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Online retail trading has an advantage in terms of knowing a customer's buying behaviour and individual preferences, since the necessary data is already available. In brick-and-mortar shops, the link to customers – e.g. via customer loyalty programmes such as Payback – must first be established. Once this has been done, the data collected can be used to predict customer behaviour with the help of AI (or to put it more precisely, customer segmentation, shopping basket analysis and collaborative filtering) in order to make predictions. This enables the retailer to send appropriate push messages to the consumer via smartphone or email and to present them with personalised products or coupons.
External data on socio-demographics can be added to the customer segmentation if need be. With the help of AI, users are able to precisely characterise customer behaviour, correlate it with other buyers and, finally, forecast it. This personalised approach significantly increases marketing efficiency. One example of this is the German food discounter Lidl, which has been working with an app-based customer loyalty programme since 2019. This programme creates customer profiles in brick-and-mortar shops and can deliver personalised offers and coupons for individual consumers via the app.
Product range management is the central component of modern category management. Based on detailed customer segmentation and a customer needs analysis, it includes product range determination and the creation of planograms, i.e. the placement of merchandise on the shelf.
Predicting which articles will be relevant in the future or are in line with the latest trend is hard to do manually. Although companies do have historical data such as sales, data from loyalty programmes, etc. at their disposal, there is often a lack of any joint evaluation and reference to one another. The correlating trend data from social media or search engines are also not included. This is precisely where AI technologies can enable a holistic explorative analysis of all available data pools: a link should be established both between internal data pools as well as between internal data and external data from search engines and social media. This means that AI can identify trends and dependencies on the basis of analytical models so as to suggest products that retailers should include in their product range or whose positioning they should expand.
Since trends make themselves apparent in external data earlier on than in sales figures, retailers can respond much faster and more specifically to developments. It is also a faster way to identify unknown dependencies, such as between promotional business and normal business. This leads to increased accuracy, which not only increases sales but also reduces write-offs. For example, the German drug store chain Rossmann applies an AI solution that uses historical sales figures and external trend data to predict consumer trends over the next 18 months.
To sum up, it can be said that the retail trade has also arrived in a digital age that the human brain can hardly comprehend. Fortunately, however, that does not have to be the case, because with advancing AI technologies, retailers today can get to know their customers on a whole new level, not only optimising their own processes and sales, but also offering customers a completely new shopping experience.
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