Retail Sensor Analytics for Actionable Customer Interaction Metrics
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Solution Overview
Problem
Existing methods for gathering customer data in brick-and-mortar retail environments often rely on customer surveys, which may not accurately reflect actual customer behavior, leading to suboptimal customer experience improvements.
Innovation Solution
A system utilizing sensors, machine-learning models, and computer-vision algorithms to analyze user interactions with items in a retail environment, generating actionable metrics such as interaction times and item comparisons, and presenting this data through graphical user interfaces (GUIs) to vendors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customer surveys are used to gather data, then data collection is simple and low-cost, but the accuracy of customer behavior representation deteriorates
Solution Approach 1:
The patent replaces manual survey-based data collection with automated sensor-based monitoring systems. Sensors capture actual customer interactions with products (picking up, examining, purchasing) and use machine learning models to analyze this data, substituting the mechanical survey process with automated detection and analysis systems that provide more accurate behavioral data.
Solution Approach 2:
The patent introduces sensors and machine learning models as intermediaries between customers and the data collection process. These intermediaries automatically capture and interpret customer behaviors without requiring direct customer participation in surveys, thereby improving measurement accuracy while reducing the complexity of direct customer engagement.
2Measurement precision
If sensors and machine learning models are deployed to track customer interactions, then measurement precision of customer behavior improves, but device complexity and implementation cost increase
Solution Approach 1:
The patent designs a multi-functional system where sensors serve multiple purposes: detecting customer presence, tracking product interactions, and capturing purchase data. The machine learning models perform multiple analysis functions including identifying customer intent, predicting purchases, and generating actionable metrics, thereby reducing the need for separate specialized systems.
Solution Approach 2:
The system employs machine learning models that automatically analyze sensor data and generate insights without requiring manual intervention. The models self-train on collected data, automatically adjust to different retail environments, and generate actionable metrics autonomously, reducing the operational complexity despite the initial deployment complexity.
3Measurement precision
If detailed sensor data is collected and analyzed in real-time, then customer behavior insights accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent pre-processes sensor data by filtering and organizing it into structured formats before detailed analysis. Machine learning models are pre-trained on historical data to quickly process new inputs, and the system prioritizes processing of high-value interactions, thereby reducing real-time processing time while maintaining insight accuracy.
Solution Approach 2:
The patent divides customer interaction data into discrete events (product pickup, examination duration, purchase decision) and processes them through specialized analysis modules. This segmentation allows parallel processing of different interaction types, reducing overall processing time while maintaining comprehensive analysis accuracy.
Data Source
AI summary
This disclosure is directed to systems and techniques for generating actionable metrics data based on sensor data generated from one or more sensors within an environment. For instance, a brick-and-mortar retail environment or other materials handling facility may include one or more sensors, such as overhead cameras, which generate image data as users interact with items in the environment. Machine-learning models and/or computer-vision algorithms may then analyze the resulting image data to identify the interactions between the users and the items. The system may then generate metrics data indicating varying metrics associated with these interactions, which may be used to generate graphical user interfaces (GUI) for presenting the metrics data to vendors, brand owners, and the like.