ETA Calculation Using Customer and Merchant Location Offsets
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Solution Overview
Problem
In the context of online order pickup, merchants face challenges in accurately determining the arrival time of customers at their physical locations, leading to potential wait times and impacting customer satisfaction, as existing systems fail to account for individual customer behavior and location-specific factors.
Innovation Solution
The system calculates a refined estimated time of arrival (ETA) for customers by using offsets based on previous travel data, combining customer-specific and merchant location-specific adjustments to account for factors like driving behavior and parking lot congestion, allowing for more accurate arrival time predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the merchant uses a standard ETA provided by the customer's device or service, then the system is simple to operate, but the ETA accuracy is insufficient to account for individual customer behavior and location-specific factors
Solution Approach 1:
The patent segments the ETA calculation into multiple components: base ETA from navigation, customer-specific offset from historical behavior, and location-specific offset from merchant characteristics. This segmentation allows each component to be calculated independently and combined for improved accuracy without overwhelming system complexity.
Solution Approach 2:
The system performs preliminary actions by collecting and analyzing customer historical travel data and merchant location characteristics in advance. These pre-calculated offsets are stored and automatically applied when needed, reducing real-time computational complexity while maintaining high ETA accuracy.
2Loss of time
If the merchant prepares items earlier based on standard ETA, then items may be ready for pickup, but customers may arrive earlier and experience extended wait times
Solution Approach 1:
The system uses feedback from actual customer arrival times compared to predicted ETAs to continuously refine and update customer-specific and location-specific offsets. This feedback loop improves ETA accuracy over time, enabling better synchronization between item preparation and customer arrival, thereby reducing wait times.
3Measurement precision
If the merchant tracks actual arrival times to improve ETA accuracy, then future ETAs can be more precise, but the system requires additional data collection and processing infrastructure
Solution Approach 1:
The system implements self-service by automatically collecting arrival time data through the existing mobile application and service provider infrastructure. The mobile device's navigation application and the service provider's server automatically track and process arrival data without requiring additional manual data collection mechanisms, reducing infrastructure complexity.
Data Source
AI summary
A customer offset may be determined with respect to a customer who has previously traveled to one or more merchant locations. A merchant location offset may be determined with respect to multiple customers who have each traveled to a particular merchant location. Upon determining that the customer is in transit to the merchant location, a generic ETA to the merchant location may be determined. A customer-based ETA for the customer with respect to the merchant location may be determined based on the generic ETA and the customer offset. Moreover, a merchant location-based ETA for the customer with respect to the merchant location may be determined based on the generic ETA and the merchant location offset. Based on the refined and more accurate ETAs, the merchant location may be instructed to begin assembling items for pick-up by the customer.