At aiQ, we are committed to providing the most comprehensive and insightful alternative data solutions for the Japanese market. We are excited to announce a significant enhancement to our aiQ Geolocation dataset: the introduction of a new Stay-Type data item. This new feature enables a more granular and precise analysis of foot traffic, leading to significantly improved correlations with corporate KPIs.
What is aiQ Geolocation?
aiQ Geolocation represents the most extensive mobile location-based footprint data available in the Japanese market. Unlike traditional data sources, it captures human movement in detail, providing valuable context for consumption and production activities. This dataset is sourced from Japan’s largest mobile carrier, ensuring a vast and stable pool of base station-derived data compared to GPS-based solutions, offering more consistent and voluminous data.
The primary focus of aiQ Geolocation is on heavy industry manufacturers with plants and factories across Japan. This includes car manufacturers, car parts manufacturers, and semiconductor manufacturers, among others. By tracking the foot traffic of workers at these sites, we provide a measurable indicator of activity levels, allowing for the assessment of operational activities at those locations.
Stay-Type: A New Dimension in Data Analysis
While existing aiQ Geolocation data items (such as geofence (all-day foot traffic) and geofence_ot (nighttime foot traffic)) have proven effective, the introduction of Stay-Type provides users with a more sophisticated approach to analyzing foot traffic based on behavioral attributes inferred from individual smartphone movement patterns.
This addition significantly enhances flexibility and practicality by enabling the selection and analysis of the optimal traffic volume for each location. For instance, by analyzing only “Worker” traffic for a factory, noise from nearby residents can be explicitly excluded, leading to a clearer signal of corporate performance.
Stay-Type: Boosting Corporate KPI Prediction Accuracy
Extensive backtesting has demonstrated that the addition of Stay-Type significantly improves the correlation between foot traffic and year-over-year (YoY) quarterly revenue.
- Period: January 2023 to December 2024
- KPI: Quarterly sales YoY
- aiQ Geolocation: Explored the values with the highest correlation for each stock among geofence and Stay-Type, and compared with geofence
- Lag: Explored the values with the highest correlation for each stock among lags ranging from -1 to +3 months
These results indicate that the Stay-Type data item substantially increases the number of companies for which our foot traffic data achieves a high correlation with corporate KPIs, offering valuable predictive capabilities.
Real-World Examples of Correlation Improvement
Let’s look at some compelling examples from our backtesting that illustrate the power of Stay-Type.
a. Car Manufacturer: SUBARU CORP (7220 JT Equity)
For SUBARU CORP, a transportation equipment manufacturer known for its automobile and aerospace divisions, analyzing the year-over-year change in “Worker” foot traffic dramatically improved the correlation with consolidated quarterly revenue YoY.
- Sales: YoY of Quarterly Sales disclosed by the company (Consolidated)
- aiQ Geolocation: YoY of “Worker”
- Lag: Shifted aiQ Geolocation by +2 months
- Locations: Automobile Factories (Place), Aerospace Plants (Place, Place), HQ Office (Place)
While the existing geofence data showed a correlation of 0.600, the “Worker” Stay-Type data achieved an impressive correlation of 0.983. This highlights how employee foot traffic data can accurately track revenue fluctuations, surpassing existing data.
b. Semiconductor Test Systems: ADVANTEST (6857 JT Equity)
ADVANTEST, a leading semiconductor testing system manufacturer with a top share in automatic testing equipment, also demonstrated significant improvement.
- Sales: YoY of Quarterly Sales disclosed by the company (Consolidated)
- aiQ Geolocation: YoY of “Worker”
- Lag: Shifted aiQ Geolocation by 0 months (= No Shift)
- Locations: R&D (Place, Place, Place, Place), Factory (Place)
The geofence data correlated 0.627, but the “Worker” Stay-Type data elevated this to an impressive 0.900.
c. Electronic Parts Manufacturer: MURATA Manufacturing (6981 JT Equity)
In the case of Murata Manufacturing Co., Ltd., a major electronic component manufacturer, the impact of Stay-Type was particularly striking.
- Sales: YoY of Quarterly Sales disclosed by the company (Consolidated)
- aiQ Geolocation: YoY of “Worker”
- Lag: Shifted aiQ Geolocation by +1 months
- Locations: R&D (Place, Place, Place), Factories (Place, Place), Subsidiary Factories (Place, Place etc.)
While the existing geofence data showed a negative correlation of -0.147, the “Worker” Stay-Type data dramatically improved this to a strong correlation of 0.931, accurately tracking revenue fluctuations.
Unlocking Investment Potential
These examples demonstrate how aiQ Geolocation data, with the added Stay-Type, is a powerful tool for financial institutions seeking to optimize investment decisions and gain a predictive edge. By understanding the complex relationship between human movement and business outcomes, more informed decisions can be made regarding equity analysis, real estate valuation, and the identification of early indicators of market shifts. Our enhanced dataset is specifically optimized to support advanced investment strategies and provide valuable insights for proactive decision-making in the Japanese market.
aiQ is dedicated to offering the most comprehensive and insightful alternative data solutions for the Japanese market. Our aiQ Geolocation dataset, now with the added dimension of Stay-Type, stands as a testament to this commitment, providing unparalleled depth and accuracy.
For a full report or more detailed information on our dataset, please do not hesitate to contact us.
