In-Store Sensor Modeling for Scalable Price Sensitivity Scoring
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Solution Overview
Problem
Existing systems lack an effective method to determine a user's price sensitivity for online concierge services, which is crucial for personalized recommendations and operational insights.
Innovation Solution
A trained model is used to predict a user's price sensitivity based on input data from in-store sensors, including replacement data and in-store behavior, to generate a price sensitivity score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation methods are used to determine user price sensitivity, then measurement precision may be adequate for individual cases, but productivity is severely limited and cannot scale to large user bases
Solution Approach 1:
The patent replaces manual observation methods with an automated machine learning model that processes sensor data from shopping carts and user interactions. This substitution enables the system to evaluate price sensitivity for large numbers of users simultaneously, resolving the contradiction between measurement precision and productivity by automating the previously manual assessment process.
Solution Approach 2:
The system enables users to indirectly contribute to their own price sensitivity profiling through their natural shopping behaviors captured by sensors. The machine learning model automatically processes this self-generated data without requiring manual intervention, allowing the system to scale to large user bases while maintaining accurate price sensitivity determination.
2Productivity
If in-store sensors and machine learning models are deployed to determine price sensitivity at scale, then productivity increases significantly, but device complexity and system infrastructure requirements increase
Solution Approach 1:
The patent integrates multiple functions into a unified system: shopping cart sensors capture both shopping behavior data and price sensitivity indicators, while the machine learning model simultaneously processes various data types (sensor readings, user interactions, purchase history) to generate comprehensive price sensitivity profiles. This multi-functionality reduces the need for separate dedicated systems, managing complexity while enabling large-scale processing.
Solution Approach 2:
The machine learning model serves as an intermediary layer that translates complex sensor data and user behavior patterns into simplified price sensitivity scores. This intermediary processing abstracts the complexity of raw sensor data, enabling the system to handle large user bases without proportionally increasing operational complexity.
3Measurement precision
If comprehensive user behavior data is collected through in-store sensors, then measurement precision for price sensitivity improves, but loss of information privacy and user data security increases
Solution Approach 1:
The patent extracts only the essential features needed for price sensitivity determination from comprehensive sensor data, rather than storing or processing all raw data. The machine learning model identifies and extracts key behavioral patterns (item substitutions, price point responses, shopping cart contents) while discarding irrelevant information, thereby maintaining measurement precision while reducing privacy exposure.
Solution Approach 2:
The system transforms raw sensor data into aggregated statistical parameters and behavioral metrics that preserve price sensitivity information while reducing identifiable personal details. By changing the data representation from granular sensor readings to processed behavioral parameters, the system maintains prediction accuracy while mitigating privacy concerns.
Data Source
AI summary
A trained model is used to determine a price sensitivity feature for a user of an online system. The online system generates input data by gathering replacement data via a user interface at a device associated with the user and/or in-store behavior data related to replacement of items performed by the user at a location of a retailer when using a physical receptacle in communication with the online system. The online system applies a price sensitivity model to predict, based on the input data, a price sensitivity score for the user indicative of the price sensitivity feature of the user. The online system identifies, based on the price sensitivity score, one or more actions related to prompting the user to convert one or more items. The online system applies the one or more actions to prompt the user to convert the one or more items.