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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.


Robert Kugel – SVP Research

PROS Holdings, a provider of price and revenue optimization software, has an agreement in principle to acquire Cameleon Software, which offers configure, price and quote (CPQ) applications. The combined company is likely to benefit from a broader geographic presence (PROS is based in Houston while Cameleon is in Toulouse, France) for their sales and marketing efforts. However, the longer-term strategic value of the merger lies in the combination of the related categories of price optimization and CPQ to improve sales effectiveness and financial performance.

Price and revenue optimization, which I have written about before, is a business discipline used to effect demand-based pricing; it applies market segmentation techniques to achieve strategic objectives such as increased profitability, greater market share or both. Software to manage price and revenue optimization first came into wide use in the airline and hospitality industries in the 1980s as a way of maximizing returns from less flexible travelers (such as people on business trips) while minimizing the unsold inventory by selling incremental seats on flights or hotel room nights at discounted prices to more discretionary buyers (typically vacationers). Today, it is a well-established part of any business strategy in the travel industry and is increasingly used in others including retailing (chiefly through mark-down management), financial services and many business-to-business verticals. PROS started in the travel and hospitality industry, which accounted for 44 percent of its 2012 revenues, but its recent growth and focus have been more in manufacturing, distribution and services; those customers accounted for 56 percent of 2012 sales.

For its part, CPQ software emerged to make the process of vr_SPM_01_CPQ_software_is_well_establishedconfiguring complex products more efficient. This issue is of particular importance for industrial companies that sell to other businesses. A Class 8 truck, for example, has multiple options for mechanical parts such as the engine, transmission and braking system, as well as comfort features for the cab such as air conditioning and the radio/audio system. Assembling the various piece-parts of an offering manually, determining that the configuration is a valid one (for instance, whether transmission Y actually works with engine X) and calculating a basic offer price can be time-consuming and error-prone. CPQ software enables those quoting a price to quickly develop even multiple proposals for a prospective buyer. This is a well-established software category. Our benchmark research shows that about half of all companies with 1,000 or more employees use it, another one-third intend to deploy it and only 17 percent have no plans to use it.

Although valuable on its own, when CPQ software is joined to price and revenue optimization in an end-to-end, lead-to-order process, it increases the effectiveness of that process by giving sellers more ways to intelligently manage volumes and margins through altering the cost of individual components. For instance, the base price of a unit may be priced with little or no markup if the goal is to generate margin on the other parts of the sale. (This is similar to many retailers’ strategies except that the price of each piece of the transaction may be negotiated and the prices involved are often considerably greater.) Optimization software can enable sellers to achieve their revenue and margin targets by using purchase behavior patterns to better assess the buyer’s price elasticity. Indeed, the choice of certain components themselves may provide sellers with clues about the buyer’s overall price sensitivity: For instance, those wanting certain features, brands or grades may be less inclined to negotiate and therefore should be quoted a higher price. (Similarly, certain online merchants have been found to charge buyers using Apple products more than others.) Thus when price optimization is part of the business logic in using CPQ software, it makes the software more helpful to the user.

Viewed from the other side of the combination, adding a native CPQ capability to price and revenue optimization software makes the analytics far more actionable because it can support an end-to-end process. Although PROS has had CPQ capabilities in its Quote2Win application, they are not as robust as what’s available in Cameleon, which provides configuration capabilities and guided selling. PROS has published APIs to facilitate integration with CPQ systems, but integration out of the box with a full-featured application is certainly better. One of the biggest barriers to more widespread adoption of price and revenue optimization is that products don’t always enable user organizations to easily embed the analytics and data that drive optimization directly into the sales process.

Businesses that first adopted price optimization (and which have the deepest penetration) include travel, hospitality and retail mark-down management. Their common characteristic is that all are (or started out as) relatively simple products (say, a round-trip seat or a dress) for which prices are set, not negotiated. Business-to-business (B2B) transactions, however, often are more complex because the product often is a bundle of physical goods, services, warranties and ancillary provisions such as delivery. Moreover, typically these transactions involve some negotiation allow the sales representative a degree of freedom in setting prices and discounts. Having the actual price being quoted is critical for to capture and use in the sales process as our research in sales forecasting found that pricing data is one of the top components in 48 percent of organizations but so is the configuration of products to 22% percent of organizations and want it to be included in the sales forecast. Because the process is more complicated, prospective users of price optimization may find it daunting to adopt the strategy. In theory at least, adding a robust CPQ capability should make it easier for a company to implement a successful price and revenue optimization strategy in a reasonable period of time.

Decades of experience have demonstrated the value of this software category. Without the benefit of price optimization applications, it is almost impossible to assess a customer’s demand elasticity to determine an optimal offer price. Margin may be lost unnecessarily when sales people default to discounting to ensure a sale. Simple up-sell and cross-sell strategies can be beneficial, but they can fall short of what’s optimal and – increasingly – what’s possible. Having software to better gauge price sensitivity and control more elements of a negotiation with greater visibility into its profitability can help companies achieve an optimal balance of revenue and margin. The process can be even more effective when it’s coupled with sales incentive management software. All of which points to improving the sales process and our latest research in sales found that inconsistent execution is the largest impediment in 53 percent of organizations that is motivating management to invest into sales technology like CPQ and pricing optimization.

Organizational issues also have inhibited adoption of price and revenue optimization strategies in industrial companies as well as the use of this category of software. Responsibility for managing profits usually involves both the finance and sales organizations. Both have roles in handling profitability, but the process is typically simplistic (using up-sell and cross-sell strategies with little regard to the profitability of the components), imperfectly coordinated between Sales and Finance and almost never optimized. Ideally, CEOs and COOs should be initiating an optimization effort, but I find this is rarely the case. Using analytics to manage pricing and support a sophisticated strategy is an important business innovation that industrial and other business-to-business verticals should embrace. Finance organizations – specifically the financial planning and analysis (FP&A) group – should take the lead, especially if they want to demonstrate the ability of Finance to deliver more strategic value to the company. Successful price and revenue optimization strategies can provide a sustainable competitive advantage. Companies of course need a pricing strategy; understanding the benefits of price optimization software can help them see what’s possible and develop an implementation plan.


Robert Kugel – SVP Research

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