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Pricing and profit margins appear to be trending topics, which is normal at this stage of the business cycle. North American companies achieved high levels of profitability coming out of the last recession by staying lean, but this trend has run its course. Margins are being squeezed, and companies are looking for ways to add to the bottom line.

In my terminology, “managing profitability,” which is what most companies do, is not the same as “profitability management.” The latter will give a corporation a long-term advantage over competitors that simply manage profitability because it almost always provides a better balance of the trade-offs between revenue and market share objectives and profit goals within other essential business constraints (such as operating in a legal, ethical and sustainable fashion).

Managing profitability is a workable but simplistic approach that seeks to maximize revenues and minimize costs. The practice is attractive because it’s straightforward, but by itself, maximizing revenue is rarely optimal in today’s complex business environment. Companies usually do not explicitly consider margin in establishing sales targets. Neither do they always measure costs properly, in a way to make the best economic decisions in setting revenue goals and sales quotas. Maximizing revenues is the wrong approach if the mix of sales is more heavily weighted to low-margin products or if the products consume a disproportionate share of a scarce resource, such as time used on an expensive machine tool.

Profitability management has been possible but until recently not really practical, which explains why it is not more widely practiced in most types of business. Software that facilitates the analyses needed to perform profitability management has been available but not widely adopted, in part because it has not been easy enough to use in day-to-day operations. And although profitability management is the norm in some businesses, such as airlines and hotels, it is still a novel approach in most others. Consequently, so far there haven’t been enough success stories to spur senior executives to change.

Price optimization, which I covered in an earlier research perspective, can be described as surfing the demand curve. Rather than setting a single price, companies use segmentation techniques to assess buyers’ price elasticity to set the highest price that has the greatest likelihood of completing a sale. Thus, a company trying to maximize market share will be willing to accept lower prices from any type of buyer, while those companies focusing on profitability will be more discriminating to achieve a higher average price. Such optimization usually requires analytic software to handle the mass of data necessary to identify relevant and valid buyer segments and calculate their elasticity on an ongoing, dynamic basis. The rise in retailing on the Internet is enabling greater use of price optimization, since sellers can easily present different prices to potential buyers using a variety of techniques.

Price optimization alone works best in situations where the marginal cost of sales is essentially the same for each unit. This is the case in travel, hospitality and most other finite inventory product categories. (“Finite” is a term of art used to distinguish time- or date-specific goods such as airplane reservations or hotel beds that disappear if not used, or fashion or other goods that have limited production or quickly become obsolete.) This type of optimization is also used in financial services, where the cost of funds for a given asset class will be identical.

One specialized form of price optimization is managing discounts, which is used by bricks-and-mortar retailers because they are able to display only a single price to all prospective customers. Since they have no way of knowing or testing the elasticity of demand of potential buyers, they use software to carefully monitor sales data and inventories to manage price markdowns. This approach is especially useful for seasonal items or fashion because such goods become obsolete in a relatively short period of time. Segmentation is achieved mainly on how much the purchaser values novelty, selection, immediate gratification or convenience.

The difference between price optimization and revenue optimization (as I use the terms) is that revenue optimization explicitly considers the profitability of sales in making trade-offs to determine how much to sell at which price. For companies that offer goods and services with different degrees of profitability, revenue optimization usually works better than price optimization alone. Indeed, revenue optimization can improve bottom lines even without adopting price optimization.

Accurate optimization of revenue requires an accurate measurement of costs. Too often, companies do not gauge product margins accurately. They may use static assumptions about margins or employ standard cost accounting methods, which often do not measure the true economic costs. Several costing methodologies have emerged over the past half century that are designed to give companies better ways to measure costs, including activity-based costingmarginal planned cost accountingresource consumption accounting and lean accounting, to name four of the most common. An alternative approach that can be used with any costing method is a time-based optimization technique (although the more accurate the costing method employed, the better the results will be). A time-based approach is especially useful for any asset-intensive business.

Most businesses sell multiple products and/or services, and in most cases each of these has different degrees of profitability. Since organizations have finite resources, they need to allocate them to generate the optimal profit margins given market demand and other factors. Here again, optimal is not necessarily the maximum, because pricing and production decisions are usually constrained by market demand (not everyone wants a premium widget) or by strategy (such as an objective of increasing market share in some low-margin segment or using discounts to undermine the profitability of a competitor’s key product). “Optimal” is a temporary condition that changes according to demand, costs and market conditions, to name three key factors. Thus, because of the complexity of dealing with large, complex and changing data sets, a dedicated software application almost always provides a corporation with a greater ability to frequently recalculate optimal solutions and analyze their impact, compared with manual or desktop spreadsheet-based systems.

Profitability management is still in its infancy in many businesses. It is a departure from tried-and-true approaches, requiring a change management effort applied across the organization. It therefore requires focus from the CEO and senior executives to be implemented successfully. Price and revenue optimization software is a necessary component in profitability management. And as I recently noted, its impact can be amplified when it is used in connection with complementary software, such as that designed for sales and operations planning, sales incentive management and performance management. The recent increase in management’s focus on profitability is likely to spur increased adoption of profitability management techniques and software that will allow companies to operate more effectively, not just efficiently, so that higher margins become more sustainable.


Robert Kugel – SVP Research

One of the most important IT trends over the past decade has been the proliferation of ever wider and deeper sets of information sources that businesses use to collect, track and analyze data. While structured numerical data remains the most common category, organizations are also learning to exploit semistructured data (text, for example) as well as more complex data types such as voice and image files. They use these analytics increasingly in every aspect of their business – to assess financial performance, process quality, operational status, risk and even governance and compliance. Properly applied, business analytics can deliver significant value by deepening insight, supporting better decision-making and providing alerts when situations require attention from managers or executives.

Packaged analytic applications and specialized tools continue to expand in number and improve in functionality in response to specific needs. Businesses now have access to tools for creating tailored applications for vertical industries, which means analytics use is no longer limited to trained statisticians. Techniques for processing large data sets (big data) have proliferated to the point where employing insights from them for practical business purposes is now within reach, even for midsize companies. This includes the use of predictive analytics to enable earlier and more intelligent responses to changing business conditions. Web-based platforms for handling huge sets of data make it possible for companies to access and utilize these advanced analytics economically. Advances in mobile technology provide simpler access to analytics from smartphones and tablets and facilitate collaboration.

Analytics has long been a tool used by finance. Because accounting records are numerical and readily available, people in the finance function have been able to use forms of analytics for centuries. As a result, analytical techniques for assessing balance sheets, income statements and cash flow statements are well-developed and widely accepted. Unfortunately, because these techniques are so well-established, finance professionals have been slow to broaden their palette of analytics even as the opportunities available to them have proliferated. Our research shows that many organizations lag in their use of advanced finance analytics. Many of them view the role of finance analytics narrowly; as a whole, finance has largely failed to take advantage of advanced analytics to address the broader needs of today’s enterprises and thus increase its own value. Indeed, too few professionals even realize that these tools can help finance take more of a leadership role in their corporation.

Forward-looking finance departments can start to use cutting-edge analytic initiatives in areas that include customer profitabilityprice and profitability optimization, lean manufacturing, risk mitigationvr_bigdata_big_data_capabilities_not_available and economic costing methods, but their use requires an in-depth understanding of the options available and the information technology requirements for each. Those insights alone, though, will not make a difference; few finance organizations have done the evaluations necessary for selecting the right analytic methods and tools and using them properly. For example, our research in big data shows that fewer than half of organizations are using big data for activities that should be staples of finance organizations, such as contingency (“what if”) planning and predictive analytics.

I find that prospective buyers’ poor understanding of analytics-based best practices and functional requirements are significant issues in most companies, as are deficiencies in their software and data environments. All these hinder their ability to improve their control over business processes and make it more difficult to choose new technology that can deliver value.

Our research shows that finance organizations typically trail other lines of business in adopting technology. Today, advances such as in-memory processing, big data, predictive analytics and visual discovery offer the potential to revolutionize finance analytics. Finance organizations need to understand how to use advanced analytics to achieve better performance. Analytics also must be accessible anywhere and at any time to foster collaboration and promote agility – two management ingredients that can drive superior performance. Many analytics-related processes (such as planning and reviewing) are collaborative as well as iterative. Mobile analytic capabilities (provided by smartphones and tablets) that enable quick access and instant communication across an organization are not shiny new toys. They are important components to an IT infrastructure that supports better processes.


Robert Kugel – SVP Research

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