Some quantities which can be easily calculated using this data: How many people visited a specific Toyota dealership last month? How many times did someone test drive a car? Who were those people who test drove a car? How many people stopped / started a typical 9-5 job commute pattern? (BLS only dreams of knowing this number.) What was the occupancy of a specific hotel or rental property last month? How many people were on the 1:30 PM flight from LAX to Laguardia last Friday? Who were they? ...
Of course, absolute numbers may be noisy, but diffs from month to month or year to year, with reasonable normalization / averaging, can yield insights at the micro, macro, and individual firm level.
If your quant team is not looking at this data, it should be ;-)
Google Data Collection
Professor Douglas C. Schmidt, Vanderbilt University
August 15, 2018
... Both Android and Chrome send data to Google even in the absence of any user interaction. Our experiments show that a dormant, stationary Android phone (with Chrome active in the background) communicated location information to Google 340 times during a 24-hour period, or at an average of 14 data communications per hour. In fact, location information constituted 35% of all the data samples sent to Google. In contrast, a similar experiment showed that on an iOS Apple device with Safari (where neither Android nor Chrome were used), Google could not collect any appreciable data (location or otherwise) in the absence of a user interaction with the device.
e. After a user starts interacting with an Android phone (e.g. moves around, visits webpages, uses apps), passive communications to Google server domains increase significantly, even in cases where the user did not use any prominent Google applications (i.e. no Google Search, no YouTube, no Gmail, and no Google Maps). This increase is driven largely by data activity from Google’s publisher and advertiser products (e.g. Google Analytics, DoubleClick, AdWords)11. Such data constituted 46% of all requests to Google servers from the Android phone. Google collected location at a 1.4x higher rate compared to the stationary phone experiment with no user interaction. Magnitude wise, Google’s servers communicated 11.6 MB of data per day (or 0.35 GB/month) with the Android device. This experiment suggests that even if a user does not interact with any key Google applications, Google is still able to collect considerable information through its advertiser and publisher products.
f. While using an iOS device, if a user decides to forgo the use of any Google product (i.e. no Android, no Chrome, no Google applications), and visits only non-Google webpages, the number of times data is communicated to Google servers still remains surprisingly high. This communication is driven purely by advertiser/publisher services. The number of times such Google services are called from an iOS device is similar to an Android device. In this experiment, the total magnitude of data communicated to Google servers from an iOS device is found to be approximately half of that from the Android device.
g. Advertising identifiers (which are purportedly “user anonymous” and collect activity data on apps and 3rd-party webpage visits) can get connected with a user’s Google identity. This happens via passing of device-level identification information to Google servers by an Android device. Likewise, the DoubleClick cookie ID (which tracks a user’s activity on the 3rd-party webpages) is another purportedly “user anonymous” identifier that Google can connect to a user’s Google Account if a user accesses a Google application in the same browser in which a 3rd-party webpage was previously accessed. Overall, our findings indicate that Google has the ability to connect the anonymous data collected through passive means with the personal information of the user.