected information, utilizing a machine learning algorithm to suggest variations based on matched data, and updating customer relationship management software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If information validation is performed in real-time during checkout, then information accuracy is improved, but conversion rate deteriorates due to additional obstacles for customers
Solution Approach 1:
The system performs information validation after transaction completion rather than during checkout. The validation process is initiated in advance (post-transaction) to avoid disrupting the checkout flow, allowing customers to complete purchases without interruption while still achieving information accuracy through subsequent verification and correction cycles.
2Productivity
If information correction is performed post-transaction, then conversion rate is improved by removing checkout obstacles, but information accuracy may deteriorate if corrections are not implemented
Solution Approach 1:
The system implements a feedback loop where post-transaction validation results are communicated back to customers, who can then provide corrected information. The system continuously iterates by validating the corrected information against the verification database, ensuring information accuracy is achieved through this feedback-driven correction process while maintaining high conversion rates.
3Measurement precision
If multiple input variations are generated and presented to users, then information accuracy is improved through user selection, but device complexity increases
Solution Approach 1:
The system generates multiple input variations (excessive action) to ensure comprehensive coverage of possible correct information, then presents these variations to users for selection. This approach prioritizes achieving information accuracy over system simplicity, using the complexity of generating and managing multiple variations as a means to guarantee correct information is identified and corrected.
Data Source
AI summary
A computer system for self-input information validation and correction may include receiving, from an electronic device, an input. The input may include information; checking the input against a verification database; if the input matches one set of verified information, tagging the input as verified; if the input does not match, or matches more than, one set of verified information, generating at least one input variation; checking each input variation against the verification database; if an input variation matches one set of verified information, tagging the input as verified, and updating the input on CRM software with a matched input variation; if the input variation does not match one set of verified information, sending a list of input variations to the electronic device; receiving, from a user, a corrected input selection; and updating the input on a CRM software with the corrected input selection.