Retailer Preference Scoring via Biometric Sensor Detection
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Solution Overview
Problem
Current systems fail to effectively tailor customer interactions in retail and transportation settings, leading to lost sales due to mismatched levels of interaction between customers and service providers, as they struggle to predict individual preferences for communication and attention.
Innovation Solution
Implementing a system that uses electronic sensors, such as image and audio sensors, to monitor customer behavior and emotions, generating a preference score based on user input, facial expressions, and transaction history to customize the customer service experience by adjusting the level of interaction accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sales associates provide high level of interaction and attention to all shoppers, then shoppers who desire attentive service are satisfied, but shoppers who prefer quiet browsing become frustrated and leave the store
Solution Approach 1:
The system performs preliminary action by detecting shopper presence and determining their interaction preferences before the sales associate engages with them. Sensors capture behavioral data and facial expressions to predict whether the shopper wants high or low interaction, allowing the system to prepare appropriate service levels in advance. This prevents the need for complex real-time adjustments during customer interactions.
Solution Approach 2:
The system implements feedback by continuously monitoring shopper behavior, facial expressions, and interaction responses. This feedback loop allows the system to dynamically adjust the recommended interaction level based on real-time observations, enabling sales associates to adapt their service approach according to actual customer preferences rather than using a one-size-fits-all approach.
2Reliability
If sales associates monitor and respond to every shopper's needs, then customer service quality improves, but sales associates experience increased workload and stress
Solution Approach 1:
The system applies self-service by enabling shoppers to effectively communicate their preferred interaction level through their natural behavior and facial expressions. The system interprets these signals and manages the interaction preferences autonomously, reducing the burden on sales associates. Shoppers essentially 'serve themselves' by expressing their needs through biometric data, and the system handles the coordination without requiring constant associate intervention.
Solution Approach 2:
The system replaces the mechanical system of manual observation and judgment by sales associates with automated sensor-based detection and analysis. Instead of associates needing to visually assess and interpret customer preferences in real-time, electronic sensors capture facial expressions and behavioral data, and algorithms automatically determine interaction preferences, substituting human cognitive effort with automated processing.
3Measurement precision
If the system uses multiple sensors and analysis methods to accurately determine shopper preferences, then customer experience customization improves, but system complexity and implementation cost increase
Solution Approach 1:
The system applies universality by using a multi-functional sensor array where the same sensors (cameras, microphones, movement detectors) serve multiple purposes. These sensors not only detect shopper presence and location but also analyze facial expressions, gestures, and behavioral patterns to determine interaction preferences. This multi-functionality reduces the need for specialized equipment for each measurement task, simplifying overall system implementation while maintaining high measurement precision.
Data Source
AI summary
Systems and methods are provided for providing a customized user experience. Providing a customized user experience may include receiving, from a graphical user interface displayed on a mobile device a user input indicative of a preferred level of interaction by the user, and monitoring, by a sensor, a facial expression of the user. Then, a first preference metric may be assigned to the user based on the monitored facial expression. Providing a customized user experience may further include monitoring, by a sensor, a behavior of the user, assigning a second preference metric based on the monitored behavior, and aggregating at least the user input, the first preference unit, and the second preference metric to generate a preference score of the user. The preference score may be stored in a central database, displayed on a remote device, and used to modify a customer service experience.