Point-of-Sale Data Correspondence Using Video Analysis
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Solution Overview
Problem
Current retail systems fail to effectively match point-of-sale data with shopper behavior data from in-store videos due to cost and physical constraints, limiting understanding of consumer behavior at the point of purchase.
Innovation Solution
A method and system that constructs binary tables and computes conditional probability distributions to establish correspondences between point-of-sale data and shopper behavior data using video analysis, incorporating item-shopper and event association matrices with constraints to solve the correspondence problem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video cameras are deployed throughout the store to track shopper behavior, then measurement precision of consumer behavior is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts only the necessary behavioral data from video streams using computer vision algorithms, focusing on key actions like product pickup, placement in cart, and checkout associations. This selective extraction approach maintains measurement precision for critical behaviors while avoiding the complexity of comprehensive video surveillance throughout the entire store.
Solution Approach 2:
The patent introduces an intermediary data matching system that connects video-derived behavioral data with POS transaction data through a correlation engine. This intermediary layer enables precise behavioral measurement without requiring direct visual tracking of every customer throughout the store, instead using strategic video points combined with transaction data for comprehensive analysis.
2Reliability
If complete shopper tracking is implemented, then reliability of customer behavior data is improved, but loss of time and computational resources increase
Solution Approach 1:
The patent segments the shopper journey into distinct behavioral phases (product inspection, pickup, cart placement, checkout) and analyzes each phase separately using targeted video analysis. This segmentation approach maintains reliability by capturing complete behavioral sequences while reducing computational time compared to continuous full-store tracking.
Solution Approach 2:
The patent performs preliminary data processing and feature extraction from video streams in advance, pre-processing behavioral data before the final correlation with POS data. This preliminary action reduces the computational burden during the main analysis phase while maintaining the reliability of the matched behavioral-transaction data.
3Measurement precision
If video analysis is performed for all customers, then measurement precision of purchase behavior is improved, but productivity of data processing decreases
Solution Approach 1:
The patent applies local quality analysis by focusing video analysis on specific areas of interest within the store (such as checkout zones, product aisles, and cart corridors) rather than uniformly analyzing the entire store. This targeted approach maintains measurement precision for critical purchase behaviors while significantly improving processing productivity.
Solution Approach 2:
The patent applies partial action by analyzing only the essential behavioral cues needed for purchase decision modeling (product pickup, cart placement, checkout actions) rather than analyzing all possible customer behaviors. This selective analysis maintains sufficient measurement precision while dramatically improving data processing productivity.
Data Source
AI summary
The present invention is a method and system to provide correspondences between point-of-sale data registered at the store checkout and shopper behavior data observed at point-of-purchase through video analysis. The point-of-sale data include the list of shoppers and purchase items, and the shopper behavior data include the purchase events along with observed purchase items. The correspondence in the form of checkout shopper IDs matched to purchase event IDs is derived based on the algebraic constraint among the point-of-sale data and the purchase event data. Additional constraint based on shopper tracks and checkout/event times can also be incorporated to the correspondence problem. Uncertainties due to the video measurement can be systematically handled utilizing a Bayesian model.