Delivery Tagging Model for Region-Specific Time Window Prediction
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Solution Overview
Problem
Users are often deterred from purchasing items with listed delivery times that could be shorter than advertised due to unawareness of faster delivery options in their region, leading to missed sales opportunities.
Innovation Solution
Implementing a machine learning-based delivery tagging system that uses historical data and machine learning models to accurately classify and adjust delivery time windows for items based on regional delivery performance, allowing for real-time updates and personalized delivery options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conservative delivery time window (e.g., 5-day) is listed for an item, then delivery reliability is ensured, but user conversion rate decreases due to perceived long wait time
Solution Approach 1:
The patent applies local quality by providing customized delivery time estimates for different geographic regions. Instead of using a single conservative delivery window for all users, the system analyzes historical delivery data specific to each region and displays region-specific delivery estimates. This allows regions with faster delivery performance to show shorter delivery windows (e.g., 2-day) while regions with slower performance maintain longer windows (e.g., 5-day), thereby improving conversion rates in fast-delivery regions without compromising reliability in slower regions.
Solution Approach 2:
The system performs preliminary action by pre-calculating and storing delivery time estimates for different regions before users make purchasing decisions. Historical delivery data is analyzed in advance to determine region-specific delivery performance, and these pre-computed estimates are then displayed to users during the shopping process. This eliminates the need for users to wait for delivery time calculations after placing orders and allows them to make informed decisions based on accurate, pre-analyzed delivery information.
2Productivity
If a shorter delivery time window is advertised, then user conversion rate increases, but delivery reliability may be compromised
Solution Approach 1:
The system enables regions with demonstrated fast delivery performance to display shorter delivery time windows (e.g., 2-day) while regions with slower performance display longer windows (e.g., 5-day). This region-specific approach allows the platform to advertise shorter delivery times to users in regions where such promises are reliable, thereby increasing conversion rates without compromising overall delivery reliability.
Solution Approach 2:
The system continuously monitors actual delivery performance against promised delivery windows and uses this feedback to adjust future delivery estimates. By analyzing whether deliveries meet their promised time windows, the system can refine its predictions and ensure that advertised delivery times remain achievable, thus maintaining reliability while optimizing for shorter delivery promises where performance supports it.
3Productivity
If region-specific delivery estimates are implemented, then user satisfaction and conversion rate improve, but system complexity increases
Solution Approach 1:
The system segments the delivery estimation process into distinct components: historical data collection, region identification, performance analysis, and estimate generation. By dividing the overall system into these modular segments, each handling a specific aspect of delivery estimation, the system can implement complex region-specific logic without creating unmanageable overall complexity. Each segment can be independently developed, tested, and maintained.
Solution Approach 2:
The patent introduces intermediary components such as region identification modules and data analysis layers that mediate between the user interface and the delivery estimation logic. These intermediaries handle the complexity of analyzing historical delivery data and determining region-specific performance, while presenting simplified delivery time estimates to users. This intermediary layer shields the user interface from underlying system complexity while enabling sophisticated region-specific functionality.
Data Source
AI summary
A system including one or more processors; and one or more non-transitory computer-readable media storing computing instructions, that when executed on one or more processors, cause the one or more processors to perform certain operations including: training a first submodel of a machine learning model by at least (i) creating a cumulative addition of light gradient boosting models, and (ii) determining weights for aggregation with probabilities from the light gradient boosting models; generating, using the machine learning model, as trained, classifications for nodes, wherein the classifications comprise unions of outputs of the first submodel of the machine learning model and outputs of a second submodel of the machine learning model; and based on the classifications for the nodes, automatically tagging a portion of the nodes as deliverable in an online platform. Other embodiments are described.