[46Bit]

Ordering satellite imagery and counting cars is just a weekend project. The last time I looked at ordering imagery, the main obstacle was the minimum order size, so it'd actually scale better for monitoring every store car park than for looking at a single car park.

[avidiax]

So if Macy's parking lots have 11% more cars than the same time last year, is that a buy or a sell? Are people actually buying more, or are they more cash strapped and spending more time looking for value?

[aurareturn]

You’d have to have historical data to see if more cars mean more spending.

[lazide]

It’s an indicator they’re getting more traffic. Which you can then feed into your model to decide if it’s a buy or a sell, based on all other data.

For instance, is the stock and/or expected earnings > 11%, while traffic seems to be only 11% - or vice versa.

[baxtr]

What if Macy‘s parking lots have fewer cars but they’re selling more and more online now?

[helsinkiandrew]

How busy are the car parks by their dispatch center? are the cars staying longer because people are working overtime? how many UPS trucks are visiting?

[baxtr]

Ok fair, so you need to have a model for every one of their revenue streams.

[fragmede]

you buy data flow data from ISPs at all tiers, so even though they're encrypted, knowing how much traffic is going to Macy's.com vs JCPenney.com gives you information you can act on.

We know this is being done, because of reports that say Netflix is X% of Internet traffic. The undredacted reports from those same data sources have much more detail. It's also why some apps that don't appear to have any business model are actually quite valuable.

[jgtrosh]

2024 answer: just train a predictive AI with that rarely measured data and avoid thinking about the innards of the black box.

[ttyprintk]

In terms of parallel construction, can you tell the difference between insider trading and confident-sounding tips from WallStreetBetsLM?