Surge pricing comes to the supermarket
Online retailers are increasingly using your personal data to decide how much to charge you. And high-street shops are about to get in on the act
By Tim Adams
Jun 4 2017
In 1861 a shopkeeper in Philadelphia revolutionised the retail industry. John Wanamaker, who opened his department store in a Quaker district of the city, introduced price tags for his goods, along with the high-minded slogan: “If everyone was equal before God, then everyone would be equal before price.” The practice caught on. Up until then high-street retailers had generally operated a market-stall system of haggling on most products. Their best prices might be reserved for their best customers. Or they would weigh up each shopper and make a guess at what they could afford to pay and eventually come to an agreement.
Wanamaker’s idea was not all about transparency, however. Fixed pricing changed the relationship between customer and store in fundamental ways. It created the possibilities of price wars, loss leaders, promotional prices and sales. For the first time people were invited to enter stores without the implied obligation to buy anything (until then shops had been more like restaurants; you went in on the understanding that you wouldn’t leave without making a purchase). Now customers could come in and look and wander and perhaps be seduced. Shopping had been invented.
For the last 150 years or so, Wanamaker’s fixed-price principle has been a norm on the high street. Shoppers might expect the price of bread or fish or vegetables to go down at the end of a day, or when they neared a sell-by date, but they would not expect prices to fluctuate very often on durable goods, and they would never expect the person behind them in the queue to be offered a different price to the one they were paying. That idea is no longer secure. Technology, for better and worse, through the appliance of big data and machine intelligence, can now transport us back to the shopping days of before 1861.
The notion of “dynamic pricing” has long been familiar to anyone booking a train ticket, a hotel room or holiday (Expedia might offer thousands of price changes for an overnight stay in a particular location in a single day). We are used to prices fluctuating hour by hour, apparently according to availability. Uber, meanwhile, has introduced – and been criticised for – “surge pricing”, making rapid adjustments to the fares on its platform in response to changes in demand. During the recent tube strikes in London, prices for cab journeys ‘automatically” leapt 400%. (The company argued that by raising fares it was able to encourage more taxi drivers to take to the streets during busy times, helping the consumer.)
What we are less aware of is the way that both principles have also invaded all aspects of online retailing – and that pricing policies are not only dependent on availability or stock, but also, increasingly, on the data that has been stored and kept about your shopping history. If you are an impulse buyer, or a full-price shopper or a bargain hunter, online retailers are increasingly likely to see you coming. Not only that: there is evidence to suggest that calculations about what you will be prepared to pay for a given product are made from knowledge of your postcode, who your friends are, what your credit rating looks like and any of the thousands of other data points you have left behind as cookie crumbs in your browsing history.
Facebook has about 100 data points on each of its 2 billion users, generally including the value of your home, your regular outgoings and disposable income – the kind of information that bazaar owners the world over might have once tried to intuit. Some brokerage firms offering data to retailers can provide more than 1,500 such points on an individual. Even your technology can brand you as a soft touch. The travel site Orbitz made headlines when it was revealed to have calculated that Apple Mac users were prepared to pay 20-30% more for hotel rooms than users of other brands of computer, and to have adjusted its pricing accordingly.
The algorithms employed by Amazon, with its ever-growing user database, and second-by-second sensitivity to demand, are ever more attuned to our habits and wishes. Websites such as camelcamelcamel.com allow to you monitor the way that best-buy prices on the site fluctuate markedly hour by hour. I watched the price of a new vacuum cleaner I had my eye on – the excitement! – waver like the graph of a dodgy penny stock last week. What is so far less certain is whether those price changes are ever being made just for you. (Amazon insists its price changes are never attempts to gather data on customers’ spending habits, but rather to give shoppers the lowest price available.)