TALKING

In the retail sector , this flood of information goes to an even higher level with loyalty campaigns , increased online transactions , flash offers , price scanners , electronic shelf labeling and even security control challenges when employees can use their own devices ( Bring your Own Device – BYOD ).

When discussing large quantities of data in retail , many people might also think of Artificial Intelligence ( AI ). And why not ? We already see the arrival of this technology in modern self-service ATMs , automatic age verification , facial recognition and confirmation of consumer preferences for product targeting and promotions .

There will be a reduction in payment queues with automatic item recognition and the presentation of fresh products on the checkout screen . Undoubtedly , these data-driven solutions will make grocery shopping increasingly convenient , reduce the need for support team interventions , cut costs and simultaneously enhance the consumer experience .

Furthermore , when managing and optimizing the daily operations of retailers , access to the database is crucial . Just think of operational information such as sales per square meter , customer retention rate , item turnover , average transaction value , employee performance , number of daily visitors or even average dwell time . There are many Key Performance Indicators ( KPIs ) that retailers need to monitor to keep their businesses profitable and competitive .

In this journey , data interpretation is a critical factor for success . The era of information collection is in the past , making way for a new one of analysis and understanding of how specific data can aid business performance .

It is advisable to extract meaning from the immense amount of information we have available . However , it is not easy to interpret it in a way that contributes to retail success without first having a clear strategy and smart tools that make the analysis assertive and more efficient .

In this context , I understand that we have three significant challenges . The first is related to silos , where data is typically stored . Historically , different stages of customer journeys collected other types of information stored in separate databases . This situation persists to this day .

Surprising as it may seem , customer data remains with sales , promotional actions stay with marketing in CRM systems , transactions stay in a Point of Sale ( POS )

Marcelo Sturn , Retail Leader at Diebold Nixdorf Brazil

When discussing large quantities of data in retail , many people might also think of Artificial Intelligence ( AI ). And why not ?

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