Posts Tagged 'differential pricing'

Asymmetric haggling

I’ve pointed out before that the real threat to privacy comes not from governments, but from multinationals; and the key model of you that they want to build is how much you’re willing to pay for products and services. They can then use this information directly (airlines, Amazon) or sell it to others (Google).

We’re used to a world in which products and services cost the same for every buyer, but this world is rapidly disappearing. There’s a good article about the state of play in the May issue of the Atlantic:

https://www.theatlantic.com/magazine/archive/2017/05/how-online-shopping-makes-suckers-of-us-all/521448/

The price are quoted in an online setting already depends on the time of day, what platform you’re using, and your previous browsing history.

Of course, fixed prices are a relatively new invention, and haggling is still how prices are determined in much of the world. The difference in the online world isĀ  that the seller has much more data, and modelling capability, to work out what you’re willing to pay than you have about how cheaply the seller is willing to sell. So not only is pricing an adversarial process, it’s become a highly asymmetric one.

This is going to have all sorts of unforeseen consequences: analytics for buyers; buying surrogates; a return to bricks and mortar shopping for some people; less overall buying, … We’ll find out.

In the meantime, it’s always a good idea to shop online using multiple browsers on multiple platforms, deleting cookies and other trackers as much as you can.

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“But I don’t have anything to hide” Part II

In an earlier post, I pointed out that differential pricing — the ability of businesses to charge different people different prices — is one of the killer apps of data analytics. Since the goal of businesses will be to charge everyone the most they’re willing to pay, there are strong reasons why we might not want to be modelled this way, even if “we have nothing to hide”

There’s a second area in which models of everyone might feel like a bad thing — healthcare. As the costs of healthcare rise, there will be strong arguments that individuals need to be participants in maintaining their health. This sounds good — but when big data collection means that a health case provider might charge more to someone who has been smoking (plausible case), overeating (hmm), not exercising enough (hmmm), or any of a number of other lifestyle choices, it begins to be uncomfortable, even if “we have nothing to hide”.

The point of much data analytics by organisations and states is to assess the cost/benefit ratio of interacting with you. Of course, humans (and their organisations) have always made such assessments. What is new is the ability to do so in a fine-grained, pervasive, and never-removable way. What will be lost is the possibility of a fresh start, of redemption, or even of convenient amnesia about aspects of the past.