Action Prediction Device for Retail Movement Destination Areas
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Solution Overview
Problem
Existing retail store management systems cannot predict the number of people who will purchase products in movement destination areas, limiting their ability to effectively manage inventory and customer engagement.
Innovation Solution
An action predicting device that uses a storage unit to store action and movement probabilities, an acquisition unit to gather population distribution data, and a prediction unit to forecast the population involved in actions in movement destination areas based on this data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a retail store management system uses basic visitor prediction based on causal information, then the system can predict sales volume with simple methods, but the system cannot predict the number of people who will purchase products in movement destination areas
Solution Approach 1:
The system segments the prediction process into distinct components: visitor prediction, movement prediction, and purchasing action prediction. Each component handles a specific aspect of the customer journey, allowing the system to predict purchasing population in movement destination areas by combining these segmented predictions rather than attempting a single complex prediction model
Solution Approach 2:
The system adds spatial dimensionality by incorporating movement destination areas into the prediction model. Instead of only predicting sales at a fixed store location, the system predicts population movement to various destination areas and combines this with action probabilities to forecast purchasing behavior across multiple spatial dimensions
2Measurement precision
If the system predicts population involved in actions using multiple probability factors and population distribution data, then the prediction accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary calculations by pre-computing and storing action probabilities and movement probabilities for different areas and time periods. This allows the prediction unit to quickly retrieve and combine these pre-calculated values with current population distribution data, reducing the computational power needed during actual prediction operations
Data Source
AI summary
A population involved in actions in the movement destination area is predicted. A purchase predicting device 1 includes: a storage unit 10 that stores a prediction coefficient, which is a probability that a person in a predetermined area will be involved in actions, and an average moving population ratio, which is a probability that a person in a predetermined area will move to a movement destination area that is an area of a movement destination; an acquisition unit 11 that acquires population distribution data regarding a population for each area; and a prediction unit 13 that predicts a population involved in purchasing for each movement destination area based on the population distribution data acquired by the acquisition unit 11 and the prediction coefficient and the average moving population ratio stored in the storage unit 10.