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Managing prices has always been an activity of keen interest to businesses, but it has become even more critical to do it well. Over the past decade many companies have found their ability to raise prices has been constrained by intense competition resulting from Internet commerce, global competition and other factors. One tool for dealing with this pressure is price and revenue optimization (PRO), an analytic methodology that calculates how demand varies at different price levels and then uses that algorithm to recommend prices that should optimally balance revenue and profit objectives. Computer-supported PRO began in earnest in the 1980s as the airline and hospitality industries adopted revenue management practices in efforts to maximize returns from less flexible travelers (such as people on business trips) while minimizing the unsold inventory by selling incremental seats on flights or nights in hotel rooms at discounted prices to more discretionary buyers (typically vacationers). Price and revenue optimization algorithms are designed to enable a company to achieve fatter profit margins than are possible with a monolithic pricing strategy. Using PRO, airlines and hotels catering mainly to less price-sensitive business travelers found they could match discounters’ fares and rates to fill available seats and rooms without having to forgo profits from their high-margin customers.

PRO has expanded into other industries as computing power and data storage become ever less expensive, as software vendors have improved their techniques and algorithms to deliver better results and as the software has grown increasingly user-friendly. While the concepts underlying all PRO software are the same, there are different categories in which it is customized to meet the needs of specific industries. Retailers in particular have requirements that are best met by using applications that manage markdowns.

At the heart of price and revenue optimization is the concept of demand-based pricing. As its name suggests, demand-based pricing is a method that sets a price that is controlled by the seller’s assessment of what the buyer is willing to pay, which in turn is based on an estimate of a good’s or a service’s perceived value to the buyer. Companies use demand-based pricing to optimize – rather than simply maximize – their pricing to achieve revenue and profitability objectives. It uses data to estimate where the prospective buyer sits on a demand curve and therefore how much the individual is likely to pay. In some respects this is similar to what happens daily in souks, bazaars and other markets in cultures that do not insist on set prices. However, software makes demand-based pricing practical in large businesses and facilitates its introduction in societies used to set pricing.

Advanced analytic applications – especially for price and revenue optimization – have been gaining ground in corporate management because they have demonstrated to work. Significantly, they have the ability to deliver results that are unobtainable otherwise. Such software can crunch through very large data sets rapidly, apply purpose-built algorithms and automate the repetitive mechanical steps needed to put decisions into action.vr_Office_of_Finance_13_finance_lacks_advanced_analytics It also ensures consistency and supports objectivity in how executives and managers make decisions. Price and revenue optimization applications have benefited as the cost and complexity of the computing resources needed to use them have declined.

The adoption of PRO software is part of a broader trend of using applications to support fact-based decisions that once depended on experience and hunches. However, our benchmark research on the Office of Finance finds that just 20 percent of companies use price optimization analytics extensively. Only one-third look at product profitability. We think that more of them should do both. Analytic applications can digest a considerable amount of data to segment markets into useful groupings, pinpoint correlations and divine trends, to name a few tasks necessary for pricing management. However, companies investigating PRO software should narrow their search to applications that are appropriate for their specific business. While some offerings have broader applicability than others, no software product now available performs well in every industry.

Retail businesses that have multiple outlets, especially those that deal in trend- or fashion-driven products, face unique price and revenue optimization challenges and this affects the design of pricing management applications aimed at retailers. Many of these businesses are self-service, exclusively so if they are Internet-based, so there is no face-to-face contact during the product selection process. Negotiating prices isn’t feasible in most multiple-outlet retail settings in developed economies because of cultural norms and the hazard of delegating these decisions to front-line staff in even a midsize company. Unlike business-to-business transactions that involve ongoing relationships with established products, most stores today know little about most of their customers, so there is no direct way of judging an individual’s price sensitivity for the specific purchase at hand. In other words, most of the elements that support PRO strategies in analytics used for other types of businesses aren’t available to multiple-outlet retailers.

Since they usually cannot gauge the price sensitivity of their customers, retailers take a different approach: Let the merchandise do the talking. Products that aren’t selling well are by definition overpriced in that market. Retailers have used markdowns as a crude tool of price optimization for a long time. Offering a 30 percent discount near the end of the season is usually better than having to take a 60 percent haircut from a close-out specialist. Yet deciding when and by how much to reduce prices and then implementing the reductions at the store level in an optimal fashion is complicated because of the number of variables that must be considered. There are different types of merchandise, including long-life categories of goods that can be offered for sale for years, short-life fashion and fad items that are offered only once and those somewhere in between. There are differences in demand patterns and price sensitivity between regions and even at the store level. Seasonality, weather and movable holidays such as Easter and Thanksgiving must be considered.

Using analytic applications is superior to relying on experience and intuition because applications often demonstrate that the best decisions go against the grain of established practices. For example, retailers have found that smaller markdowns applied earlier and more frequently produce better results (that is, greater volumes sold at a lower aggregate markdown) than the common practice of making one or two big moves. Until the data became available, minimizing the number of markdowns was reasonable because of the cost in staff time to change prices at the store level. However, retailers using smaller and more frequent markdowns more than pay for these costs and then establish processes to facilitate price changes. Some retailers have found to their surprise that early small markdowns reduce the overall cost of markdowns. Analytic applications also are able to deal with a range of variables that retailers can use in markdown management. For example, they can vary percentages and frequency by size and color as well as by location. The software can monitor sales and inventory levels by the SKU at each store and automatically make detailed recommendations on how to adjust pricing. The software also enables retailers with multichannel operations (usually an online presence) to manage pricing decisions optimally across different types of outlets.

PRO software designed for markdown management also enhances the ability of a multiple-outlet retailers to run their business in a way that maximizes the productivity of their stores measured in sales or gross margin per square foot (or meter) or per linear foot (or meter) of shelf space. Items taking up space in a store or on a shelf have an opportunity cost in that they could be replaced by faster-moving or more profitable goods. Modeling the cost of the uplift required to free up space can result in a more attractive mix of merchandise that will improve returns.

While usability and capability of markdown management software have been improving, retailers face internal challenges in being able to utilize it. Analytic applications are only as good as the data available to feed the systems. Our research consistently finds that data accuracy and availability are significant challenges that almost all midsize and large companies face. Using markdown management software successfully also involves a change management effort requiring heavy involvement by senior management to endorse changes in how the organization handles day-to-day business as well as changes to processes and training and considerable amounts of follow-up to ensure compliance with the new ways of doing business.

Information technology is playing an increasingly important role in how companies conduct their businesses. Analytic applications can transform how entire industries operate. Today, airline and hospitality businesses operate very differently from how they ran in the 1980s because of the Internet and analytics. All sorts of businesses are finding that price and revenue optimization software enables them to improve their results measurably. Retailers should look into markdown management software as a way to fatten their bottom line. Other types of businesses also should consider PRO tools as applied to their particular needs.

Regards,

Robert Kugel – SVP Research

In our benchmark research at least half of participants that use spreadsheets to support a business process routinely say that these tools make it difficult for them to do their job. Yet spreadsheets continue to dominate in a range of business functions and processes. For example, our recent next-generation business planning research finds that this is the most common software used for performing 11 of the most common types of planning. At the heart of the problem is a disconnect between what spreadsheets vr_NGBP_09_spreadsheets_dominant_in_planning_softwarewere originally designed to do and how they are actually used today in corporations. Desktop spreadsheets were intended to be a personal productivity tool used, for example, for prototyping models, creating ad hoc reports and performing one-off analyses using simple models and storing small amounts of data. They were not built for collaborative, repetitive enterprise-wide tasks, and this is the root cause of most of the issues that organizations encounter when they use them in such business processes. Software vendors and IT departments have been trying – mainly in vain – to get users to switch from spreadsheets to a variety of dedicated applications. They’ve failed to make much of a dent because, although these applications have substantial advantages over spreadsheets when used in repetitive collaborative enterprise tasks, these advantages are mainly realized after the model, process or report is put to use in the “production” phase (to borrow an IT term). To date most dedicated applications have been far more difficult than spreadsheets for the average business user to use in the design and test phases. To convince people to switch to their dedicated application, a vendor must offer an alternative that lets users model, create reports, collect data and create dedicated data stores as easily as they can do it in a desktop spreadsheet. Spreadsheets are seductive for most business users because, even with a minimum amount of training and experience, it’s possible to create a useful model, do analysis and create reports. Individuals can immediately translate what they know about their business or how to present their ideas into a form and format that makes sense to them. They can update and modify it whenever they wish, and the change will occur instantly. For these business users ease of use and control trump putting up with the issues that routinely occur when spreadsheets are used in collaborative enterprise processes. Moreover, it’s hard to persuade “spreadsheet jockeys” who have strong command of spreadsheet features and functions that they should start over and learn how to use a new application. Those who have spent their careers working with spreadsheets often find it difficult to work with formal applications because those applications work in ways that aren’t intuitive. Personally these diehards may resist because not having control over analyses and data would diminish their standing in the organization. Nevertheless, there are compelling reasons for vendors to keep trying to devise dedicated software that an average vr_ss21_spreadsheet_maintenance_is_a_burdenbusiness user would find as easy and intuitive as a desktop spreadsheet in the design, test and update phases. Such an application would eliminate the single most important obstacle that keeps organizations from switching. The disadvantages of using spreadsheets are clear and measurable. One of the most significant is that spreadsheets can waste large amounts of time when used inappropriately. After more than a few people become involved and a file is used and reused, issues begin to mount such as errors in data or formulas, broken links and inconsistencies. Changes to even moderately complex models are time-consuming. Soon, much of the time spent with the file is devoted to finding the sources of errors and discrepancies and fixing the mistakes. Our research confirms this. When it comes to important spreadsheets that people use over and over again to collaborate with colleagues, on average people spend about 12 hours per month consolidating, modifying and correcting the spreadsheets. That’s about a day and a half per month – or five to 10 percent of their time – just maintaining these spreadsheets. Business applications vendors started to address business users’ reluctance to use their software more than a decade ago when they began to use Microsoft Excel as the user interface (UI). This provides a familiar environment for those who mainly need to enter data or want to do some “sandbox” modeling and analysis. Since the software behind the UI is a program that uses some sort of database, companies avoid the issues that almost arise when spreadsheets are used in enterprise applications. There also are products that address some of the inherent issues with such as the difficulty of consolidating data from multiple individual spreadsheets as well as keeping data consistent. Visualization software, a relatively new category, greatly simplifies the process of collecting data from one or more enterprise data sources and creating reports and dashboards. As the enterprise software applications business evolves to meet the needs of a new generation of users, as I mentioned recently, it’s imperative that vendors find a way to provide users with software that is a real alternative to desktop spreadsheets. By this I mean enterprise software that provides business users with the same ability to model, create reports and work with data the way they do in a desktop spreadsheet as well as update and modify these by themselves without any IT resources. At the same time, this software has to eliminate all of the problems that are inevitable when spreadsheets are used. Only at that point will a dedicated application become a real alternative to using a spreadsheet for a key business process. Regards, Robert Kugel – SVP Research

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