Merchant Review System Using Normalized Tip Data
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Solution Overview
Problem
Existing electronic recommendation systems rely on subjective customer reviews, which are inconsistent and outdated, leading to inaccurate merchant rankings and unfair evaluations, as they often lack real-time data and objective metrics.
Innovation Solution
A system that uses objective customer review data, specifically tip amounts, to provide recommendations by normalizing historical tip data and determining user sentiment, thereby offering fresh and accurate merchant reviews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If subjective customer reviews are used for merchant rankings, then the system can capture customer feelings and opinions, but the reviews become inconsistent and inaccurate due to individualistic rating scales
Solution Approach 1:
The patent transforms the rating parameter from subjective star ratings to objective tip amount data. By changing the measurement parameter from qualitative (stars) to quantitative (actual tip amounts in currency), the system achieves both accuracy in capturing customer sentiment and consistency in measurement across all users.
Solution Approach 2:
The patent replaces the mechanical system of manual star rating with an automated data extraction system that pulls objective tip amount information from transaction records. This substitution eliminates human subjectivity in the rating process while preserving the underlying customer sentiment expressed through actual tipping behavior.
2Loss of time
If real-time customer reviews are acquired, then the system can provide fresh data, but customers often provide inconsistent reviews when preoccupied with the merchant
Solution Approach 1:
The patent performs the review data collection automatically at the time of transaction without requiring customer action. By capturing tip amount data preliminarily during the checkout process, the system obtains fresh real-time data while avoiding the problem of customers providing inaccurate reviews when distracted.
Solution Approach 2:
The system uses the customer's own tipping behavior as the review data source. The customer's actual tip amount, which they voluntarily provide during payment, serves as the authentic sentiment indicator without requiring them to separately compose a review, thereby ensuring both freshness and accuracy.
3Productivity
If traditional review systems are used, then the system can provide merchant evaluations, but the reviews become stale and do not reflect current customer experiences
Solution Approach 1:
The patent implements continuous data collection by automatically capturing tip amount information with every transaction. This continuous flow of fresh data replaces the periodic, stale review updates of traditional systems, ensuring merchant evaluations always reflect current customer experiences without manual intervention.
Solution Approach 2:
The system preliminarily collects and stores tip data at the moment of each transaction, creating a continuous stream of fresh review information. This preliminary capture of data ensures that the most recent customer sentiments are immediately available for merchant rankings, eliminating the staleness problem of traditional review systems.
Data Source
AI summary
The disclosed embodiments provide systems, methods, and techniques for managing merchandising cards. A merchandising card may be, for example, a gift card, loyalty card, or the like. Consistent disclosed embodiments, a system for managing merchandising cards may include one or more memory devices storing instructions and one or more processors configured to acquire, from a device over a network, a plurality of locations associated with the device, the device locations being acquired at different instances in time within a predetermined period of time. Additionally, the processor may be configured to calculate a merchant confidence rating for a merchant using the device locations. Further, the one or more processors may be configured to, based on the merchant confidence rating, determine that the merchant matches a merchant that is associated with merchandising card, and send a reminder a user of the device.