Technology & Innovation

Striking a balance on real-time pricing

November 05, 2015

Global

November 05, 2015

Global
Our Editors

The Economist Intelligence Unit

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Changing prices in response to real-time fluctuations in demand allows companies to maximise profits and control supply – but there is a risk of angering the customer

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The US driver network company Uber has created a market virtually out of thin air by connecting drivers and passengers in real-time, and in doing so has earned itself a $50 billion valuation.

Many startups hope to bring this business model to other areas of life. But Uber’s most future-thinking innovation is not its go-between functionality, but the way it manages its network and charges its customers – through real-time dynamic pricing.

As stranded users of its service will know, when demand is high customers are presented with ‘surge pricing’ – a markup that may be several times the usual fare. The idea is that, with greater competition, customers will be willing to pay more and that higher fares will encourage more drivers onto the roads or into busy areas to restore supply.

Surge pricing is made possible by Uber’s ability to monitor the driver network in real time, and act on it to price live services accordingly. It is a model that could certainly be applied to other sectors. But is it fair?

Dynamic pricing is good news for businesses, as it allows them to charge more when people are willing to pay more, or less to stimulate demand when it is scarce.

That kind of matching efficiency is an economist’s dream. Dynamic pricing is not just good for boosting margins – it’s also good for managing demand when there are supply constraints. By incentivising drivers to hit the road when they might not otherwise have bothered, the higher fee increases the chance that customers will find a ride.

Demand-sensitive pricing is not brand new: airlines and hotels already managing pricing in response to demand. But real-time technology is allowing other industries to adopt it too. In energy, smart electricity meters allow suppliers to charge customers according to demand, to discourage extensive use at peak times – allowing them to spread demand, and handle fluctuations in supply.

Advertising, too, has become a real-time industry. With real-time bidding, digital ad spots are bought and sold per impression through real-time auctions. When a user visits a website today, their demographics, location and browsing history are offered to multiple advertisers that then bid against each other to place their ads in front of that person, with the highest bid taking the spot – all within 100 milliseconds as the page loads. This is a long way from the broadcast mass marketing techniques of old.

In the energy sector, real-time data is allowing utilities providers to modify pricing in response to fluctuations in supply and demand. US provider Baltimore Gas and Electric, for example, offers dynamic rates to over 300,000 customers with smart meters installed in their homes. This includes a “peak-time rebate”, which rewards customers for postponing the use of energy-hungry appliances such as dishwasher until after peaks in demand have subsided.

And for retailers, price intelligence is becoming a vital competitive tool. Online retailers scour the prices of competitors and adjust their own automatically. This algorithmic approach is not without its risks, however – competing algorithms have observed driving the costs of books up to an absurd $23 million.

Real-time pricing is not without its critics, however. Some have argued that dynamic pricing comes perilously close to price discrimination – offering different customers different prices based on who they are – which is illegal in some circumstances.

The challenge for organisations is to find a balance between the ability to use real-time data effectively to match supply with demand, and the risk that they will present customers with a price so high that it offends them. The most effective organisations will use the data itself to find where that balance lies.

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