Low-Latency Checkout Delivery Date Estimation with IP Geolocation
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Solution Overview
Problem
Existing checkout interfaces provide imprecise and inaccurate delivery date estimates, leading to a disjointed user experience due to latency issues when machine learning models require user input for precise predictions, which delays the presentation of essential shipping options.
Innovation Solution
Utilize geolocation based on the user's IP address to determine candidate delivery regions before obtaining the desired shipping address, caching candidate delivery estimates using a machine learning model, and retrieving these estimates from cache with little or no latency upon address input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is used to provide accurate delivery date estimates, then measurement precision is improved, but loss of time increases due to execution latency
Solution Approach 1:
The system performs preliminary actions by obtaining geolocation based on IP address before the user provides their shipping address, pre-calculates and caches candidate delivery estimates for multiple potential delivery regions, so that when the user actually inputs their address, the system can immediately retrieve and present the appropriate delivery estimate without executing the ML model at that moment
2Measurement precision
If the ML model waits for user input before generating predictions, then measurement precision is improved, but productivity decreases due to delayed interface functionality
Solution Approach 1:
The system performs preliminary actions by obtaining geolocation based on IP address before the user provides their shipping address, pre-calculates and caches candidate delivery estimates for multiple potential delivery regions, so that when the user actually inputs their address, the system can immediately retrieve and present the appropriate delivery estimate without executing the ML model at that moment
Solution Approach 2:
The system dynamically adapts its behavior based on available information: it uses IP-based geolocation to determine candidate delivery regions when shipping address is not yet provided, and switches to using the actual shipping address once provided. This dynamic approach allows the system to maintain high productivity at each stage while ensuring accuracy when complete information is available
3Measurement precision
If pre-generating delivery estimates for all possible delivery destinations is done, then measurement precision is improved, but use of energy increases due to excessive computation
Solution Approach 1:
Instead of generating delivery estimates for all possible delivery destinations globally, the system applies local quality by focusing computational resources on generating estimates only for candidate delivery regions that are geographically relevant to the user's location (determined by IP-based geolocation). This localized approach ensures high accuracy for relevant regions while minimizing unnecessary computation for distant or irrelevant destinations
Data Source
AI summary
Methods and systems for presenting dynamically generated estimates in a checkout interface are described. A geolocation estimate is obtained based on an IP address associated with a user device. A machine learning model is used to obtain candidate estimates for candidate regions overlapping with an accuracy region defined about the geolocation estimate. The candidate estimates are stored in a cache. Responsive to receiving, from the user device, input indicating a desired region, a candidate estimate is retrieved from the cache for an identified candidate region matching the desired region. The retrieved candidate estimate is communicated to the user device, to cause the user device to present the at least one retrieved candidate estimate in a checkout interface.