Our analysis from early 2006 brings us to the conclusion that almost all companies need to approach the management and optimization of their offerings' (products or services) selling prices, discounting, and potential price increases with the same firmness they use to manage all manufacturing and procurement related costs.
Indeed, most companies have thus far done almost everything in their power to cut costs--from outsourcing information technology (IT) departments, indirect material cutbacks, streamlining, restructuring, and layoffs, to limiting employee travel and whatnot. But there has long been another side to the profitability equation that often goes unexplored: pricing the last citadel of hunch, instinct, or guesswork in businesses. For the record, price is the collection of monetary and business terms (including applied discounts and rebates) that are assigned to the acquisition of a good or service. In its broadest definition, price includes much more than the "list price" of an offering. Price is the monetary measure of the value assigned by a customer to a good or service.
Advanced analytics and sophisticated systems have long been used to manage inventory, control the cost of goods sold, and manage the supply chain in terms of costs and delivery. But the irony is that none of these factors is as powerful a lever for profitability as pricing. Indeed, recent research and surveys show that when companies make pricing a priority and implement solutions from a specialized pricing formula, these vendors can see a profit improvement, sometimes as high as 20 percent.
Further, many companies make substantial investments in three of the four classic "marketing Ps"--product, place (direct sales and fulfillment channels), and promotion. As for the fourth "P"--price--most companies have not yet moved beyond pesky spreadsheets or a few hours of a consultants time to ensure that their pricing strategies give them the best chances for success. This is again despite the indications that many organizations that have automated their pricing strategies and operations as an antidote to the automated procurement and strategic sourcing (that have in turn helped many businesses cut costs on the buying side) have, as a result, reportedly experienced significant gains in both margins and profitability. Margin is a generic term most typically associated with profits. Common financial measures include gross margin, contribution margin, and net margin. Each reflects profits after certain costs are subtracted. Profit, on the other hand, refers to financial gain or revenues minus expenses.
Moreover, the potential benefits of improved pricing can flow through an entire organization, since more predictable and effective pricing policies can help manage sales force compensation, promotional expenditures, incentive programs, cost allocations, and operational planning. This is because smart pricing can do much more for a company than simply allow it to increase margins and grow revenue. Smart pricing processes and approaches can help companies gain market share, apply pressure to competitors, improve the use of production capacity, or reduce the risk associated with new product launches.
Quantitative, systematic, optimized pricing can then mean survival or not, as depicted in the well-known (by now almost classic) McKinsey & Co. report from 2003 titled The Power of Pricing. A price rise of 1 percent, at constant volumes of sale and costs, should generate an 8 percent increase in operating profits, which is 50 percent greater than the impact of a decrease of 1 percent in variable costs (that is, materials and direct labor), and more than 3 times greater than the impact of a 1 percent increase in sales volume (even if one forgets that increased production typically increases costs).
Thus, while there are many determinants of a company's success, no variable can influence margins as much as pricing. In other words, poorly constructed pricing policies can be just as detrimental to a company as optimized pricing can be beneficial.
In spite of the above findings and increasing market awareness, one can still sense a chasm between the markets realization of the potential pricing benefits and its corresponding (expected) moves. Specifically, many companies realize that having non-profitable products or customers results in margins quietly and unnoticeably leaking (and money being left on the table). Related to this is the notion of the pocket price waterfall, which displays how much actual revenue enterprises really keep in their pockets from each of their transactions with customers. These "pocket" prices help companies diagnose and capture missed pricing opportunities.To be clear, a pocket price is a financial description of the price paid after direct selling costs have been subtracted, which is the money the company "puts in its pocket". Further, a pocket margin is a financial description of the margin that gets "put into the company's pocket" after all costs are allocated (indirect overhead and indirect costs). Price waterfalls are analytic reports that measure the erosion of list prices and compare them to the actual pocketed price. Price waterfalls do so by taking into account several factors. Such factors include negotiated (if not irresponsibly generous) discounting (a component of a price that represents a deduction from a baseline or "list"); rebates and promotions; consignment costs; cooperative advertising; chargebacks; payment terms and cash discounts; online order discounts; performance penalties; receivables carrying costs; slotting allowances; stocking allowances; freight charges; volume incentives; and so forth.
Each of the above factors places a unique "fingerprint" on each and every order and deal, and yet, these factors and fingerprints remain largely invisible throughout the enterprise. This is to say that managers who watch over pricing often focus on invoice prices that are readily available. Unfortunately however, revenue leaks are not detailed on invoices, and are therefore not easily spotted. Revenue leaks (or price waterfalls) can include cash discounts for prompt payments; late payment and extended terms costs; cooperative advertising allowances; volume-based rebates; promotional programs (a form of discounting that has clear guidelines and time scales to encourage very specific buying behavior); freight expenses; special handling; and so on.
Since commoditization, price transparency, price wars, and price erosion are all seemingly here to stay, there is thus an increasing urge to transform the crude, self-destructive, reactive, and other "dark art" pricing strategies of yesteryears that are still largely practiced today. Such archaic methods have companies relying on anecdotes from the field, applying a "cost plus" pricing approach, watching and matching competitors prices, etc. to form their pricing strategies.
Companies see the need to turn their pricing strategies into a more exact science by using complex algorithms to analyze available historical transaction and market data. This raw data can be harvested mostly from existing corporate databases, such as enterprise resource planning (ERP), supply chain management (SCM), or customer relationship management (CRM) systems to synthesize a detailed analysis of the profitability of every level of business, all the way down to each individual transaction. Managers or pricing analysts can then study the results and figure out how to adjust their price operations accordingly in a more educated, data-driven manner. The idea here is not to customarily "guestimate" (make a somewhat informed decision) what is going to happen. Rather, it is to change prices in a more controlled (even if experimental) way, watch what happens, and then set prices for real after that (with the next set of tests and observations taking place soon after).
For instance, astute software captures real-time and historic purchase data, and organizes it into analytical models to determine optimal price and deal structure. This is made possible by taking into consideration such variables as the customer's buying power and geographic region; the relative value and cost of the supplier's goods and services; the competitive dynamics; and how frequently the customer makes a buy. This marriage of statistical science and analysis empirically answers the proverbial question of what the market will bear for "this much, at this time, for this thing." This determination is made at a very precise level, benchmarking pricing decisions against the subset of transactions that are similar in terms of price response, and sets the stage for price optimization and negotiation guidance.
The "one-size-fits-all list" price, coupled with the "let the sales guy negotiate the best deal he can get" pricing method, and further helped by a mega Microsoft Excel spreadsheet full of unexplainable exceptions and variations, is slowly being replaced by this data-driven approach. Also, given the growing awareness that a single item can have different prices for different customers and segments, a solid price management solution must take each individual customer into account and sense, set, and enforce the price according to that segment. For some enterprises, pricing science, a combination of statistical and algorithmic methods that synthesize price recommendations from historical pricing and marketing data, could be one (if not the only) way to find coveted profit margins.
Enterprises are increasingly realizing the need for holistic, data-driven pricing management, which in many instances starts with the application of pricing science to determine how price response varies across customers, products, and orders. Price response refers to the net prices achieved in the market correlated to customer, product, and order variables that influence the price outcomes.
In general, demand elasticity (or consumers' price sensitivity) is responsiveness of the quantity purchased of an item to changes in the item's price. If the quantity purchased changes proportionately more than the price, the demand is elastic. Conversely, if the quantity purchased changes proportionately less than the price, the demand is inelastic. Price sensitivity is the specific elasticity measurement as it relates to a customer's response to price or discount movements. For example, high price sensitivity would reflect substantial changes in behavior from a small pricing movement.
One must also remember that a truly strong price optimization system is not merely a "price-raising" system. There may be as many opportunities to reduce the price on a given item, or increase item turnover, ultimately producing more profit dollars than if a price were to be increased beyond the consumer sensitivity level.
However, the terms customer price sensitivity and elasticity often carry with them the negative connotations associated with "exploiting willingness to pay." While this may be an accurate description of business-to-consumer (B2C) pricing dynamics, willingness to pay and sensitivity are not major factors in business-to-business (B2B) pricing. Conversely, B2B market prices reflect a range of qualitative and quantitative factors: product-service differentiation and associated value, competition, relationship, service, supply and demand, and variable costs, to name the most significant.
The terminology thus used in B2B environments to describe the aggregated effect is market price response. Once the enterprise has determined price response, it can employ price segmentation to quantify how response varies across the market based on the customer, product, and deal circumstances associated with each transaction. Once the enterprise has determined which circumstance, or deal attributes, affect price outcomes in a customer's market, the company can use price optimization to help it align prices within each segment, and to differentiate prices across the segments, which improves consistency and profits.
Once a company has determined how price response varies across its markets, it can then discover, analyze, and remove margin leakages. Once this is done, companies are then able to enforce and manage pricing policies (including discretionary negotiation guidance) to become more proficient with quoting, contracts, and negotiations. Comprehensive insight into pricing performance should be a powerful tool for improving profitability, starting with sales representatives who, when armed with scorecards showing market pricing conditions and recent peer group quotes, can negotiate deal terms with greater confidence.
The idea here is to counteract the all-too-common faulty selling practice of lowering prices in order to maximize the odds of winning. Fear of losing the sale on price inevitably biases a majority of uneducated pricing outcomes lower than the circumstances actually warrant. Whether negotiating a deal discount or setting product line prices, the impulse to "do whatever it takes to get the business" often results in suboptimal pricing and margins. Many benchmarks have supplied empirical evidence of the pricing that is really necessary to win under a given set of circumstances, which in most cases is higher than assumed.
Even if a salesperson is willing to compete aggressively, it is difficult to do so without a sound analytical support. A well-known anecdote illustrates that every salesperson remembers the details of the last deal only, which is usually completely inappropriate for a new sales opportunity. The distribution of price outcomes for each price segment should therefore reveal where prices were set lower than was likely needed to win the deal. This analysis, based on looking backward, should not only highlight grossly unprofitable outliers that are well below the price segment median, but it should also identify the much more common case in which prices and margins could have been slightly higher.
It is typical to find that in total, these underpriced transactions have reduced realizable margins by 10 to 20 percent, or more. Information is likely the most powerful negotiating tool available, since giving salespeople contextual price recommendations (with reasonable space to maneuver) based on quantitative information about what similar customers paid under similar circumstances should immediately improve results. Further, pricing analysts can spotlight outlier transactions and reap immediate benefits by enhancing the margin characteristics of these "low-hanging fruit" (most obvious pricing opportunities), whereas executives can more quickly review the profitability of their business units and take action where needed.
Last but not least, when management deems that an order deserves an exception to standard discounting policy, the ability to evaluate different value-added scenarios (those activities or steps that add to or change a product or service as it goes through a process; the ones customers view as important and necessary) based on their relative profitability ensures "must win" deals and helps limit the overall financial impact. The main point of price segmentation is to recapture those previously wasted profits going forward, and this is where benchmarking each new price decision against its respective price segment peer group should truly pay off. A pricing segment is a group of transactions that display similar circumstances and behavior related to pricing, discounting, and promotions. This capability allows companies to segment and optimize their prices and promotion offers at a more granular level, thereby improving alignment with each segments respective price sensitivity.
With a clearer picture of what pricing is achievable across the market, decision makers should have the prescriptive information they need to set prices as high as possible without putting business at risk. This understanding can then be used to eliminate unprofitable pricing variation within price segments by increasing and tightening the distribution of price outcomes.
In addition to better market information and price recommendations, well-devised incentives also have powerful effects on a sales representative's discipline and confidence. Incentive compensation plans are designed to motivate sales and service professionals to achieve goals and strive for excellence. But, an alarming fact is that these same compensation plans are often at odds with the corporate strategy of customer satisfaction. This is because sales employees, in their zeal for earning more, often lose sight of what is important--their customers' needs and their companies' strategies.
If, for example, a company wants to increase sales of a new product line, but the direct sales and indirect channel still receive hefty incentives that favor existing product lines, the sales folks will logically not care to pursue sales for the new (but unrewarding) product line. Also, what if a manufacturing company's salespeople are paid on the volume of purchase orders, and continue to sell under heavy discounts or by overpromising nonexistent features to customers? The company's profits will likely dwindle quickly as a result. There have been many examples of companies paying immense sales commissions to their sales forces (who, to be fair, have all reached their quotas, albeit inadvertently set wrong by their superiors), even as the companies suffer terrible losses, possibly at the risk of going out of business.
Therefore, some companies have been using monitoring capabilities to discover and address issues with sales force performance. Using information gathered via the monitoring processes, a company can then use analytics to study order win rates and discounting across sales representatives and field offices. Many companies reward sales forces on the basis of revenue booked, but some pricing solutions also provide insight into win rates and the "cost" (discounts offered) to achieve that win rate.
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