ML Conversion Prompting Across Online and Physical Carts
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Solution Overview
Problem
Existing online shopping systems struggle to predict user behavior across different conversion channels, such as adding items to an online cart versus a physical cart, leading to inconsistent shopping experiences and inefficiencies.
Innovation Solution
A trained machine-learning model is used to analyze user interactions and conversion data to generate user interfaces that prompt users to switch between online and physical conversion channels based on likelihood scores, optimizing the shopping experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the online system provides generic shopping interfaces without channel-specific optimization, then the system complexity is reduced, but user engagement and conversion rates deteriorate due to inconsistent shopping experiences across channels
Solution Approach 1:
The system pre-trains machine learning models using historical conversion data from multiple channels before deployment. This preliminary action enables the models to predict user conversion likelihood across channels, allowing the system to provide channel-optimized interfaces without adding real-time computational complexity
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw user interaction data and the online shopping interface. These models process and interpret user behavior patterns, translating complex multi-channel data into simplified conversion predictions that guide interface customization without requiring the system to directly manage all channel-specific complexities
2Productivity
If the online system uses machine learning models to predict user conversion behavior across channels, then user engagement and conversion rates improve, but the device complexity and computational requirements worsen
Solution Approach 1:
The system applies machine learning models selectively rather than universally - using them for high-value conversion predictions while relying on simpler rules for low-stakes interactions. The model processes only the necessary features from user data rather than analyzing all possible attributes, reducing computational overhead while maintaining conversion rate improvements
Solution Approach 2:
The system implements feedback loops where actual conversion outcomes are fed back into the machine learning models to continuously refine predictions. This feedback mechanism improves conversion rate accuracy over time while the models learn to require less computational power for confident predictions, gradually reducing the computational burden
3Measurement precision
If the online system collects and processes extensive conversion data from multiple channels, then the precision of conversion prediction improves, but the loss of time for data processing and the device complexity worsen
Solution Approach 1:
The patent segments conversion data into distinct channels (online, physical store, mobile app) and trains separate machine learning models for each channel. This segmentation allows the system to process channel-specific data more efficiently without the overhead of analyzing all channels uniformly, reducing data processing time while maintaining or improving prediction accuracy through channel-specific insights
Solution Approach 2:
The system performs preliminary data processing and feature extraction during data collection phases, preparing conversion data in advance for model training. This preliminary action reduces the computational burden during real-time prediction, minimizing data processing time while ensuring high prediction accuracy through pre-processed, high-quality input data
Data Source
AI summary
An online system uses a trained machine-learning model to create an online cart or a physical cart for a user of the online system. Upon receiving a signal with an indication about an interaction by the user with one or more items via a first conversion channel of the online system, the online system retrieves one or more candidate items for the user to convert via a second conversion channel of the online system that is different from the first conversion channel. The online system applies the machine-learning model to output a conversion score for each retrieved candidate item that indicates a likelihood of conversion. Responsive to the conversion score being above a threshold score, the online system generates a user interface at a device associated with the user prompting the user to use the second conversion channel for conversion of each retrieved candidate item.