Big Data has applications in practically every sector, and large-scale distribution is no exception. Consumer purchases at the point of sale, items displayed on an online site or data deriving from loyalty cards are all information that can become central to a data-driven strategy. A correct implementation of Analytics allows one to leverage this data to follow the consumers' purchase path, enhance their experience, increase their satisfaction and stem their churn rate. Each activity generates data. For this reason, Big Data is now the real catalyst for the new retail experience both aimed at the end customer and aimed at improving productivity and the organization of human resources.
The Internet of Things (IoT) allows large-scale retail companies to monitor the effectiveness of promotions or the availability of goods in real time. Leveraging data allows companies to anticipate the demand for a given product and thus guarantee a top-level customer experience. A possible application of Big Data is to customize a limited-time promotion in real time, exploiting sentiment on social networks or weather forecasts to get the best economic return. Thanks to Big Data, a company can also react quickly to inventory problems or any default by a supplier. Their real-time analysis guarantees reliable data and precise information in order to avoid a distribution problem which, on large-scale perspective, can mean significant economic and image damage and could have a domino effect on other partners in the same supply chain. Likewise, it is part of Big Data applications to record an increase in sales of a product category and to be able to meet this growing demand quickly, immediately organizing a more extensive supply in the points of sale. The extraction of complex models from Big Data also allows the introduction of Dynamic Pricing, particularly felt in the management of expiring fresh food. The use of predictive models allows companies to set the minimum discount applicable to an expiring product that still guarantees its sale before it becomes a deadweight loss for the company.
Big Data that is generated in the points of sale and by e-commerce portals can be exploited to implement effective and - above all - personalized marketing campaigns. By evaluating the specificity of the individual consumer (such as the time elapsed between the last purchase and the previous one or the items purchased) and by recording data online and offline, Decathlon, for example, has organized an ad hoc data-driven strategy for its business. Effectively managing Big Data allows the company to know the impact on sales that the opening of another nearby Decathlon point will have on an existing store. Another application of Big Data in large-scale distribution is to exploit data from purchases or loyalty cards for personalized marketing based on predictive analysis. The analysis of this data allows us to define purchasing habits, price sensitivity and lifestyle, key dimensions for knowing your customers. Strategies of this type require specific figures, such as data scientists, who are often not internal to the company. In many cases, the support of an external partner is essential to quickly acquire essential analytics skills.
Proper management of Big Data generated by large-scale retail outlets makes it possible to better organize staff shifts or the distribution of sales staff for each floor of the store. The data generated guarantees, for example, the opportunity to understand the effectiveness of a marketing strategy, analyzing the potential customers who have entered the store or visited a specific department. With Big Data it is possible to act on the arrangement of products on the shelves or on how to optimize the spaces of the single store. La Rinascente, for example, has implemented an Analytics strategy so that top managers and store managers could have, within an application, all the data needed to make timely decisions. In doing so, part of the staff can be transferred from an unproductive floor to a busier one, thanks to the insights resulting from Big Data.
By analyzing consumer store behavior through loyalty cards and receipts, large-scale distribution companies can recognize clusters of customers at greatest risk of churn in advance. This is only one of the KPIs (Key Performance Indicators) useful for developing the best strategy for each store. Retail companies can also evaluate how much the customer is actually spending in proportion to his purchasing possibilities or the degree of loyalty of a person to a specific store. This latest application of Big Data makes it possible to generate detailed insights to evaluate a marketing and productivity strategy dedicated to each location. A logic of marketing automation thanks to which you do not lose contact with the purchasing trends and the movements of customers in their stores. Discounts or personalized promotions can help build customer loyalty or keep them from losing interest. The dedicated promotional campaigns also make it possible to reach unloyal consumers. Big Data is leveraged for predictive analytics. For years the managers in the large-scale retail trade have carried out final reports; today, with Big Data, they can act at the dawn of new trends among their customers.