Opportunity Loss Estimation for Retail Products
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information processing systems fail to accurately assess the opportunity loss for target objects in retail environments by not effectively determining the probability of specific behaviors, such as purchases, based on customer interactions and stay times across multiple locations.
Innovation Solution
An information processing apparatus that acquires data on customer interactions and stay times, calculates an evaluation value for the probability of specific behaviors, and estimates opportunity loss for target objects with short stay times, using collaborative filtering and image analysis from cameras to determine customer engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system monitors customer behavior in all places, then the accuracy of behavior prediction improves, but the system complexity and data processing burden increase
Solution Approach 1:
The system extracts only the necessary behavior data (stay time and purchase behavior) from customer monitoring, rather than processing all possible data. This selective extraction maintains prediction accuracy while reducing system complexity and data processing requirements.
Solution Approach 2:
The system pre-calculates and stores stay time data for each customer in each place before prediction is needed. This preliminary data preparation reduces the computational burden during actual prediction operations, resolving the contradiction between accuracy and complexity.
2Measurement precision
If the system collects detailed behavior data for all customers, then the estimation accuracy of opportunity loss improves, but the data processing time and computational resources increase
Solution Approach 1:
The system pre-calculates stay time data and stores it in advance, so that when opportunity loss estimation is needed, the data is already prepared and readily available. This eliminates the need for real-time calculation, maintaining accuracy while reducing processing time.
Solution Approach 2:
The system automatically calculates and stores behavior data without requiring manual intervention or complex real-time processing. The automated data collection and pre-processing reduce computational burden during critical estimation operations.
3Productivity
If the system focuses only on places with long stay times, then the processing efficiency improves, but the detection precision of potential purchase opportunities decreases
Solution Approach 1:
The system applies a threshold-based approach by focusing on places where stay time exceeds a predetermined value. This partial action approach maintains processing efficiency while still capturing significant purchase opportunities, balancing efficiency and detection precision.
Data Source
AI summary
An information processing apparatus includes an acquisition section that acquires first information indicating whether or not a target person performs a specific behavior on a target object disposed in plural places and second information indicating a behavior of the target person and including a stay time in the plural places, for each target person, a calculation section that calculates an evaluation value indicating a probability of the target person who has not performed the specific behavior performing the specific behavior on the target object, based on the acquired first information, and an estimation section that extracts data on the target object disposed in the place having a stay time which is smaller than a predetermined value, based on the acquired second information, and estimates an opportunity loss for the target object based on the evaluation value calculated for the target object.