Calendar Data Mining for Automated Merchant Offer Application
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Solution Overview
Problem
The current process of selecting a meeting location is cumbersome and time-consuming, often requiring manual decision-making and additional communication between parties. Additionally, users frequently face challenges in automatically placing repeat orders at merchants, especially when traveling.
Innovation Solution
The system searches a user's calendar for meetings with unspecified locations and provides offers from merchants based on the users' locations. Upon determining that both users are present at a merchant's location, the system automatically applies the offer to the transaction. Furthermore, the system identifies repeat transactions based on historical data and automatically places these orders when the user is near the merchant.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select meeting locations through communication, then location selection can be customized to user preferences, but the process becomes time-consuming and cumbersome
Solution Approach 1:
The system performs preliminary actions by automatically searching calendar schedules for meetings with unspecified locations, identifying suitable merchant locations in advance, and preparing offers before users need to make a decision. This eliminates the need for real-time manual coordination and reduces the time required for location selection.
Solution Approach 2:
The system enables self-service by automatically analyzing calendar data, generating location recommendations, and presenting offers without requiring users to manually communicate with each other or search through promotions. Users simply review the automated recommendations and select from pre-prepared offers, significantly reducing the operational effort required.
2Adaptability or versatility
If users search through multiple promotions to find suitable locations, then they can find the best offer, but the process becomes more complex and time-consuming
Solution Approach 1:
The system merges multiple functions into a single automated process: calendar analysis, location identification, offer retrieval, and recommendation generation are all combined in one system. This consolidation eliminates the need for users to separately search through multiple promotion platforms, reducing complexity while maintaining access to diverse offers.
Solution Approach 2:
The system achieves universality by handling multiple types of meetings, various merchant locations, and different offer types through a single unified platform. It can process calendar data from different users, identify various kinds of establishments, and retrieve offers from multiple merchants, all through one system that reduces overall process complexity.
3Productivity
If automatic offer application is implemented, then transaction processing is streamlined, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring user location through GPS data, comparing it with merchant locations, and automatically triggering offer application when users arrive at the selected merchant. This automated feedback loop streamlines transaction processing by eliminating manual offer retrieval and application steps, while the system complexity is managed through standardized detection and response protocols.
Solution Approach 2:
The system replaces manual mechanical processes (physically presenting coupons, manually entering promo codes, cashless payment verification) with automated electronic detection and application. GPS-based location detection automatically triggers offer application, and the system electronically verifies and applies discounts without requiring manual user actions, thereby increasing productivity while keeping the automation complexity manageable through standardized electronic protocols.
Data Source
AI summary
Methods and systems are employed for searching a first user's calendar schedule for a meeting with a second user, providing an offer from a merchant based on a user's calendar schedule and automatically applying the offer from the merchant based on arrival of the first user and second user at the merchant's location. The verification of the presence of the first user and the second user may be based on identifying the location of each user, and offers from merchant locations may be provided based on the respective locations of the users.