While Xing still leads the way with over two million more users in Germany than LinkedIn, the latter is catching up quickly. Now there are 80 employees in the Bavarian capital, and the number just keeps getting higher – reflecting the platform's rising user numbers. By 2016, the team which had grown to 60 staff moved to another office in a shopping centre in downtown Munich.

When the first LinkedIn office opened in Munich in 2011, there were only five employees at the time.

But it may not be long before others catch up.In terms of social media networks having to do with business and employment, there are only really two big players in the German market: Xing and LinkedIn.īut LinkedIn – an American company owned by Microsoft – is now growing faster than Xing, a German company which is headquartered in Hamburg, reports Süddeutsche Zeitung (SZ), adding that LinkedIn has been “a success story in Germany.” The degree to which the company has yielded control to an algorithm, he says, is extremely unusual. Otto’s experience also underlines that ordinary companies can use AI, not just giants such as Amazon and Google, notes Dave Selinger, a retailing-technology expert and former data scientist at Amazon. In many cases AI will not affect a firm’s overall headcount, but will perform tasks at a level of productivity that people could not achieve. Otto did not fire anyone as a result of its new algorithmic approach: it hired more, instead. The initiative suggests that an important role of AI in business may be simply to make existing processes work better. Customers get their items sooner, which improves retention over time, and the technology also benefits the environment, because fewer packages get dispatched to begin with, or sent back. The new AI system has reduced product returns by more than 2m items a year. Overall, the surplus stock that Otto must hold has declined by a fifth. Online retailing is a natural place for machine-learning technology, notes Nathan Benaich, an investor in AI. It would be impossible for a person to scrutinise the variety of products, colours and sizes that the machine orders. The AI system has proved so reliable-it predicts with 90% 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 analyses around 3bn past transactions and 200 variables (such as past sales, searches on Otto’s site and weather information) to predict what customers will buy a week before they order. A deep-learning algorithm, which was originally designed for particle-physics experiments at the CERN laboratory in Geneva, does the heavy lifting.

Otto went further and created a system using the technology of Blue Yonder, a startup in which it holds a stake. 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. 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.

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.īut customers also dislike multiple shipments they prefer to receive everything at once. Its conventional data analysis showed that customers were less likely to return merchandise if it arrived within two days.

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