Shelves with fresh produce and plants in Billa corso

A question of price

20. May 2016, Editorial

Retailers want to control their sales in a very targeted way with an individual pricing structure. How much a product costs depends on the customer.


big data
data collection
individual prices
personal prices
price differentiation
price discrimination
The same products have always costed different amounts for buyers. In future, prices should be even more exactly tailored to individual customers and their financial resources.

Two customers are standing in the wine aisle of a supermarket. Both are interested in the same wine from the same winemaker, and both receive a coupon for buying the wine because they are regular customers. But Customer A still pays more at the cash point than Customer B. That is price discrimination (and price differentiation). The retailers hope to increase revenues again – finally. Camouflaged under the cover of customer loyalty cards and bonus programmes, variable pricing has been going on for a long time, but now, with the help of technology, prices are to become even more ‘personal’, more tailored to the individual, even more irresistible. Instead of coupons in the mailbox, for example, push notifications on a smartphone the minute the customer walks into the shop. Dynamic prices for mobile consumers.

Customers are increasingly more transparent, prices increasingly less. It’s hard to say who gets the bargain. And who has to pay more.

Price discrimination is not a new concept. Different prices for the same product can be found everywhere. For instance, at markets where sellers and buyers bicker over a few euros. Or, when students have the right to reduced entry tickets. Even classic sales come under price differentiation because some customers purchased the product weeks earlier at the original price. Why have consumers nevertheless accepted the inequality up until now? They could understand how the price functioned. Price structuring today, in contrast, is increasingly less transparent. It is aided by data that is generated from the purchasing behaviour of customers on the internet. Or, it is geared to the online shops they visit (the travel booking site Orbitz apparently always recommends more expensive hotels to Mac users compared to other users). Thanks to cookies – i.e. the information about users that is stored when they browse websites – it possible to recognize a pattern, the user’s preferences, their interests and their willingness to pay for certain products. And even more: An algorithm is created that allows predictions to be made about what will interest the customer in future. Whoever buys a certain item, will also be interested in another according to the statistics. What we know from Amazon becomes reality everywhere.

Great, on the one hand. Customers get the products they like to buy anyway and hopefully even cheaper.

That strengthens customer loyalty, increases satisfaction. On the other hand, no one knows exactly how many are paying how much for what. There are no longer options for comparison to protect consumers from too high prices. This is a problem, because these ‘personal’ prices are not just about discounts and rewards for dutiful customers. Data collection also reveals how much buyers are willing to spend on a product – sometimes it’s even more than what is listed on the price sticker. That’s why one is often advised to regularly delete browser history. For example, if someone searches for a certain airline connection more than once, they will possibly get a higher price the second or third time they go to the airline site or travel portal. Because the increased interest by the user implies that it is important for him, and, therefore, his willingness to pay is higher. Information that possibly costs customers a lot.

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