Manufacturers face an array of complex challenges including intense competition, increasing costs, and accelerated product life cycles. Technology investments in enterprise systems such as ERP, SCM, PLM, and CRM over the last ten years have helped to rein in operating costs and increase corporate agility. As adoption of these capabilities has become widespread, they no longer provide competitive advantage. However, the resulting quantity and quality of data they produce have set the stage for business intelligence applications that leverage this data to analyze and optimize business performance.
One of the functional areas best-suited for a quantitative approach is pricing. 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 easily applied through analytical and execution applications, enabling smarter decisions and better execution across all business functions related to pricing. Given the current state of pricing practices and its considerable impact of financial performance, the potential benefits of pricing initiatives are enormous.
Moreover, manufacturers’ broad product lines, numerous customers, and negotiated sales model combine to create a business environment that is very conducive to a science-guided approach to pricing. Early adopters’ pursuit of an intelligent pricing advantage has led to the development of commercial solutions. Having worked with manufacturers for years, Zilliant’s industry-specific pricing applications provide the fit and flexibility that they need.
Until now, only a limited number of success stories have been made public due to the sensitive and strategic nature of pricing practices. This lack of publicity belies the growing use of advanced pricing techniques and applications in business-to-business markets including manufacturing. As the leading practitioners have achieved significant, measurable benefits, however, more and more manufacturers are investing in pricing-related initiatives. As a result, the industry is experiencing a steep adoption curve similar to what was previously seen in travel, hospitality and retail.
Most manufacturing companies are very familiar with the power of process automation to improve business performance. However, many are just beginning to exploit the potential of applied business intelligence applications that harness data from operational systems to improve decision-making across the enterprise. A few of these companies have embraced sophisticated analysis and optimization systems so deeply that some management strategists have dubbed them “analytics competitors”.
Regardless of how widespread a company’s use of analytics is, using more quantitative approaches to pricing can deliver significant financial gains. Many companies’ current pricing processes are manual and largely ad hoc, resulting in poorly-priced deals that leave significant margin on the table. In most cases, price, discount, and other margin-driving terms are arbitrarily based on precedent and circumstance rather than empirical analysis and strategic intent. Even companies that apply pricing principles manually, e.g. basic price segmentation, would benefit greatly from more precise price differentiation that can only come from advanced quantitative models and optimization techniques.
Following the example set in other industries such as airlines and hotels, some manufacturers began pursuing pricing initiatives years ago. Their focus has been in two areas: controlling discounting and applying optimization techniques to exploit price elasticity. These early adopters realized that the sooner they started, the greater the financial benefit. In almost every case, these projects have exceeded their projected return on investment, in some cases delivering tens of millions of dollars of incremental profit. Learning of these compelling successes, many more companies in a wide range of manufacturing sectors (industrial, high tech, chemical) are confidently pursuing similar pricing initiatives.
So what are some of the key lessons learned? And why did it take so long for manufacturers to begin to adopt quantitative pricing methods? Answering these questions requires an understanding of manufacturing’s three most pressing pricing challenges and the opportunities attendant in overcoming them. The answers also point to key capabilities are required to capitalize on these opportunities.
1. Ad Hoc Pricing Facilitates Price Differentiation: Diversified manufacturers typically have extensive product portfolios and very sizable customer bases. After taking into account all price-related variables (e.g. costs, contracts, discounts, volume agreements, customizations, shipping, etc.), the total number of unique prices in market at any one time can easily exceed 100,000. With so many products, exceptions, and changes over time, it is no wonder that manufacturers’ price points and margins vary widely across their business. In fact, some manufacturers struggle with merely calculating prices that are “correct”, i.e. are in accordance with their numerous price lists, policies, and contracts, let alone differentiating prices to maximize margins and overall profits.
Fortunately, this complexity can be not only managed, but also exploited. Information systems designed to allow companies to calculate “correct” prices are widely available, often as functional modules within the order management products marketed by ERP and CRM vendors. Better still are integrated price segmentation, optimization and enforcement applications from best-of-breed pricing application vendors like Zilliant. Their advantage is that they allow manufacturers to operationalize differentiated, profit-maximizing prices by employing science-based segmentation and optimization capabilities.
The scientific foundation of price differentiation is segmenting customers by the price sensitivity and using each price segment’s unique sensitivity to set prices on future deals. Figure 2 shows examples of how companies have dramatically increased the number of actionable segmentation variables they use to set differentiated prices.
Only by identifying and acting on the variables that most affect customers’ buying behavior can companies fully capture the value of these purchases as incremental margin on every deal.
2. Better Data Quantity and Quality Available for Quantitative Applications: Managing all of these pricing inputs and calculations typically involves a number of disparate transactional and price administration systems. Additionally, most manufacturers now have some type of customer relationship management (CRM) software in place for managing customer, sales opportunity, and competitive data, adding to the multitude of isolated systems.
Fortunately, while data collection and integration can be difficult, this challenge is now more tractable than ever. Many companies have already rationalized numerous legacy systems into a few data-rich enterprise applications, and have begun to aggregate transactional and CRM data into central repositories. Even when data remains scattered across multiple systems, improved APIs and integration tools have simplified its retrieval. In all, companies now are much better able to aggregate and analyze the data needed to define price segments and their relative profitability.
Even after aggregating all available data, 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, valuable data for determining price sensitivity. Overcoming these limitations is crucial to maximizing the profit potential from segmentation and optimization, and is a major advantage offered by Zilliant.
3. Informed Negotiations Increase Profits: Last but not least, the negotiating behaviors of sales personnel and channel partners play a major role in determining the profitability of deal outcomes. Uninformed decisions related to discounts and other costly terms is a major source of revenue leakage in most companies.
The good news is that the most powerful negotiating tool available is information. Giving sales people reasonable price recommendations based on quantitative information about what similar customers paid under similar circumstances immediately improves results. In addition to better market information and price recommendations, incentives also have a powerful effect on sales representatives’ discipline and confidence. And when management deems that an order deserves an exception to standard discounting policy, the ability to evaluate different scenarios based on their relative profitability ensures “must win” deals helps limit the overall financial impact.