The impact of big data on the retail industry - 【2020】
big data and retail

The impact of big data on the retail industry

When it comes to big data, the first thing that comes to most people’s minds is the mass of data collected from social networks and other online players like Google and Amazon. Each individual leaves traces of their behavior in everyday life that companies want to use to their advantage. Big Data refers to a gigantic amount of unstructured data material that is fed at high speed from various sources. On the other hand, it also refers to the methods used by actors, such as companies, etc., to organize this flood of information in a meaningful way and to draw conclusions from it.

Big Data is also becoming increasingly relevant to the retail sector. Data analysis can help decide how goods are organized in the supermarket or how prices are set.

Data grows and potential applications increase

Experts estimate that 300 times more data will be generated in 2020 than in 2005, which means that companies will not only have the great challenge of extracting improvements for business processes from raw materials, but also of taking into account the ethical component. After all, the customer naturally wants to benefit from the best offer for him – but without his data being recorded, stored and analyzed in any way. Amazon and Zalando show how online behavior and the resulting data volumes can be used, for example, to recommend other products.

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Possible applications for Big Data range from marketing to logistics and distribution of goods. By combining the information, new, more detailed target groups can be formed or even purchasing behavior can be predicted, for example. The world’s largest retailer, Walmart, for example, discovered that certain types of cornflakes sold particularly well in the United States following hurricane warnings and subsequently adapted its logistics to the weather forecast. So-called sentiment analysis on social networks should also enable brands and companies to read the willingness to buy. One program examines what is said about the product or company on Facebook and Co. and tries to read the current mood.

Many different factors must also be taken into account when it comes to the question of optimal price: How cheap is the competition, what is the current demand, how full are the stores? Yes, perhaps in the future too: What is the climate like? Electronic price tags would allow for relatively spontaneous changes. This so-called “dynamic pricing”, based on algorithms, is already common practice in online trading.

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Here, too, external factors must be taken into account, such as how the climate can affect purchasing behavior and product selection. “A modern and flexible business intelligence and analysis landscape is increasingly becoming a decisive success factor in retail,” says Lidl’s IT Director Alexander Sonnenmoser. In the future, data from web shops, mobile devices and social networks could be used to identify possible correlations and increase sales.

Retailers will have to adapt if they want to win

So far, retailers are still rather reluctant to use large data: According to studies by the University of Potsdam and SAS, retailers only evaluate between 20 and 50 percent of the available data. Moreover, the findings will not be systematically incorporated into future measures, criticizes study leader Norbert Gronau. “The main reasons for this low utilization are the lack of technical expertise and the scarcity of internal resources,” he explains. Therefore, Big Data is mainly used in customer loyalty programs and campaigns.

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In this context, beacons, for example, provide retailers with tools for a wider use of big data. With the help of wireless sensors, retailers could determine the position, length of stay and movement of a customer in the store and then offer individual discounts along with other contextual information, for example.

Similarly, customer advisors could be assigned to different areas of a store if a large number of customers are detected in the store. Companies such as Minodes support stationary retailers in data entry, for example, by using Wlan, Bluetooth or cameras. In Israel, for example, a supermarket chain commissioned Minodes to find out when and how queues are formed and to develop automated communication to inform staff.

Investing in such a system still scares many retailers, but at the same time many are also sceptical about the true expertise and success that Big Data can offer. However, the pressure to innovate in the stationary trade is constantly increasing as more sales are channeled into e-commerce. And this is where companies are originally well equipped for Big Data.

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