ML Shipping Coach for Granular Sales Predictions
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Solution Overview
Problem
E-commerce systems face challenges in accurately determining user shipping preferences for items across different regions, as uniform shipping preferences do not account for varying demand and urgency levels, making it difficult for selling partners to decide on optimal shipping options.
Innovation Solution
A machine learning (ML) model is trained to predict the quantity of items that will be sold based on shipping speeds and destination regions, enabling selling partners to specify granular shipping preferences and dynamically adjust their offerings using an ML shipping coach, which provides user interface features for viewing historical sales and inventory predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uniform shipping preferences are applied across all items and regions, then the system complexity is reduced and ease of operation is improved, but the accuracy of sales predictions and ability to capture regional demand variations deteriorates
Solution Approach 1:
The patent segments shipping preferences by item category and destination region, allowing different shipping options to be configured for different segments rather than applying a uniform preference across all items. This enables the system to capture regional demand variations and urgency levels while maintaining manageable complexity through structured segmentation.
Solution Approach 2:
The patent implements local quality by allowing selling partners to specify granular shipping preferences for specific items and regions based on local demand characteristics. The system learns and applies region-specific shipping preferences that reflect local urgency levels and demand patterns, improving prediction accuracy for each local market.
2Measurement precision
If granular shipping preferences are specified for each item and region, then the accuracy of sales predictions and capture of regional trends is improved, but the device complexity and difficulty of configuration increases
Solution Approach 1:
The patent applies preliminary action by providing default shipping preferences and automated recommendations based on learned patterns from historical data. Selling partners can start with pre-configured preferences that are automatically adjusted based on performance, reducing the initial configuration burden while enabling granular control when needed.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously learns from actual sales data and shipping performance, then automatically adjusts shipping preferences and provides recommendations to selling partners. This closed-loop feedback reduces configuration complexity by automating the optimization process while maintaining high prediction accuracy through continuous learning.
3Measurement precision
If machine learning models are trained on historical sales data to predict quantity sold, then the ability to capture trends and make informed decisions is improved, but the loss of time for training and deploying models increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical sales data from the entire electronic catalog before deployment to selling partners. The models are trained in advance on aggregated data to learn regional and categorical patterns, then deployed ready-to-use, reducing the time loss associated with real-time training while maintaining high prediction accuracy.
Data Source
AI summary
Generally described, one or more aspects of the present application correspond to machine learning techniques for generating predictions about how much of an item will sell to certain destination regions at certain shipping speeds. A machine learning model, such as a neural network, can be trained to make such predictions based on historical data. The predictions can be presented in a user interface that enables sellers to evaluate the predicted sales and opt in to supporting particular shipping speeds to particular destinations, for example enabling the sellers to create offers for the items with terms corresponding to desired shipping speeds and destinations.