In this report, Opimas examines how asset managers are using mobile geolocation data to generate alpha. The report explains the process used to generate and gather the data, as well as the corrections and calibration necessary to make the data useful. The report reviews the offerings of the main mobile geolocation data analytics vendors, and provides two case studies looking at examples of how to use, and not use, mobile data when making revenue predictions.
Mobile Data Vendor Landscape
| Table of Contents | Page | ||
|---|---|---|---|
|
Executive Summary |
2 | ||
|
Introduction |
3 | ||
|
How It Works |
6 | ||
| Mobile Device Geolocation | 9 | ||
| Geofencing | 12 | ||
| Data Quality and Normalisation. | 14 | ||
| Use Cases | 17 | ||
| Amusement Parks | 17 | ||
| Retailers | 19 | ||
| Hospitality | 20 | ||
| Manufacturing & Mining | 20 | ||
| Mobile Geolocation Analytics Vendors | 21 | ||
| Advan Research | 25 | ||
| AirSage | 27 | ||
| Cuebiq | 28 | ||
| Factual | 29 | ||
| FourSquare | 30 | ||
| Gravy Analytics | 30 | ||
| Groundtruth | 31 | ||
| Thasos | 32 | ||
| Case Study: Foursquare and CMG | 34 | ||
| Case Study: Advan Research and EXR | 38 | ||
| Looking Forward | 41 | ||
| Broader Geographic Coverage | 41 | ||
| 5G | 41 | ||
| Improving Quality and Uptake | 42 |
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