The following dialog is from Season 4, Episode 8 of House of Cards:
NSA agent 1: Can you walk us through how the filtering algorithms work?
Aidan: We’ll take firearms, for instance. If we start with everyone who legally owns a gun, track geo-location patterns through their phones, we start to put together a portrait of where gun-owners live, eat, shop, everything. From this, we predict everyone who might want a firearm, but who isn’t registered. They’re likely to exhibit the same behavior as people who are. You can use that for people who are interested in Arabic, uh, wanna travel to the Middle East, who are disillusioned with the U.S. government.
Now, this is a fictional show, but quite a few people have already weighed-in on the plausibility of the above scenario besides another recurring mention (in the show) of a Google-like search engine (Pollyhop) that lets a Presidential nominee manipulate voters’ (users’) behavior through search engine results and analyzing search engine data.
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This could be the future of targeting, starting with offline, moving to online and then ending up offline again.
For example, here is another fictional scenario.
You head marketing for a car brand. You are launching a new small car (hatchback), to be priced very competitively. You want to target users of all other brands’ similarly priced entry-level hatchbacks. How would you go about it?
Using the scenario above,
- You need a phone numbers list of people who own an entry-level hatchback. Considering phone number is a basic detail every car buyer shares with the dealers (either during purchase and/or during service), there is a possibility that you would get a list. Now, this could clearly be against the rules (but then, this happened to me – and if phone number database sharing is kosher between car insurance companies, I’m assuming this is operating in India already) but consider the possibility that you do get it.
- Assuming you have the list, going by the above example, can you track geo-location data of those, and aggregate them to form patterns of where in India they move around the most (the majority)?
- Considering you do have a few prominent roads and areas in top 10 cities (perhaps), you’d need to place your communication there! This may be better data-backed than other modes of arriving at this outcome (popular/most frequented roads in each city, popular landmarks in city etc.)
The opportunity explodes if you use your imagination. For example, if you cross-analyze the apps that this list of people use most often, you could simply advertise within that app, in context (could be a food ordering app, for all you know).
Now, you’d perhaps wager that all this crosses legal boundaries and is clearly against the law. I agree.
But then, an unnamed senior Reliance executive told Reuters yesterday on the eve of the launch of Jio, “It’s called Deep Packet Inspection, and what you can do with the analytics of that is mind-boggling.”
House of Cards/Data pic courtesy Bigwisdom Blog.