Shopper Behavior Analysis via Automated Video Metrics
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Solution Overview
Problem
Current systems for analyzing shopper response to marketing and merchandising stimuli in retail environments lack a scalable framework for data collection and analysis over time, failing to accurately track shopper behavior and predict its influence on purchasing decisions, and do not utilize automated vision algorithms to capture unique demographic and engagement data.
Innovation Solution
A method and system using multiple cameras to capture and analyze shopper behavior over time, employing automated and semi-automated video analysis to extract demographics and behavior data, and correlating this data through shopper interaction funnels to measure exposure, engagement, and conversion metrics for different shopper segments, allowing for optimization of marketing and merchandising strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated vision algorithms are used to collect shopper data, then measurement precision and data uniqueness are improved, but device complexity increases
Solution Approach 1:
The patent replaces manual data collection methods with automated vision algorithms that use computer vision technology to detect, track, and analyze shopper behavior. This substitution of mechanical/manual processes with optical/algorithmic systems enables automated collection of unique data including demographics and engagement metrics without requiring human observers, thereby improving measurement precision while the system handles the complexity internally.
2Productivity
If a scalable framework for data collection over large periods is implemented, then productivity and analysis capability are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system implements self-service capabilities through automated data collection and processing pipelines that operate continuously without manual intervention. The framework automatically captures video data, processes it through vision algorithms, extracts behavioral metrics, and stores results in databases, enabling scalable long-term data collection where the system serves itself by handling all operations autonomously.
Solution Approach 2:
The patent establishes a pre-configured scalable framework with predefined data collection parameters, processing pipelines, and analysis models before data collection begins. This preliminary setup includes configuring vision algorithms, defining behavioral metrics, and preparing data storage structures, allowing the system to efficiently collect and analyze data over large periods without requiring complex real-time decision-making during operation.
3Measurement precision
If multiple cameras and automated analysis systems are deployed, then measurement precision and data completeness are improved, but loss of energy and operational cost increase
Solution Approach 1:
The system employs partial action by deploying cameras and analysis resources selectively based on store layout, traffic patterns, and strategic priorities rather than uniformly across all areas. Automated analysis processes focus on extracting specific behavioral metrics from video feeds rather than processing all visual data equally, reducing computational energy requirements while maintaining measurement precision for key shopper interactions.
Data Source
AI summary
The present invention is a method and system for capturing a dataset over time that represents the response of shoppers to a set of marketing and merchandising stimuli or “elements”. The response includes exposure metrics, engagement metrics and conversion metrics for different shopper segments. The shopper segments may be defined by demographics such as gender, age and ethnicity or by the type of trip, such as a quick trip or a fill-up trip. The system comprises a plurality of means for capturing images, such as cameras, covering the area of interest in the vicinity of the marketing or merchandising element. The method comprises automated and semi-automated analysis of the video to extract the shopper behavior and demographics data for computing the defined metrics. The captured data can be further combined with information such a promotions and advertisement outside the store to further enhance the applications of the data collected in-store from the invention. The captured shopper data can be used for many applications such as comparison of the effectiveness of different marketing elements or the relative effectiveness of different types of promotions, variation of the relative effectiveness of different marketing elements over time or differences between the relative effectiveness of a marketing element for different demographic segments.