Tenant Registration via SNS Data Extraction
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Solution Overview
Problem
The process of considering opening a store is time-consuming due to the difficulty in inputting products or services handled by tenants and target customer features, making it challenging to encourage the use of existing matching systems.
Innovation Solution
A computer system that manages space features, acquires social networking service (SNS) account information from tenants, extracts keywords from SNS posts, estimates tenant attributes based on this information, and predicts sales for each combination of tenant attributes and space features, presenting these estimates to the tenants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tenants manually input product/service information and target customer features, then the matching system can accurately match tenants with spaces, but the registration process becomes time-consuming and complex
Solution Approach 1:
The system performs preliminary actions by automatically extracting tenant attributes from SNS data before the matching process begins. This pre-extraction of information from social media profiles eliminates the need for tenants to manually input detailed product/service information and customer features, significantly reducing registration time while maintaining accurate matching capability.
Solution Approach 2:
The system introduces SNS (social networking service) data as an intermediary source to obtain tenant attributes. Instead of direct manual input from tenants, the system uses SNS profiles as a mediator to automatically acquire information about products/services and target customers, thereby reducing the burden on tenants while ensuring accurate matching data.
2Measurement precision
If tenants input detailed information about products, services, and target customers, then the matching system can provide accurate recommendations, but the ease of operation decreases
Solution Approach 1:
The system implements self-service by automatically extracting tenant attributes from SNS data without requiring manual input from tenants. The tenant's SNS profile serves as the source, and the system autonomously processes this information to derive product/service information and target customer features, making the registration process extremely easy while maintaining recommendation accuracy.
Solution Approach 2:
SNS data acts as an intermediary that automatically provides the detailed information needed for accurate matching. Instead of requiring tenants to manually fill out complex forms, the system uses SNS profiles as a mediator to automatically capture and process tenant attributes, significantly improving ease of operation while preserving recommendation precision.
3Productivity
If the system collects and processes SNS information to estimate tenant attributes, then the productivity of the matching system improves, but the device complexity increases
Solution Approach 1:
The system extracts only the necessary attributes from SNS data that are relevant for matching purposes. Instead of processing all SNS information, it selectively extracts tenant attributes such as product/service information and target customer features, thereby improving matching efficiency while avoiding excessive system complexity from processing unnecessary data.
Solution Approach 2:
The system replaces manual mechanical processes of information collection and analysis with automated computational processing of SNS data. By substituting manual tenant input with automated SNS data extraction and analysis, the system improves productivity through efficient automated processing while managing complexity through algorithmic approaches rather than manual procedures.
Data Source
AI summary
A computer system for assisting registration to a service for matching a tenant and a space, in which the computer system manages space features representing characteristics of the space, acquires account information for SNS used by the tenant from the tenant, accesses the SNS using the account information and extracts keywords included in post information as SNS information, estimates tenant attributes representing business characteristics of the tenant based on the SNS information, estimates sales when the space is used for each combination of the tenant attributes and the space features, and presents an estimation result of the sales for each space to the tenant.