Customer Preference Scoring and Feedback for Item Substitutions
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Solution Overview
Problem
E-commerce platforms face challenges in recommending suitable substitute items when ordered products become unavailable, often leading to decreased sales and customer dissatisfaction due to inadequate consideration of customer preferences and item characteristics.
Innovation Solution
A smart substitution computing device uses a customer understanding model to generate preference scores for substitute items based on order data and customer attributes, combining relevance and preference scores to rank substitutes, and iteratively improves the model using performance data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If substitute items are recommended without considering customer preferences and item characteristics, then the substitution process is simple and fast, but customer satisfaction decreases and sales are reduced
Solution Approach 1:
The system collects feedback data from customers about their acceptance or rejection of substitute items and uses this feedback to iteratively retrain and improve the machine learning model. This creates a closed-loop system where substitution recommendations continuously improve based on actual customer responses, resolving the contradiction between operational simplicity and customer acceptance reliability
Solution Approach 2:
The system dynamically adjusts recommendation parameters by considering multiple item characteristics (category, brand, price, attributes) and customer preferences simultaneously. The machine learning model optimizes weighting of these parameters to generate substitution scores that balance operational efficiency with customer satisfaction, allowing the system to adapt recommendations based on changing conditions
2Reliability
If a comprehensive model considering multiple customer attributes and item characteristics is used, then customer acceptance increases, but system complexity increases
Solution Approach 1:
The machine learning model performs self-training by automatically consuming feedback data and retraining itself to improve performance. This self-service capability allows the system to handle increased complexity of comprehensive customer and item attribute analysis while maintaining operational efficiency, as the model autonomously optimizes without requiring manual intervention
Solution Approach 2:
The system segments the complex substitution problem into distinct analytical components: customer attribute analysis, item characteristic analysis, preference scoring, and acceptance prediction. Each component processes specific data types independently before integrating results, which manages complexity while achieving comprehensive evaluation for improved customer acceptance
3Productivity
If substitute recommendations are made without iterative model improvement, then implementation is faster, but performance and accuracy remain suboptimal
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing customer feedback data and item characteristic data in ready-to-use formats. This preliminary preparation allows the machine learning model to be rapidly retrained when needed without delaying substitution recommendations, maintaining implementation speed while enabling continuous accuracy improvement through iterative model updates
Data Source
AI summary
A system is configured to train a customer understanding model to generate a preference score for substitution items. The customer understanding model generates a preference score for each of a plurality of related substitution items based on order data including data indicative of at least one item ordered and location data indicating a location of a first store. The customer understanding model ranks each of the substitution items based on the preference score. Order data is transmitted including substitution data identifying each of the substitution items and corresponding rank. Performance data associated with a set of operations implemented based on the order data and the substitution data is obtained. An updated customer understanding model is trained based on the performance data and iteratively modified based on the updated training dataset and updated performance metrics generated from second performance data.