A glimpse into the future of retailing is available in a smallish office in Hamburg. From there Otto, a German e-commerce merchant, is using artificial intelligence to improve its activities. The company already is deploying the technology to make decisions at a scale, speed and accuracy that surpass the capabilities of its human employees.
Big data and “machine learning” have been used in retailing for years, notably by Amazon, the e-commerce giant. The idea is to collect and analyze quantities of information to understand consumer tastes, recommend products to people and personalize websites for customers.
Otto’s work stands out because it already is automating business decisions that go beyond customer management. The most important is trying to lower returns of products, which cost the company millions of euros a year.
Its conventional data analysis suggested that customers were less likely to return merchandise if it arrived within two days. Anything longer spelled trouble: A customer might spot the product in a shop for one euro less and buy it, forcing Otto to forgo the sale and eat the shipping costs.
Customers also dislike multiple shipments, however, preferring to receive everything at once. Since Otto sells merchandise from other brands, and does not stock those goods itself, it is hard to avoid one of the two evils: shipping delays until all the orders are ready for fulfilment or lots of boxes arriving at different times.
The typical solution would be slightly better forecasting by humans of what customers are going to buy, so that a few goods could be ordered ahead of time. Otto went further and created a system using the technology of Blue Yonder, a startup in which it holds a stake.
A deep-learning algorithm, which originally was designed for particle-physics experiments at the CERN laboratory in Geneva, does the heavy lifting. It analyses around 3 billion past transactions and 200 variables, including past sales, searches on Otto’s website and weather information, to predict what customers will buy, a week before they order.
The AI system has proven so reliable—it predicts with 90 percent accuracy what will be sold within 30 days—that Otto allows it automatically to purchase around 200,000 items a month from third-party brands with no human intervention. It would be impossible for a person to scrutinize the variety of products, colors and sizes that the machine orders.
Online retailing is a natural place for machine-learning technology, said Nathan Benaich, an investor in AI.
Overall, the surplus stock that Otto must hold has declined by a fifth. The new AI system has reduced product returns by more than 2 million items a year. Customers get their items sooner, which improves retention, and the technology also benefits the environment, because fewer packages get dispatched to begin with and fewer get sent back.
The initiative suggests that an important role of AI in business may be simply to make existing processes work better. Otto did not fire anyone as a result of its new algorithmic approach, and in fact has hired more. In many cases AI will not affect a company’s overall headcount, but will perform tasks at a level of productivity that people could not achieve.
© 2017 Economist Newspaper Ltd., London (April 15). All rights reserved. Reprinted with permission.
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