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Big Data to increase e-tailer profits

Big Data to increase e-tailer profits

Researchers have found a way to boost online retailer profits by up to four per cent

Researchers have devised a new strategy which uses real-time big data to deliver e-tailers improved service and a profit boost of up to four per cent.

New research by academics from Warwick Business School, Lancaster University Management School and the University of Southampton have come up with a new analytic approach that helps retailers to decide when to incentivise customers - by, for example, lowering delivery fees - in which area and in which time slots, all in real time.

The new approach was tested using real shopping data from a major e-grocer in the UK over a period of six months and generated a four per cent increase in profits on average in a simulation study, outperforming traditional delivery pricing policies.

As tablet and smartphone usage becomes more widespread, shopping online has become quicker and easier and the speed of delivery has become critical in the online fulfilment race.

The group of researchers, which includes Arne Strauss, Assistant Professor of Operational Research at Warwick Business School, propose an analytic approach that will predict when people want their shopping delivered depending on what delivery prices (or incentives such as discounts or loyalty points) are being quoted for different delivery time slots.

It takes into account accepted orders to date as well as orders that are still expected to come in.

Dr Strauss said, traditionally, online retailers would collect orders including delivery time requests until a certain cut-off time and plan their delivery schedule accordingly.

“Therefore, maximising profits is a problem because the final set of orders for a given delivery day are not known until shortly beforehand, yet decisions on the pricing of delivery time ‘slots’ have to be made in advance based on an estimate,” he said.

“With our new approach we demonstrate that analysing the customer data which is already at retailers’ fingertips and using it to predict the impact of future expected orders in the estimation of delivery costs produces higher profits than only using orders accepted to date in this estimation.

“Our model can outperform the static two-tier delivery pricing policies that are often found in practice by around four per cent in profit.

"In an industry that operates on very small margins, this profit potential is significant.”

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Tags anlayticsArne StraussAssistant Professor of Operational Research at Warwick Business SchoolLancaster University Management School and the University of Southamptonbig dataWarwick Business School

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