Data competition: Business Intelligence applications in the U.S. retail industry

What are the biggest features of the U.S. retail industry? Networking? Internationalization? None. In early January this year, The New York Times reported that the biggest feature was data explosion. You can probably imagine the data and computing workload required to trigger an atomic bomb or send a rocket. It is based on high, precise, and sharp computing on massive data, however, this data volume and computing method are common for retail giants such as wolma and Amazon.

The direct cause of data explosion is that data collection awareness and information integration capabilities of major retailers have greatly improved in recent years. For example, the application of Radio Frequency Technology (RFID) in the retail industry not only improves the granularity of data collection, but also enhances the real-time and accuracy of data, which greatly promotes the growth of data volume in the retail industry. According to the New York Times, due to changes in concepts and adoption of new technologies, the data volume of individual retailers has even increased by five times over the past three years. These data are becoming the greatest wealth and resources of retailers. Among them, the "Number" magic wand is exactly the business intelligence analysis software.

After careful investigation, we will find that business intelligence is no longer a department or a local phenomenon in the retail field, but an application that can penetrate the entire industry and penetrate the bone marrow. Nowadays, every cell in the retail industry can be said to be breathing and growing in the air of "Intelligent Analysis and Prediction.

Customer analysis is at the forefront. Each product that flows to the customer's hand will generate a record at the sales front-end to analyze the data, at the macro level, we can grasp the structure, periodic traffic, and profit contribution rates of different customer groups. Each customer can determine the purchase frequency, predict the trend of products, loyalty, and loss that interest them. With these from-point-to-point analysis, you may predict the customer's willingness to consume, take the initiative to provide customers with personalized sales and promotion services, and increase sales and profit margins.

Second, commodity analysis. The core issue is how to determine the best product combination and inventory for each branch, and when to import new products and eliminate old products? How can we classify existing products and add them together or sell them together? How should each item be priced, when the price is reduced, and in what form of promotion? For example, if a hurricane will land in Florida next month, then bottled water, rain gear and other related products should immediately increase the storage volume. What other products should be improved? How much need to be improved? These questions must be answered based on data analysis. Individual analysis requires not only internal data of the enterprise, but also data from external resources such as the market, industry research, and competitors.

The second is the analysis of the supply chain and the analysis of operation efficiency, which includes selecting suppliers, optimizing logistics and cash flow, configuring human resources, and selecting store locations.

These analyses are interlocking and integrated. For example, if you import new goods that are not selling well and stay on shelves, the supermarket may become a "museum". Even if the supply chain and operation efficiency are higher, it will be blank. For example, individual goods are in short supply. If the stock is insufficient or the supply chain is lagging behind, the shelves will be vacant. If you miss out on business opportunities, you will also waste space.

All kinds of forest. In a word, data analysis based on various dimensions is already the norm of the retail industry. These analyses have laid the foundation for various business decisions. To a certain extent, the competition in the retail industry has become the competition for data collection, integration, and analysis capabilities. The ability to integrate and analyze data is becoming the core competitiveness of retailers.

At present, this data-centric competition is still fierce, and more advanced business intelligence applications are beginning to emerge in the retail industry. This is mainly manifested in two aspects. First, the application of mobile commerce intelligence. At&t in the United States has partnered with vaultus software to launch the Retail Business Intelligence mobile solution, A & P, Avon, store managers of American retailers such as TJX company can view dial reports on their mobile phones. Microstrategy, one of the giants of business intelligence, also announced its mobile business intelligence solution. Second, extract customers' opinions through text analysis. It is a kind of unstructured data that customers use to record their consumption experiences, views and comments on consumption behaviors and products on blogs, forums, social websites, and Twitter. How to integrate unstructured text resources distributed on the Internet and explore valuable information for retailers is also one of the hot topics of business intelligence.

Whether looking at the history or the status quo, the retail industry is still unable to take the lead in business intelligence applications, and there are also finance, finance, insurance and other fields in the front row. The comprehensive application of business intelligence in the retail industry tells us that the explosion of industry data will affect the future competition in the industry, and the future competition will be more refined, in terms of data analysis, many industries must enter an era of data competition where data is emerging and data is strong. In this era, data is the direct fortune, and the core competitiveness.

For this data competition, every industry and enterprise in China should be prepared.