In today's fast-paced world of digital retailing, the ability to revise prices swiftly and on a large scale has emerged as a decisive differentiator for companies. Many retailers now track competitors' prices via systems that scrape rivals' websites and use this information as an input to set their own prices manually or automatically. A common strategy is to charge X dollars or X percent less than a target competitor. However, retailers that use such simple heuristics miss significant opportunities to fine-tune pricing. Some companies are now applying machine-learning models to guide their pricing decisions, but even these retailers tend to take an overly limited approach. They try to match or undercut competitors' prices without taking into account factors such as whether rivals are out of stock or how consumers make their purchasing decisions. In this article, the authors present a step-by-step process for dynamic pricing that focuses on building computer models that consider not just competitor pricing but also product availability and customer behavior to recommend optimal prices in real time.

As they fight for survival in the era of online shopping, brick-and-mortar retailers are cutting costs by slashing head count and budgets for training. But that erodes their biggest edge over e-tailers: a live person customers can talk to face-to-face. For every dollar a retailer saves on staffing, it may be losing several dollars in revenues and gross profits if customers leave stores empty-handed because they can't find a knowledgeable salesperson to help them. The solution lies in optimizing staffing and training for each store, but most retailers don't know how to do that. This article offers them a step-by-step approach. It involves analyzing historical data, conducting experiments, and assessing the results, and when applied systematically can add as much as 20% to the revenues of existing stores. Even better, if staffing increases at some stores are offset by cuts at others, and vendors fund product training, those higher sales will cost retailers little or nothing to generate.

In pursuit of double-digit top-line growth, many retailers relentlessly open new stores, even when doing so destroys the profitability of their businesses. This addiction is fueled by Wall Street and a capitalist culture that's obsessed with growth. It's hard to kick, primarily because companies don't know when or how to turn off the growth machine--or what to replace it with. To explore the problem, the authors studied the financial data of 37 U.S. retailers with recent sales of at least $1 billion whose growth rate had faltered. They found that the less successful retailers had continued to chase growth by opening new stores far past the point of diminishing returns. By contrast, the more successful retailers had drastically curtailed expansion and instead relied on operational improvements at their existing stores to drive additional sales. This allowed them to increase revenues faster than expenses, which had a powerful positive impact on earnings. This article lays out a framework for determining when to switch to a low-growth strategy and how to put it into practice. If retailers execute well, they can stay in the maturity stage of the life cycle for a very long time, forestalling decline.

Getting product assortment right isn't easy, yet it's absolutely critical to retail success. Unlike inventory management and pricing, where retailers have lots of data and analytical tools to guide decision making, assortment optimization is still much more art than science. And making the wrong call can be disastrous. A new approach to assortment planning addresses this deficiency. The foundation for the approach is the notion that most of the time customers don't buy products; they buy a bundle of attributes. For instance, when customers consider buying a TV, they think about screen size, resolution, price, LCD or plasma, and brand. The new method uses sales of existing products to estimate the demand for their various attributes and then uses those estimates to forecast the demand for potential new products on the basis of their attributes. This technique helps retailers do a better job of replacing slow sellers with new ones, understanding whether customers are likely to settle for another choice if they don't find their ideal product, and customizing assortments of individual stores or clusters of stores.