Dynamic Pricing

by Dave

ack in 2000, Amazon experimented with an embryonic “dynamic pricing” project that charged people different amounts for a DVD, based on who knows what – zipcode, shopping habits, etc.  Consumers spotted this, got annoyed, and so Amazon backed down and allegedly issued refunds to all their customers who were overcharged.

There are lots of firms that engage in “dynamic pricing” today.  If they can identify you as a rich customer who is price insensitive, they’re going to gouge you worse than a tuk-tuk driver in Bangkok.

Mac users may well be charged more than Windows users, for instance.  Frequent fliers are charged more than occasional travelers.  (Ah, the benefit of being a repeat customer – whose data the airline already has).

Seriously.  The only way to survive such indignities is to have a single-use browser session that strips as many identifying marks out of its signature as possible, with your connection made via VPN, preferably appearing to originate from some poor neighborhood in East Bupkis, NJ.  Of course if you have to log in to provide delivery instructions, they’ve got you.  Or – in the future – when Amazon owns retail, you simply won’t have an alternative.  By then, even bricks-and-mortar (Amazon “GO”, naturally) will have “digital price tags”, where the store recognizes you using face recognition (plus your phone’s digital emissions signature), changing prices as appropriate in real time as you wander around, to match your ability and/or willingness to pay.

Once they own retail, there won’t be much you can do about it.

Centralizing profits.  Mr. Global.  That’s the risk anyway.

medium.com/syncedreview/ai-powered-dynamic-pricing-is-everywhere-4271a9939d11

A different and more controversial angle in dynamic pricing is setting different prices for different customers. Many major E-commerce companies prefer not to disclose whether they do this, or may do it discreetly, as the practice can be regarded as a form of discrimination pricing. It’s not unusual for example to see a different price for a travel package when visiting a booking website on your laptop versus the price displayed on a friend’s computer, or even on the booking company’s app on your smartphone.

Online retailers’ dynamic pricing systems build and respond to individual users’ pricing profiles, which can be based on their zip code, device type, the type of products they have browsed and ordered, and other data. Like a savvy car salesman, sellers endeavor to size up the customer to determine how much they can afford; it is thus natural to set a higher price for those who can be expected to pay it.

Back in 2000, Amazon was found to be charging different people different prices. The company apologized and promised it would not set prices based on customer demographics. However even if pricing remains consistent on a specific product, there are other variations that can create personalized pricing scenarios:

  • Create different pricing tiers tailored to different customers
  • Customize product bundles based on users’ pricing profiles
  • Target different customers with different product suggestions at different prices, etc.

Such subtle adjustments in presentation and pricing, augmented by algorithmic dynamic pricing, can also result in some shoppers paying more than others, which benefits the seller.

The expansion of sales platforms using dynamic pricing is making it increasingly difficult to detect potentially unfair pricing schemes or protect oneself against them. Consumers would do well to apply the age old “caveat emptor” not only to the product they are purchasing but also the price tag it carries — even if one’s wits alone will not unravel the mystery and complexities of algorithmic dynamic pricing.

 

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