Price and revenue optimization (PRO) is a business discipline used to produce demand-based pricing; it applies market segmentation techniques to achieve strategic objectives such as increased profitability or greater market share. In essence, PRO enables companies to surf the demand curve using dynamic rather than fixed pricing to achieve the most desirable trade-offs between revenue volume and profit margins. The trade-off is defined by strategic factors such as the company’s market position, product and service portfolio, and marketing strategy.
PRO is an important capability for sellers in markets where there is significant price transparency. It enables them to set prices dynamically, according to individual buyers’ preferences, price sensitivity and profitability. Yet comparatively few companies employ PRO: Our Office of Finance benchmark research reveals that only 15 percent of companies use this approach.
Today, however, the use of software that can optimize pricing to achieve revenue and margin objectives in interbusiness transactions – what we refer to as business-to-business price and revenue optimization (B2B PRO) – is entering the mainstream. Although the concept and mathematics of price and revenue optimization are well established, until recently companies have found it difficult to put it into practice in B2B commerce (more on this later). First, some background.
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 of setting a price that is determined by the seller’s assessment of what the buyer is willing to pay, rather than based on some fixed markup over cost or another mathematical construction. This assessment of what an individual buyer is willing to pay is based on an estimate of a good’s or a service’s perceived value to that buyer and an estimation of the buyer’s price sensitivity. Companies use demand-based pricing to optimize – rather than simply maximize – their pricing to achieve revenue and profitability objectives.
Demand-based pricing 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; it is a form of haggling. However, software makes demand-based pricing practical in large businesses by automating it and facilitates its introduction in societies used to set pricing.
Computer-supported PRO began in earnest in the 1980s as the airline and hospitality industries adopted revenue management practices in an effort 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). The main feature of this class of goods is that they are perishable: When morning comes or the cabin door is closed, an empty bed or seat is a lost revenue opportunity. It also is constrained by capacity: There are only a fixed number of seats or beds available on a given day.
Price and revenue optimization algorithms are designed to enable a company to fatten profit margins at a given level of revenues than is possible using 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. Financial services is another industry that uses PRO, mainly for retail banking and credit but also for commercial services such as credit card payments. Elsewhere retailers have used markdown management software to optimize pricing, especially for fashion-driven or seasonal items.
The application of B2B PRO is fundamentally different from the business-to-consumer (B2C) kind in at least three respects. One is that B2B usually involves negotiated pricing between potential customers and sales representatives who have some degree of pricing discretion. That is, they can offer a discount to the list price or include products or services of value (such as shipping or a longer warranty) at the stated price. Another is that, in comparison to goods and services offered in a B2C model, B2B volumes usually are more limited, especially for industrial equipment and supplies. So companies have less data on which to model buyer behavior, limiting their ability to segment buyers and tune their measurements of price sensitivity. A third characteristic is that the products involved in B2B commerce often are complex; that is, they provide buyers with multiple options for multiple components. This can range from highly configured products such as Class 8 trucks, where the purchase involves specifying a particular engine, transmission, axle configuration, body and so on, to simpler examples such as a single item with different shipping options.
That noted, there are similarities between B2C and B2B buying behaviors. One is that buyers tend to be more sensitive to pricing of frequently purchased items than those purchased infrequently. Thus, for a given customer or customer profile, it is advisable to hold prices down on items these customers might purchase frequently or for specific components of a configured product where price comparisons are readily available.
B2B PRO is only now entering mainstream adoption decades after price and revenue optimization (in the form of “revenue management”) was first used successfully by the travel and hospitality industries and as retailers’ utilization of markdown management software (a form of PRO) has become commonplace. There are three main groups of reasons why.
The first reasons involve people. Profitability management initiatives almost always involve major changes in the way a company operates, affecting multiple departments. Often, pricing authority is widely dispersed across an enterprise. Sales, marketing, finance and operations (manufacturing or professional services, for example) need to work together to arrive at a workable, consistent strategy and a process for implementing it, even if their individual objectives may not be perfectly aligned. Making algorithms work is relatively easy; getting people to work together isn’t. Gaining sustained cooperation to achieve a common objective requires leadership. Robert Crandall, the CEO of American Airlines in the 1980s, was instrumental in driving the implementation of its revenue management strategy that borrowed heavily from a similar approach conceived earlier by another airline. It helped that at the time large established carriers like American faced an existential threat from budget startup airlines.
Another people issue reflects the learning curve required to successfully adopt a new technology and integrate it into a business process. For the airlines, carrier reservation systems already were in universal use, and people were trained to use them. Adding new fare codes and adjusting prices dynamically was a technical, not a behavioral challenge. However, training a salesforce to apply PRO in its sales process takes time and can face resistance from employees who had a vested interest in the status quo or who thought they had lost business using the new technique. Moreover, change management is made easier when early adopters are either numerous or successful enough to make a compelling case to other companies. Compelling references have taken longer to emerge in B2B PRO. Because of American Airlines’ success and its high profile, it was soon copied by other airlines and large hotel chains.
The second type of reason has to do with technology and process. Part of the reason why it has taken time to build up user references is that the software has had to mature enough for mainstream users. Initially, most B2B PRO software required a large amount of implementation assistance, either to integrate components (such as configure, price and quote) necessary to manage the pricing process or to create workable buyer segmentations.
The third sort of issue B2B PRO implementations have faced is data: Successfully configuring buyer segments requires clean, complete data sets. Companies collect historical data, including product volumes, the company’s list and realized prices, promotions, competitors’ prices and promotions, economic conditions, product availability, seasonal conditions and fixed and variable cost details. A clean data set is rarely available from the start because the information is duplicate, incorrect or missing. Cleansing data and changing procedures to reliably gather missing data going forward are time-consuming and come at some expense.
Many larger companies engaged in business-to-business selling can benefit from implementing price and revenue optimization. B2B markets are likely to increasingly resemble B2C markets. Purchasing departments will employ technology to automate routine functions including price discovery and managing requests for proposals (RFPs). Blockchain distributed ledgers, which I have written about, will simplify the capture and collection of pricing and transaction data. Consequently, price transparency is likely to increase in B2B markets, creating a premium for strategies and techniques that eke out even modest increases in profit margins. I recommend that companies that operate in B2B markets evaluate PRO because using it can be the source of a sustainable competitive advantage.
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