Retail: “Anyone who fails to adopt AI will die!”

Applications of Artificial Intelligence are not just ­­to be found in e-commerce. In ­high-street shops, too, self­learning algorithms are helping to balance supply and demand more closely, and understand customers better.

The retail sector involves a complex interaction between customers, manufacturers, logistics service providers and online platforms. To gain a competitive edge, retailers need to gauge their customers’ needs optimally, and fulfil them as efficiently and closely as possible. That means retailers have to make the right choices to find the ideal mix of partners. Self-learning algorithms and AI are opening up new dimensions in process optimisation, personalisation and decision-making accuracy.

Artificial Intelligence enables retailers to respond better to their customers’ needs and, for example, optimise their ordering and delivery processes.

Artificial Intelligence is a question of necessity

Prof. Dr Michael Feindt, founder of Blue Yonder: “Anyone who fails to adopt AI will die! But those who open themselves up to the new technology and make smart use of it will have every chance of achieving sustained success in the retail sector. For retailers, digital transformation through AI is not a question of choice, but of necessity. Only those who change and adopt the new AI technologies will survive.” One way that Blue Yonder is responding to that necessity is with a machine learning solution which optimises sales throughout the season based on automated pricing and discounting. The system measures the correlation between price changes and demand trends at each physical outlet and through each channel. Based on the results, the solution automatically sets prices to increase turnover or profit throughout the selling cycle, including the application of discounted pricing and running sale campaigns as appropriate. It analyses both historical and current sales and product master data, and enables hundreds of prices to be validated and optimised each day. Using such systems, retailers can meet consumers’ rising expectations and maximise their profits at the same time. According to Blue Yonder, this means profit can be improved by 6 per cent, sales turnover increased by 15 per cent, and stocks cut by 15 per cent.

Optimising processes in retail with AI

Artificial Intelligence enables retailers to respond better to their customers’ needs and, for example, optimise their ordering and delivery processes,” says Stephan Tromp, Chief Executive Director of HDE, the German Retail Association. For example, retailers can use their suppliers’ data to measure performance and optimise processes. Combined with the data from their outlets and warehouses, they can also balance supply and demand more closely. For instance, intelligent forecasting systems learn from past orders, create buyer groups, and analyse seasonal effects. From their findings, they can forecast product sales volumes and ideally know before the consumer what he or she is going to order next. This means retailers can tailor their websites to the relevant product groups, trigger purchasing, top up stocks accordingly, and ultimately cut shipping lead times. As a result, bottlenecks in the supply of specific products can be predicted, and retailers can quickly identify which supplier is currently able to deliver top-up stocks of the required merchandise most quickly.

Keeping track of customers’ movements

AI not only has applications in retailers’ back-office operations, however. In the physical shops, too, deep-learning functions are helping to gauge customers’ behaviour. A company called Retailnext, for example, has launched an all-in-one IoT sensor which monitors customers’ movements when in the outlet: collecting their goods, trying on clothing, and walking around the shop. All those movements are monitored by a camera, and analysed directly in the unit with the aid of deep-learning functions. The data is then uploaded to the cloud in real time, so companies can gather valuable information on all the branches in their chain. “It’s precisely those projects that enable retailers to develop a deeper understanding of in-store shopping behaviours and allow them to produce differentiated in-store shopping experiences,” asserts Arun Nair, Co-Founder and Technical Director of Retailnext. “The more retailers know about what’s happening in store, the better.”

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