Retail Customer Grouping via Video Analysis and Service Interaction
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Solution Overview
Problem
Existing methods struggle to accurately analyze purchase behavior in retail settings, particularly for group customers, as individual behavior analysis can be misleading and distance-based group determination is prone to errors.
Innovation Solution
An information processing system that analyzes video footage to identify relationships between customers and sales clerks, classifies customers into groups based on service history, and associates behavior types with purchase outcomes, using machine learning models to determine sales clerks and customers and applying penalties to improve group similarity accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individual behavior analysis is used to analyze purchase behavior, then analysis simplicity is improved, but accuracy deteriorates due to misleading individual behavior data
Solution Approach 1:
The patent merges individual customer behaviors into group-level behavior analysis. By collecting behavior data from multiple customers in a store and analyzing them collectively, the system overcomes the limitation of individual behavior analysis while maintaining analytical simplicity through automated processing.
2Device complexity
If distance-based method is used to determine customer groups, then group determination is simplified, but accuracy deteriorates due to errors in group identification
Solution Approach 1:
The patent changes the parameter used for group determination from spatial distance to temporal behavior patterns. By analyzing whether customers exhibit similar behaviors within a predetermined time period, the system achieves more accurate group identification without requiring complex spatial calculations.
3Quantity of substance
If video analysis is used to identify customer-service clerk relationships, then data collection capability is improved, but processing complexity deteriorates
Solution Approach 1:
The system uses automated video analysis technology that self-processes the complex task of identifying relationships between customers and service clerks. By employing machine learning algorithms and automated recognition systems, the complexity of processing is handled by the system itself rather than requiring manual intervention.
Data Source
AI summary
A non-transitory computer-readable recording medium stores therein an information processing program that causes a computer to execute a process, the process including, identifying relationships between a plurality of customers and a sales clerk by analyzing a video in which an inside of a store is captured, identifying customers who received customer services from the sales clerk among the plurality of customers based on the identified relationships between the sales clerk and the plurality of customers, classifying each of the customers into a certain group such that the customers who received the services from the sales clerk belong to different groups, and associating the classified group with behaviors of the customers who belong to the group.