Hierarchical Recommendation Engine for BOPUS Replacement Items
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Solution Overview
Problem
In 'buy online, pick up in-store' (BOPUS) services, items may become unavailable due to in-store purchases, mislocation, damage, or inaccurate inventory data, leading to unfulfilled orders, which current systems struggle to address effectively.
Innovation Solution
A recommendation engine generates models to suggest replacement items that are likely to be available and of interest to the customer, using a hierarchical structure of sub-models that consider item features, customer preferences, and inventory dynamics, allowing for real-time updates based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a customer places a BOPUS order for specific items, then the system can fulfill the order from store inventory, but items may become unavailable between order placement and fulfillment due to in-store purchases, mislocation, damage, or inaccurate inventory data
Solution Approach 1:
The system performs preliminary actions by proactively identifying unavailable items in BOPUS orders before fulfillment and automatically generating replacement recommendations. The recommendation engine pre-processes inventory status and customer preferences to prepare replacement options in advance, so when an item is found unavailable, the replacement can be immediately presented to the customer without delay.
Solution Approach 2:
The system implements feedback loops by continuously monitoring inventory status, customer responses to replacement recommendations, and fulfillment outcomes. This feedback is used to retrain and improve the recommendation models over time, increasing the accuracy of both availability predictions and replacement suggestions, thereby improving overall fulfillment reliability.
2Ease of operation
If the system manually handles unavailable items in BOPUS orders, then customer service can address individual cases, but the process becomes time-consuming and operationally complex
Solution Approach 1:
The system enables self-service by automatically generating and presenting replacement item recommendations to customers without requiring manual employee intervention. The recommendation engine autonomously processes unavailable items, queries inventory systems, applies customer preference models, and presents replacement options through the customer interface, allowing customers to select replacements independently.
Solution Approach 2:
The system replaces manual mechanical processes with automated computational systems. Instead of employees manually checking inventory, analyzing customer preferences, and suggesting replacements, the patent employs machine learning models, recommendation engines, and automated communication systems to perform these functions, significantly increasing fulfillment speed and reducing operational complexity.
3Productivity
If the system recommends replacement items without considering customer preferences, then the process is simpler and faster, but customer satisfaction decreases due to irrelevant or unwanted recommendations
Solution Approach 1:
The system applies local quality by customizing replacement recommendations according to individual customer preferences, purchase history, and product category-specific factors. Rather than using a single generic recommendation approach, the patent tailors the recommendation process to each customer's unique characteristics and each product category's specific requirements, improving both relevance and acceptance rates.
Solution Approach 2:
The system dynamically adjusts recommendation parameters based on customer feedback, product category, inventory conditions, and contextual factors. The recommendation engine modifies weighting factors, search criteria, and selection thresholds in real-time to optimize both the speed and quality of recommendations, balancing productivity with customer satisfaction through adaptive parameter changes.
Data Source
AI summary
In a “buy online, pick up in-store” service, customers place orders for items that are retrieved from store inventory and packaged for easy pick-up by the customer. Since these services typically fulfill orders from current store inventory, some items purchased by users are unavailable at the time of order fulfillment. In these circumstances, a recommendation system identifies a recommended replacement item using a trained model. The trained model includes a hierarchy of multiple sub-models, where each sub-model is configured to receive a different set of features of items as input and to generate, as output, a candidate recommended replacement item. A recommended replacement item is selected from the candidate recommendations generated by the multiple sub-models and sent for display to a user. The recommendation system receives user feedback regarding the recommended replacement item and selectively retrains one or more sub-models of the multiple sub-models based on the user feedback.