Advancing the Art of Pricing with Science


Originally Posted - May 23, 2007

Though companies recognize the need for a better way to manage their pricing strategies, many continue to lose money by using archaic pricing methods. But there is a new approach beginning to surface in the market of price management. Science-based software can be leveraged to help companies create more accurate and complete pricing strategies in order to meet their margins. To learn more, please see Know Thy Market Segment's Price Response.

In 2005, Zilliant, an Austin, Texas (US)-based provider of data-driven, strategic pricing applications, and the Institute for International Research (IIR) released the results of a survey that showed strategic pricing was gaining in priority among some US businesses. The PriceX Conference poll surveyed nearly seventy businesspeople responsible for making pricing decisions at their respective companies.

Despite this finding, adoption of strategic, science-based pricing and associated technologies is relatively minor in industries other than airlines, hotels, and retailers. These industries practice a form of science-based pricing called yield management. Yield management, also known as revenue management, was invented three decades ago; its goal is to fill as many seats and rooms as possible while charging the highest prices the market can bear. Since then, these industries have adopted sophisticated software programs to predict demand and to set prices, resulting in as many different price points per flight as passengers, or per room as guests.

Armed with a wealth of customer data, programmers then developed formulas that could manipulate prices up or down depending on existing sales, the likelihood of last-minute purchases, and other variables ranging from weather forecasts to competitors' prices. The underlying logic was that airplane seats and hotel rooms are worthless if unused, and selling them even at a loss meant gaining some revenue.

Given that "computer power" is much more affordable these days, user enterprises can harness statistical science to analyze transactions and other customer data to more accurately explore the cause-and-effect relationship between prices and purchase decisions. The idea here is to be able to discern customers' "willingness to pay," and set "take-it-or-leave it" prices where companies will make the maximum revenue.

Using mathematical formulas and massive databases of sales records, companies can forecast their sales plans, and test pricing and demand elasticity under various discount or package scenarios before trying them in the market. Layering in data from other customer interactions can help companies set prices, schedule markdowns, and identify top performing buyers with more sophistication than ever before. Companies can also set prices based on the value consumers derive from specific products, or even plan different discounting and pricing strategies based on anticipated customer behavior.

Again, as indicated earlier on (see Know Thy Market Segment's Price Response), business-to-business (B2B) pricing environments are different in that pricing is opaque and largely discretionary. Now that technologies have been brought to market that address these dynamics, B2B companies are getting on board too.

Nevertheless, according to Zilliant, although pricing is generally accepted as a core business practice, the process most B2B companies go through in determining a price is often archaic and arbitrary. Some businesses simply take the cost of a product and add margin on top of that price, while others simply match or better their competitor's offering. Another common practice is the so-called "out of thin air" (OTA) or "sucking (knowledge) out of my thumb" method; in other words—guessing. According to the above mentioned PriceX survey, 56 percent of companies polled have some sort of pricing strategy in place, while only 44 percent have a dedicated pricing department or an individual with pricing responsibility.

Other key trends uncovered in the survey included that 35 percent of companies consider pricing to be a top priority, yet 61 percent of companies use Excel spreadsheets to determine price (rather than specialized pricing applications from a vendor). Data cleansing was cited as the main obstacle to improving pricing policies, followed by ineffective customer segmentation.

In a somewhat older survey (taken a few years ago) by the Professional Pricing Society of its members, 30 percent of respondents said they priced new products by mirroring their nearest competitors' prices, and another 22 percent set prices for new products based on recovery of costs and to tack on a profit. Only 18 percent revealed that they performed some sort of customer research to determine the value of the product or service to potential customers. And when it comes to the Internet pricing, 40 percent said they simply mimic the pricing of their off-line sales channels, and 28 percent responded that they do not have an Internet strategy at all.

In other words, most businesses lack a detailed understanding of their market segments' responses to prices and deal terms. They rely solely on undifferentiated discount policies and sales team discretion to structure all types of deals, from quotes to orders, agreements to contracts. As a result, some deals go through with overly generous terms, while others are lost due to gross pricing misalignment.

These harmful practices continue to take place despite some pricing pundits "shouting blue murder" (protesting) about the ingrained, casual thinking that pervades the global economy regarding pricing. Both consumers and businesspeople erroneously assume that price has everything to do with cost. Yet, while any company has to know the cost of a product, it is only so that it can understand the profitability implications of the price, not for the purpose of setting the price. The value (benefit per unit price) is in the eye of the customer and depends on the circumstances surrounding the deal. Another faulty practice is the assumption that when a company is in a competitive situation and prices drop, the company must match the price-drop. Also, executives who are devoted to using data and analytics in all kinds of other functional areas still think it is entirely acceptable to set prices based on "history," "experience," or "instinct."

Gauging Market Response to Price

One of the secrets to excellence in pricing is in obtaining accurate measures or estimates of what effects pricing outcomes, and to what extent these "deal circumstances" allow for price differentiation. One well-understood method in business-to-consumer (B2C) industries is to survey customers and ask them to rank alternative offers, including alternatives of different prices. While this type of investigation (conjoint analysis) has been used with success in marketing research, it is expensive and time-consuming to design and undertake surveys. Also, because of the time and expense, results from such analyses may become out-of-date very quickly.

A variation of this approach that may be of some use in B2B markets is to survey experienced sales people and probe their judgment about customer response to alternative prices. This approach is severely limited by the knowledge that the salespeople have and by the subjective biases reflected in their perceptions.

Another method to obtaining accurate measures of market price response is the controlled experiment. In controlled experiments, price offers are varied systematically, and outcomes are recorded. The process of executing such experiments is referred to as price testing. While not as widely used, this method has provided a means to directly measure price responsiveness in some B2C, e-commerce applications. Another case in which price testing might be an effective way to obtain measures of price responsiveness occurs in businesses that have relatively static prices. For example, in industrial markets, catalogues and price lists are updated semiannually. In these markets, the natural data may exhibit variations in purchase behavior, but if there is little or no variation in the offered prices, it is impossible to infer any relationship between price and purchase behavior.

Another case arises in businesses that may have widely varying prices, such as wholesale distributors and manufacturers whose sales representatives have latitude to negotiate prices with their customers. These are situations in which statistical methods are believed to generally work well. The method uses statistical techniques (generally regression models) to analyze actual sales or offer transactions to obtain estimates of price response across segments, and price elasticity within them. These methods are widely employed and, if well designed and executed, can be successful at extracting measures of price responsiveness from other factors that influence customers' purchase behaviors.

One may refer to the use of these methods as relying on "natural" data. That is, empirical data that is pertinent to the normal business processes of marketing, pricing, and sales. These methods are especially appealing because they make use of existing data, and therefore can be undertaken relatively quickly. However, the difficulty that arises here is what statisticians refer to as endogeneity, which describes the condition in which the values of the predictor—price, in this case—are not independent of the process that one wishes to measure. Namely, sales representatives who decide what price to offer to their customers may often have some idea (wrong or not) as to how the customer is likely to respond. Salespeople will have the tendency to offer higher prices to the customers most likely to purchase, and lower prices to customers less likely to purchase.

Also known by the terms price management, price optimization, or revenue management, this emerging discipline has recently found a loyal following among consumer goods retailers that have tapped into large repositories of point-of-sale (POS) data to refine their pricing models (see Point of Sale: To Stand Alone or Not?). For more information on the scope of retail management systems, see Retail Systems: A Primer and Retail Market Dynamics for Software Vendors.

As detailed in The Case for Pricing Management, pricing is a complex process in general. This is particularly true in retail, where a thorough understanding of the numerous interdependent variables that drive demand, such as seasonality, price elasticity, cross-elasticity between items, and inventory presentation, are critical to making profitable pricing decisions.

The Pricing State of Affairs of B2B Manufacturers and Distributors

The focus here is to discern the degree to which this science-based approach to measuring price response, and aligning prices to market based on this insight, is spreading into B2B product and service industries. Companies are seeking the perfect price to maximize unit sales, price-per-sale, and ultimately, profits. Of all B2B verticals, manufacturers and wholesale distributors are the two that have warmed up the most to the idea of data-driven pricing management.

On the one hand, the world of a B2B manufacturer is dynamic and complex, since a proliferation of customer relationships, products, promotions, and channels exacerbates the inherent complexity of pricing processes and data. Unable to fully address this complexity with generic policies, strategic pricing decisions are largely discretionary, which leads to oversized discounts and unprofitable, off-invoice terms.

Likewise, massive product portfolios and transaction volumes also complicate distributors' efforts to identify and cultivate profitable relationships and deals. Manufacturing and distribution companies participate in complex markets, managing dozens of thousands of products and customers, and often millions of transactions each year. Each single transaction produces a tremendous volume of customer data. As customers continue to buy in smarter ways (even by using procurement software to cherry-pick and drive their buying decisions), uneducated "meet competition" discounts, rebates, and other concessions erode already thin margins. The "pedestrian" (standard) tools of price lists, e-mails, and spreadsheets are too inadequate to hold up during negotiations for distributors' sellers.

Last but not least, pricing industrial services is also complex because it must factor in asset availability, use, and customized offerings. Most service providers lack the tools needed to fully incorporate these variables into their pricing strategies, and consequently cannot capture the full value of their services.

These diversified B2B environments typically have extensive product portfolios and sizable customer bases. After taking into account all price-related variables (for example, costs, contracts, discounts, volume agreements, customizations, shipping, etc.), the total number of unique prices in the market at any one time can easily exceed 100,000. With so many products, exceptions, and changes over time, it is no wonder that such companies' price points and margins vary widely across their businesses. In fact, some B2B manufacturers and distributors struggle by merely calculating prices that are "correct" (that is, prices that are in accordance with their numerous price lists, policies, and contracts), let alone differentiating prices to maximize margins and overall profits.

Aggravating the situation is the fact that even after such a B2B setup aggregates all available data from disparate transactional systems, quantity and quality can still be a challenge. Many products are "slow moving," producing sparse transactional history, and most companies do not track quotes that do not become orders, albeit this loss data (quoted prices that were rejected) is valuable for determining price sensitivity. Most B2B companies do not have this data because price quotes are revised in the same document or customer relationship management (CRM) opportunity, overwriting previous offers.

Since an academic approach to optimization called market response modeling (MRM) requires data on losses and wins, some pricing optimization vendors had to come up with a proprietary way to produce recommendations based on "win-only" data. MRM is an approach that uses modeling techniques to predict market or segment trends, or reactions to pricing movements.

On a positive side, B2B manufacturers and distributors' broad product lines, numerous customers, and discretionary, negotiated sales models combine to create business environments that are conducive to science-guided approaches to differentiate pricing. Again, pricing science is a combination of statistical and algorithmic methods that synthesize price recommendations from historical pricing and marketing data. Data-driven customer segmentation and optimization models can recommend prices that are much more profitable than those currently in market. This pricing science can be applied through analytical and execution applications, enabling smarter decisions and better execution across all business functions related to pricing. The scientific foundation of price differentiation is the segmenting of customers and deals by price sensitivity, and using each price segment's unique sensitivity to set prices on future deals.

This is the part two of the series Know Thy Market Segment's Price Response. In the next part of this series, the process and steps involved in price segmentation will be outlined and reviewed in detail.

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