Chapter 16
An Enabling Systems Architecture
It would appear that we have reached the limits of what it is possible to achieve with computer technology, although one should be careful with such statements, as they tend to sound pretty silly in five years.
—JOHN VON NEUMANN
Throughout the past 10 years, strategic pricing has been a top priority for many companies. During this time, companies have made significant investments in their enabling processes and systems architecture to ensure that the bottom-line benefits of their pricing strategy are truly sustainable and have become deeply embedded in business operations.
High-performance companies have adopted sophisticated pricing solutions not only as a means to achieve significant and quantifiable bottom-line benefits but also as a source of competitive advantage. By eliminating Excel spreadsheets, ad hoc business intelligence reports, and e-mail as the primary ways to communicate pricing guidelines and approve pricing, such companies have increased total operating income from 1 percent to 3+ percent. Furthermore, the capabilities provided by pricing solutions allow companies to adapt quickly to changing market conditions and competitive threats. Without institutionalizing pricing strategy by implementing the appropriate supporting technology, companies will find the half-life of pure pricing strategy efforts to be shorter than they need to be.
Capturing, interpreting, and applying contextual information within your company’s sales and pricing process and technology is the next evolution in high-performance pricing capabilities. In order to achieve this level of sophistication, there are two key systems architecture considerations above all others that must be incorporated:
1. Collecting context. By integration of the sales and pricing processes and applications.
2. Applying context. By flexibly adjusting the pricing strategies and algorithms to set and negotiate prices.
The holistic integration of sales and pricing provides a mechanism to leverage the sales force in collecting contextual information and ensures that it is considered when setting and negotiating price. Given that context is diverse, however, a system with flexibility in applying contextual information is important to address the diversity and keep up with a changing market and competitive environment.
Requirements of Pricing Solutions
Although the landscape of pricing solutions is broad and varied, virtually all of them typically include three core capabilities:
1. Pricing analytics. The ability to mine and explore transactional data to understand areas of opportunity for pricing and profitability improvements.
2. Price setting. The application of pricing strategies and algorithms to determine and optimize list and customer prices.
3. Price administration and execution. The management of pricing as it relates communicating target prices and allowable discounts so that sales resources can create quotes or contracts, and the transfer of pricing into order execution systems.
If combined with a solid strategy, governance, and data infrastructure, these solutions create a “closed-loop pricing” capability that sustains the value that pricing strategy can deliver. Closed-loop pricing is the incorporation of insights gathered from transaction pricing execution to refine and adjust pricing strategy on an ongoing basis, thus enabling an enterprise to adjust to changing market conditions to maximize returns.
Implementing an industrial-strength system, such as PROS or Vendavo, is a great way to ensure these key pricing capabilities are available to your business in support of your pricing initiative. While the resources to purchase, integrate, and operationalize these systems are not trivial, the returns can be material. We find that for a larger company, the costs amount to perhaps 0.01 to 0.05 percent of sales, while the returns from the price analytics alone have yielded millions within even 30 days. That said, the best technology for your company will vary with your industry, business objectives and pricing challenges.
Dimensions of the Pricing Decision
Companies generally use leading-practice pricing solutions to help evaluate and manage three main dimensions that are used to determine the right pricing decisions: customer, product, and market. Attributes such as the customer’s size or importance to the company determine price or guidelines for discounting and other adjustments to the price.
Today’s leading solutions have been instrumental in helping companies enact more effective pricing strategies, considering these three dimensions of pricing—and in the process, the strategies have had a substantial impact on the profitability of companies that use them. However, the benefits generated by such solutions will be fleeting—and the technologies themselves will become obsolete—if a business fails to accommodate the crucial fourth dimension of pricing: context, which defines the buying situation and provides a full picture of the micromarket for the sale. Applying context is the final puzzle piece that enables a company to tailor its value proposition in a way that will increase the likelihood of a purchase by customers.
Incorporating Context into Pricing Decisions through Technology
Incorporating context into pricing decisions through technology involves five major enhancements to pricing processes and their supporting systems. Each addresses the need for more relevant information, better decisions, management control, and operational alignment.
1. Altering the pricing waterfall
2. Enabling price setting with context
3. Refining deal constructs through the holistic integration of sales and pricing
4. Facilitating context-specific pricing approvals
5. Aligning contextual pricing with business operations
1. Altering the Pricing Waterfall
Fundamental to any pricing system is the pricing waterfall, a representation of all the revenue and cost elements contributing to the achieved net and pocket price and profitability of a particular transaction. The pricing waterfall drives not only the design of analytics in a pricing solution but also the algorithm to determine how a specific deal should be priced and how the net and pocket contribution will be modeled. This component affects all three of the core capability areas within a pricing solution. Most companies’ pricing waterfalls today cannot account for the critical dimension of context, however; thus, two key enhancements to the pricing waterfall are required.
The First Enhancement. This involves the moving from list price to context-specific opening price. The classic pricing waterfall—the cornerstone of price analytics and price management—starts with list price as the highest benchmark for pricing and profitability and from that point forward “trickles down” to pocket price and pocket margin. As stated earlier, however, list price is irrelevant without context. Therefore, a company’s waterfall should reflect the influence of context in setting an opening price. This is, in essence, a buildup to the price from which deal-specific negotiations begin.
As an example, in the banking industry, the base interest rate for a loan is entirely dependent upon larger, market-based indices. Contextual refinements to this interest rate are then applied based on inherent risk in the type of loan (e.g., mortgage, car, cash advance), the customer’s risk assessment (e.g., credit score), and perhaps the business relationship. The net result is the context-specific interest rate.
The Second Enhancement. The second enhancement concerns the factors that determine the context of a specific deal. Because no company can anticipate all the possible drivers of context when designing and implementing a pricing solution, it should look to incorporate some “catchall buckets” in the pricing waterfall that will allow for the capturing of additional drivers as they are known. Through pricing analytics, the company should monitor new drivers added to the catchall buckets to determine if any is emerging and should be tracked independently. For example, an avionics-equipment manufacturer included standard discounting waterfall buckets such as volume discount but also added a placeholder for “other discount,” for which the sales force was required to specify the reason for the discount. Over time, a trend emerged correlating heavier competitive match discount following major industry trade shows or exhibits where competitors offered temporary price promotions. This catchall bucket helped identify the need to consider proximity to trade show season as additional context in setting price and driving sales.
Creating “catchall buckets” helps companies capture important contextual data.
2. Enabling Price Setting with Context
One difference between context and older value approaches lies in the scope of what information is gathered and applied in the pricing process. When setting prices, a systems architecture and design should allow for the incorporation of context to inform that price. Key to this ability are both descriptive and predictive analytics that generate a solid understanding of which factors have influenced and will influence a customer’s buying behavior. With this insight, a company can design pricing solutions with the flexibility to apply various pricing strategies and algorithms based on context that, in turn, will set the appropriate price for maximizing value while maintaining or improving win probability.
A pricing system’s architecture should include analytical capabilities to determine customers’ price sensitivity and willingness to pay not at the average level, but at the detailed level of the contextual conditions of the buying situation. This can be done by implementing pricing power and pricing risk designations to a combination of factors used in the price setting algorithms within the pricing solution. Furthermore, by coupling the analytics and transactional history in a pricing solution with the optimization capabilities to detect factors that are driving a difference in pricing and/or profitability, pricing professionals have the inputs necessary to model response to price changes. For example, at one pet products company, statistical analysis was performed to determine which pricing levers had the most impact on yielding a successful markdown result.
Although it is valuable to analyze history to understand contextual drivers that have yielded different price sensitivities and willingness to pay, more valuable still is the ability to forecast changes in context, in one month’s or one year’s time, and construct robust what-if models of how this could affect business results. Implementing predictive analytics as a part of a pricing system’s architecture can help companies further increase profitability. Predictive analytics are enabled by sophisticated statistical, forecasting, and modeling capabilities, and can be implemented as the next layer above descriptive pricing analytics or in combination with other functional data (from such areas as the supply chain and R&D) to set context-specific prices.
At one international aerospace and high-tech manufacturer, predictive analytics are being used to model potential variability in key factors such as raw materials prices, consumer demand fluctuations, and manufacturing-defect rates. These analyses are incorporated into future bids and programs in the form of additional risk-mitigation items or justifiable cost contingencies.
Oftentimes, different contexts defining the micromarket of a transaction require different pricing strategies to be applied—for the same product or service. In the oil and gas industry, for example, pricing can be associated with several market indices, such as Platts Global Petrochemical Index or Oil Price Information Service (OPIS). The difference between these two is that the former is associated with the refinery and tends to favor a cost-plus approach, while the latter is based on prices at the terminals and therefore is more market/value-based. For one global oil and gas company, depending on the level of risk that is expected in the market and/or the level of favorable (or unfavorable) pricing volatility projected, the application of one index versus another for pricing of longer-term contracts will drive very different bottom-line results.
Seagate, a manufacturer of data storage solutions, typically takes a premium approach to pricing. However, during the highly competitive “back to school” season, the company applied competitor-based pricing to capture market share.
We emphasize that while the concept of varying prices according to context is intuitive—everyone knows a street vendor’s umbrella costs more when it is raining—the challenge to a large enterprise is to execute this concept accurately, at scale, across thousands of deals involving billions of dollars. As the above two examples illustrate, having flexibility within a pricing system to set prices via different strategies, depending on the context expected for that transaction can be a potent competitive weapon.
Not only should the pricing strategy applied vary based on context, but the actual pricing logic implemented in the pricing solution also must be extended to consider all key drivers of context. The way in which pricing systems derive and set prices can be compared, in the simplest form, to a formula in Excel. For a simple cost-plus pricing strategy, the algorithm to set price can be thought of as follows:
Where Customer segment = A
Product family = B
Market = M
Price f(x) = Cost + Markup
When considering context in the way in which it prices, a company must consider context not only in defining the micromarket or microsegment for pricing but also in the variables for the pricing algorithm as follows:
Where Customer segment = A
Product family = B
Market = M
Product lifecycle = L
Season = S
Competitive environment = E
Need/Want = W … etc.
Price f(x) = Cost + Base markup + Adjustment for L + Adjustment for S + Adjustment for E + Adjust for W … etc.
In the meatpacking industry, there are a number of factors that are considered to optimize prices and profitability such as size, marbling, certification, commodities market indices, current supply, yield from an animal, seasonal concerns, amount of processing, and other customer requests. To support this type of capability in a pricing system’s architecture, a company must have a foundational layer of data that provides the inputs for each contextual element in the formula. As discussed in the previous chapter, much of this key contextual data may not yet be in a company’s databases or systems and therefore will have to be captured to be used in price-setting logic.
A company must have a foundational layer of data that provides the inputs for each contextual element in the formula.
The combination of descriptive and predictive analytics, price-setting capabilities and a solid understanding of context-driving pricing, and profitability differentiation can yield powerful business results—as one global alcoholic beverage manufacturer has proved. A holistic systems architecture provided “what-if” style, bottom-up sales-and-pricing planning and executive roll-ups of modeled business results. The enabling systems architecture increased profitability as a result of optimized prices and accurate forecasts, and it yielded higher operational efficiency and lower overall cost to produce.
3. Refining Deal Constructs through the Holistic Integration of Sales and Pricing
The price-setting function within a pricing solution is just the starting point for integrating context into the pricing process. A company also must gather additional context and interpret and apply that context to a specific deal to extract the optimal value and provide a winning value proposition to a customer. This is particularly important in business models that include quoting or contracting processes with negotiations leading up to the actual sale execution. At a high level, the sales and pricing process is represented in Figure 16-1.
Figure 16-1 Holistic sales and pricing process.
The process begins with customer relationship and opportunity management. Leads are generated and qualified, and pricing iterations are executed to model the deal. Much of the contextual information about customers, their value drivers, their buying preferences, and the competitive situation are gathered on the front lines by the sales force. Oftentimes, this context exists only as institutional knowledge and is not captured in a company’s system or database. Therefore, a company should see that it has a robust customer-relationship and sales-management process embedded into its systems architecture via CRM tools to ensure that context is captured and can be passed to other business processes and applications. More specifically, structured data about customers captured in account management modules can be integrated with sales leads and opportunity tracking. This can be further integrated into the pricing engine to deliver all the customer and buying situation context to systematically determine the right pricing structure. For example, a global services company incorporates information about customer operations, up-front investments needed to mobilize a project, and availability of resources with given skill sets to inform how it will structure the pricing of a job for an individual customer.
During the upstream opportunity-management process, the terms and conditions of the deal are negotiated; this is additional context that must be considered in order to have a complete picture of the pricing structure. Capturing this data for a company’s system can be challenging because such data is generally a mix of tangible and intangible items: payment terms, freight terms, fulfillment commitments, delivery times, issue resolution, design services, engineering support, length and depth of customer/vendor relationship, warranty, cost escalators, and pass-through expenses. Additionally, much of this data is unstructured—stored in documents instead of database fields—which further makes it difficult to capture, organize, and analyze. The first step in collecting the terms and conditions for a deal is to standardize, as much as possible, the offerings so they can be gathered in a database and associated with the transaction(s) to provide the full context in pricing and analytics. A secondary, but important, benefit is that it helps to inform the service strategy for the portfolio or segment of customers that, if aligned with pricing strategy, can yield further benefits to the bottom line.
Incorporating contextual information into a company’s pricing architecture also provides very valuable tools in pricing negotiations: information and increased confidence. Oftentimes, when faced with “You are too expensive” or “The competition can do it for less” tactics from procurement, sales teams tend to concede price because they lack the confidence to address these allegations head-on. With the captured contextual information, sales teams can be informed as to achievable (demonstrated) pricing power and have the confidence to address procurement’s allegations.
Integrating the sales and pricing applications in the systems architecture also helps facilitate user adoption within the sales force. Upstream sales-management applications for customer relationship and lead or opportunity management are typically the portal through which the sales force enters and views data. By making this portal a one-stop shop for relationship management, opportunity tracking, and pricing—and enabling mobile technology to deliver this in a convenient way—a company can significantly increase the likelihood of capturing higher amounts of quality contextual data. Finally, automated data sharing across the applications reduces time spent by the sales force and ensures consistency in the contextual data used to drive pricing and operations decisions.
4. Facilitating Context-Specific Pricing Approvals
Pricing decisions and actions are typically initiated long before the final sale is booked or the order is processed and filled. In a negotiated B2B transaction, the initial quote is crucial, but there will often be multiple subsequent refinements of pricing. The key question is: at what point in the sales process does an organization have the full context to make a decision on price? The answer: at almost any point, but with variable degrees of confidence.
The sales process usually has adequate contextual information for better pricing, but confidence in that information may be uneven.
With each step in the process, more context is gathered that will further inform the right pricing approach and price level required to meet the buying situation—driving up the confidence level that the price is right. The pricing systems architecture must be designed and implemented with tight integration between the pricing solution or engine and the upstream sales-management application to ensure that context is collected and delivered, and can be applied at the time of pricing determination and negotiation.
With context being captured with deals upstream in the sales process, a company can monitor and correct bad pricing and negotiation practices before it’s too late. This third type of pricing analytics should be incorporated into a company’s overall enabling systems architecture to ensure that pricing guidance and policies are being implemented throughout the sales progression. Because the opportunities or deals are in progress, audit and corrections can ensure a company doesn’t get locked into a deal or contract with unfavorable pricing structures. In recent years, for example, a printing company bid to secure the contract for a state board of education. Enticed by the idea of becoming the sole source provider for printing services, it submitted a heavily discounted list of prices, secure in the belief that volume would make up the difference. The printer won the contract, but its pricing did not incorporate the extra delivery costs associated with the fact that the schools to which it would ship its materials to had no loading docks and often required walk-up delivery. Had there been a more comprehensive opportunity management process to identify this critical issue, the printer would not have made such a costly mistake.
5. Aligning Contextual Pricing with Business Operations
One critical but often overlooked process and systems link is the one between the deal construct shaped by the sales and pricing organizations and the operations and execution capabilities of the finance, supply chain, and R&D functions. Simply put, while there is little value in a pricing architecture that does not support a company’s ability to set and execute context-specific pricing, there is no value—and much harm—in investing in a pricing architecture so complex that it cannot be effectively used by the organization.
As mentioned previously, there are tangible and intangible components of a deal’s pricing construct, and when these are included, a company must be sure that it can deliver on those commitments. Additionally, in order to have visibility into the full life cycle of a deal from an analytics perspective, the systems architecture must be designed and implemented to ensure all relevant data is captured. Finally, contextual information is valuable in the R&D process as new products and services are being developed.
A well-designed pricing architecture will feed crucial information on market response and willingness to pay back upstream to enable constant improvements to the features—or costs—of the products and services being sold. In all these cases, integration of downstream processes and systems is critical to ensuring that information on the context-specific outcomes of pricing strategy is transferred from the sales and pricing system into the ERP system and can be extracted for continuous improvement of pricing and profitability.
Ensuring that the pricing procedure in the ERP system is aligned with the pricing waterfall (and therefore with the quote/contract modeling algorithm and pricing-analytics data structure) guarantees consistent execution of a company’s pricing strategy. It follows, therefore, that if the pricing waterfall is altered to include contextual items, the order processing and fulfillment ERP functions also must reflect these contextual pricing elements. By following this leading practice approach, one global glass manufacturer saved hundreds of man-hours in designing and implementing its integrated, enabling systems architecture between pricing and ERP execution.
Furthermore, supply-chain operations and the execution of these processes in the ERP system must also be aligned—you must be able to deliver on the service levels incorporated into your pricing structure. For instance, if a company charges a premium for a product because it commits to filling every order within a certain timeframe, the company must be able to prioritize that customer and ensure that expedited delivery services can be invoked for that customer or order. One global industrial equipment manufacturer aligned its operations to deliver replacement parts in 24 hours or less and priced accordingly for that service. It understood its customers’ value driver for uptime/production efficiency and incorporated that context into not only the way it priced but also the way it executed. The inverse is also true: an industry-leading technology equipment provider was able to link its pricing tools directly to its supply-chain tools so that the pricing team could have near-real-time information on current and impending inventory levels. This allows them to dynamically change prices and up-sell or down-sell recommendations to the front-line sales reps and ensure optimal supply-demand balancing.
End-to-End Systems Architecture for Contextual Pricing
The capstone to an enabling contextual pricing systems architecture is the holistic integration of sales and pricing processes and technology—from upstream opportunity and negotiation management to pricing and further order execution and financial accounting. Figure 16-2 illustrates such an integrated architecture:
Figure 16-2 End-to-end systems architecture
If we step through the architecture from a data-flow perspective, we begin with a sales management application (e.g., CRM or SalesForce. com) that provides the backbone to compile and integrate the institutional knowledge and other contextual details that will inform the price-setting function. Should your company not have a robust sales management system, incorporating some of this functionality into the pricing system itself could be a first step. This is the key interface point with the sales force, so mobile update capabilities are important in this architecture to ensure ease of use and adoption by this key user group. Additionally, reporting on this pipeline of information is crucial to proactively drive the best deal and pricing constructs. Finally, implementing an approval process and workflow that optimizes efficiency in negotiations and cycle time to respond to customers within this system ensures that the right deal context is available to make an informed, profitable business decision.
The pricing application should be tightly integrated to this sales-management application. From an architectural perspective, real-time or high-frequency batched interfaces should be used to synchronize data between the applications. As key customer, product, market, and contextual information are captured, it should be passed to the pricing application so as to avoid additional effort for data entry and maintenance. This lessens the burden on the sales force, thereby improving the likelihood of adoption. Furthermore, by having bidirectional integration and synchronization, data quality and consistency across these applications are ensured and build credibility in the data across the board. In one large implementation integrating sales and pricing applications, for example, the architecture was designed and supported approximately 50,000 messages for data integration daily and 30,000 messages for security integration based on yearly 60,000 deal transaction volumes.
Within the pricing application, there are three main considerations that drive contextual pricing in the enabling systems architecture. First, the waterfall design should drive the deal modeling logic by incorporating the contextual information. Second, the design should allow flexibility to apply different pricing strategies based on the context or buying situation. Each pricing strategy implemented should also consider contextual elements in the algorithm to set context-specific opening prices versus list prices. And finally, time-phase pricing in the deal modeling should allow a better representation of the financial implications of the transaction, especially with business models that are highly asset-intense or use rebate or incentive programs over the life of the arrangement.
The pricing application should be integrated with the ERP systems in order to maintain process efficiency and data consistency. Not only should the price setting engine interface with ERP to send approved prices to be applied to sales orders, but it should also be integrated to pass approved contracts and quotes with the full deal structure. By doing this, orders can be created with reference to “umbrella” agreements and a full life cycle analysis of the deal is possible—better allowing for value realization from plan to actuality.
The key to enabling a system’s architecture for contextual pricing is the holistic integration of sales and pricing processes and technology—from upstream opportunity and negotiation management to pricing and further order execution and financial accounting.
Finally, a robust analytics engine completes the closed-loop pricing cycle by providing insight, both descriptive (historic) and predictive. By associating the contextual elements to the transaction, the business is better able to analyze factors contributing to higher realized price. Advanced analytics and statistical interpretations based on this data can be used to generate predictive forecasts of how different context will translate into the price or volume responses.
Summary
The holistic integration of sales and pricing provides a mechanism to leverage the sales force in collecting contextual information and ensuring that it is considered when setting and negotiating price. Doing so entails:
1. Collecting context. By integration of the sales and pricing processes and applications.
2. Applying context. By flexibly adjusting the pricing strategies and algorithms to set and negotiate prices.
Given that context is very diverse, a system with flexibility in applying contextual information is important in keeping up with multiple changing markets and competitive environments.