1.3.2: The Retail Industry

The retail industry, like most business-related ventures, has always relied heavily on data of all sorts. The problem of inventory management alone is a problem that is inherent with the retail industry. Store owners require careful planning to know how much inventory to buy and to have on hand at any one time. They must be able to anticipate demand while being aware of the overhead costs associated with keeping inventory in store. Keeping too much inventory can be costly due to the investment and storage of stock that is not selling. Not having enough inventory results in lost sales opportunities. Setting sales prices is another universal problem for the retail industry. Price is not only a function of the cost to the retailer, but also of demand for the product and competition from other stores. A retailer may decide to reduce profit for certain goods by having a sale, in the hopes that customers will buy other more profitable products when visiting the store. Advertising can be costly, and a retailer needs to be able to determine what the net effect may be to the profit margin of the store for each dollar invested in advertising. These problems are decision-making issues based on data. Statistical and mathematical methodologies, often used in conjunction with economic and business principles, have proven themselves useful when solving these problems. It is now not uncommon for an undergraduate degree in business to include at least one course in statistics and data management.

By the 1980s it was not uncommon to have computer-based point-of-sale systems in many retail operations. These systems were often linked, or networked, together so that the business would have access to large amounts of information about daily sales trends. As the computer systems became more complex, so did the data stored by these systems. By the new millennium one could track what individual products were purchased with what other individual products on a regular basis. In many cases, these purchase profiles could be linked to individual customer accounts. While data were inexpensive and easy to collect, it was not always obvious what should be done with the massive amounts of data that retail businesses were collecting.

The advent of advanced statistical and data science algorithms, along with the significant increase in computational power, brought the data to retail decision-makers in a way that was useful and timely. In the modern retail industry, targeted marketing can be specialized down to the level of a single customer. Complex interactive trends in consumer behavior are also being studied at a level never before possible. For example, it is now possible to model to an unprecedented degree how a change in the price of an item will affect store traffic, the sales of the item in question, and the sales of other items that are often bought along with the item that was put on sale.

As the introduction to this chapter demonstrates, the use of data and statistical algorithms are also crucial to the development and effective deployment of online retail operations. The use of advanced statistical algorithms applied to the large amounts of data available to these retailers is used to try to make the online shopping experience as immersive and helpful as shopping in person. In some ways the online experience can be even more helpful than a traditional store as targeted suggestions and sales can be tailored to individual shoppers.