If you arrive at a convention hotel without a booking in the middle of an exhibition and there is a room available, you will be paying more than the regular tariff. On the other hand, many inner-city hotels that rely on business travellers have cheap weekend rates on Saturday nights designed to attract customers who would not be interested in staying otherwise. The hotel industry has long been aware that customer demand for its product varies, depending on the time and day. As a result, hotel tariffs, which are designed to maximise both revenue and occupancy rates by pricing along customer-demand curves, change. In a nutshell, this is "price optimisation" - adjusting price according to the customer's willingness to pay, or demand-side pricing. The same is true of the airline industry: it is rare to sit next to someone on a plane who has paid the same price as you.
In industries other than these, however, price optimisation has been rare. Most industries price their goods and services based on supply-side factors - how much the product costs to produce plus a margin for profit, for example. But this approach leaves a lot of money on the table. Between 1 and 5 per cent of value is lost across all industries, according to a report by AMR Research, because they do not know enough about their customers' willingness to pay nor do they have the ability to profit from this knowledge.
What's more, studies by McKinsey & Co, A.T. Kearney and others have shown that pricing is the most potent weapon companies have - spending a dollar on improving price is much more profitable than a dollar spent lowering costs or increasing sales. Only with the development of sophisticated predictive modelling and the computing power to handle vast volumes of information has business started to pay attention to the demand side. Understanding how customers respond to price changes is difficult. The necessary data crunching and econometric modelling to find out have not been readily available. But demand-side revenue management techniques are beginning to spread from the established industries - hotels and airlines - to other industries such as retail and telecommunications.
According to business intelligence software specialists SAS, the Australian retail sector is about to undergo a pricing revolution, following in the footsteps of retail industry mavericks in North America. Retailers such as The Home Depot and Kohl's in the United States, and Hudson's Bay Co in Canada, have implemented complex systems that enable them to price a vast range of goods with scientific accuracy. "Kohl's has grown enormously over the past few years, and the company itself argues that it has been able to do so because it uses sophisticated analytics to develop its pricing strategy," SAS general manager, customer intelligence and retail, Mark Jeffrey, says.
Price optimisation processes help retailers in three ways, Jeffrey says. First, they enable companies to determine the best promotion strategy. By discounting goods at various levels and recording customer reactions, retailers can determine what levels will maximise price against volume. This may vary between regions and stores. Second, they can help retailers determine how much to mark down end-of-season or end-of-stock products. "You can use the analytics to determine the appropriate price to clear the amount of stock that you have - otherwise you might be discounting too much or too little," he says. And third, understanding price-sensitivity helps retailers figure out what a normal everyday price should be, rather than simply relying on cost-plus or more haphazard pricing methods.
Price optimisation systems are big and expensive, but Jeffrey says they can produce an impressive return on investment. And if he has his way, consumers will soon pay varying prices for their jumpers and toothpaste, just like they do for hotel rooms and airline seats.
Sidebar:
What is it?
Price optimisation - pricing goods and services along customer-demand curves to squeeze more money out of a market.
Who will it affect?
Hotels and airlines have been typical users, but the technique is spreading into retail, automotive, transport, telecommunications and other industries where there is a great volume of customer data.
What will it cost?
It's expensive - licence fees for price optimisation software range from $US300,000 to $US1 million.
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