Real-time Cognitive Recommendations for Retail User Behavior
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Solution Overview
Problem
Retail brick and mortar stores face declining foot traffic and sales due to increased online shopping, as they struggle to compete with lower prices and fail to effectively utilize the data generated from real-life user behaviors in-store, such as interactions with products.
Innovation Solution
A computer-implemented method and system that monitors user behaviors during a visit to a venue, tracks item locations, and uses machine learning to predict subsequent behaviors, providing real-time cognitive recommendations based on these interactions and item data, enhancing the shopping experience and boosting sales through personalized item suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If retail stores compete with lower prices like online stores, then they can attract price-sensitive customers, but they cannot compete due to higher overhead costs
Solution Approach 1:
The system enables automated self-service through real-time behavior monitoring and automatic recommendation generation. Sensors detect user interactions with products and automatically generate personalized recommendations without requiring human analyst intervention, reducing operational overhead while maintaining personalized service quality
Solution Approach 2:
The system transforms physical store data into actionable digital insights by changing the state of raw sensor data into processed behavior patterns and recommendations. This parameter transformation enables the store to leverage existing infrastructure for competitive advantage without proportionally increasing overhead costs
2Loss of information
If retail stores do not track user interactions with products, then they save on data collection infrastructure, but they leave valuable behavioral data untapped
Solution Approach 1:
The system uses multi-functional sensors that simultaneously track product interactions, user location, and purchase behavior through a single integrated infrastructure. This universal approach captures comprehensive behavioral data without requiring separate complex systems for each data type, reducing overall device complexity
Solution Approach 2:
The system introduces an intermediary layer of machine learning models that automatically process and interpret raw sensor data. This intermediary transforms complex raw data into actionable insights, reducing the complexity burden on the tracking infrastructure while maximizing information extraction
3Productivity
If retail stores implement real-time recommendation systems, then they can improve shopping experience and boost sales, but they require sophisticated data processing infrastructure
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data and training machine learning models during off-peak periods. This advance preparation reduces the computational complexity during real-time operation, enabling sophisticated recommendations without proportionally increasing system complexity
Solution Approach 2:
The system segments the recommendation process into distinct modules: data collection, behavior analysis, recommendation generation, and delivery. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining high productivity
Data Source
AI summary
User behaviors are monitored, by machine logic, during a visit to a venue by a user, the user behaviors associated with user interactions with items in the venue, a location of the items being tracked. In real-time, based, at least in part, on the user behaviors and the items, a subsequent behavior of the user is predicted, by machine logic, the predicting resulting in predicted behavior(s). Cognitive recommendations are provided, by machine logic, to the user in real-time during the visit, the cognitive recommendations corresponding to additional item(s) based, at least in part, on the predicted behaviors and the items. Machine learning is used to train a system for facilitating the noted aspects, as well as to update training.