In narrowing down the key elements of its solution, Zilliant points out three main challenges that manufacturing and distributing customers face on the road to pricing excellence. These challenges can be parlayed into data-driven pricing management opportunities for such B2B environments:
1. The typical business environment of B2B companies creates massive customer-product-price combinations. The large numbers, coupled with dynamic and complex customer relationships, products, promotions, discounting practices, and channels, proliferate price rules and exceptions. When all pricing rules and policies are considered, the typical manufacturer has dozens of thousands of prices, while the typical distributor has even hundreds of thousands. The upside to this complexity, however, is that by definition, net prices are already differentiated (determined deal-by-deal) and are largely opaque (that is, not published to the market). In B2B environments with exception-based pricing, a smart and informed company can easily adopt a more sophisticated approach to price differentiation based on price segmentation to maximize margins.
2. Paralleling the product and price complexity and the number of combinations is the complexity of transactional processes and systems. The typical scenario usually includes a combination of standard transactions processed in multiple enterprise resource planning (ERP) and order management systems combined with a large number of ad hoc exceptions executed through spreadsheets, manual system overrides, and post-transaction credits and debits. The plethora of data that is produced is inconsistent, dirty, and complicated, and thus obscures segment-specific price responses. In many cases, the data makes it hard just to determine whether individual deals are profitable or not. Specifically, it is common for net prices to reflect as many as half a dozen inputs, including several manual and discretionary variables. On top of that, most manufacturing and distribution enterprise applications were designed and implemented with the "from the shop out and inside out" mindset rather than the "from the customer in and outside in" one. Meaning, these applications favor the old-time equation of product cost plus profit margin equals customer price, instead of allowing the customer and the market to determine prices. As a result, getting the right price, and determining whether or not the company made money after the fact (by calculating and tracking the net realized price and margin at the product level), are well beyond the vast majority of manufacturing and distribution companies' means. There again, on the positive side, firms that can effectively measure and analyze segment-specific price response and profitability should be able to leverage this insight to a competitive advantage.
3. Final prices are heavily influenced by the negotiation process, unlike the "take it or leave it" pricing common in B2C industries. The term negotiated prices here refers to variable price outcomes that result from discretionary decisions made by salespeople on discounts and other financial terms. Many of these companies have tenured salespeople who negotiate based more on habit and relationships than on verified market information and customer value. The good news here though, is that with better information and specific, actionable guidance, such behaviors can be modified, producing higher price points regardless of a salesperson's experience or preexisting bias. In other words, improving deal-level sales decision making should also considerably increase profit margins.
Zilliant contends that there is a better way to price—a more analytical (scientific) and automated approach that it calls data-driven price management. This approach reportedly not only helps sales professionals to recognize and take advantage of opportunities that will improve margins (and likewise for marketing and pricing operations), but it also makes the pricing process more streamlined and efficient. Companies that have adopted a data-driven price management approach have not only improved gross margins, but they have also increased pricing agility and control.
With their greater use of enterprise resource planning (ERP), customer relationship management (CRM), and order management solutions in recent years, enterprises have amassed an enormous amount of transactional pricing data. This data can now be processed and combined using the latest innovations in pricing science to reveal where and how to improve price management. The science-based insights synthesized from this data, when paired with analytical, optimization, and process automation software, generates more accurate, effective pricing policies and guidance to increase revenues and profits.
To that end, Zilliant's offering, Zilliant Precision Pricing Suite (ZPPS), is a broad solution for price segmentation, analysis, setting (including price optimization), and execution. ZPPS identifies the four steps to establishing a strategic pricing process:
1. price segmentation—understanding what factors affect price response, and using these criteria to filter, benchmark, and set optimized pricing with precise, transaction-level granularity
2. sensing (analysis)—the process of measuring and comparing how price response and margin performance varies across a company's customers, products, and programs
3. setting—the process of establishing list and target prices, discounts, promotions, negotiating guidance, and other policies
4. enforcing—the method a company uses to implement its pricing policies, guidelines, or targets inside of transactional processes and across sales channels
Every company, knowingly or not, goes through these steps when setting and negotiating pricing, although most companies do not do it as effectively as they could because they rely on rudimentary methods or flawed techniques.
Zilliant's roots and initial focus have long been on the sales decision-support side (price analysis and planning, optimization, and negotiations). Over the last two years, the vendor has added several applications on the operations side of the sales process that include price list administration, deal execution, and policy enforcement. As the segmentation model is based on measurable, deal-specific attributes, it can be applied to these operational activities as well, improving decisions and margins at every turn. This characteristic is what makes price segmentation the foundation for effective, data-driven price management, and is why all ZPPS applications have been designed and built with Precision Price Segmentation as their scientific foundation.
1. The typical business environment of B2B companies creates massive customer-product-price combinations. The large numbers, coupled with dynamic and complex customer relationships, products, promotions, discounting practices, and channels, proliferate price rules and exceptions. When all pricing rules and policies are considered, the typical manufacturer has dozens of thousands of prices, while the typical distributor has even hundreds of thousands. The upside to this complexity, however, is that by definition, net prices are already differentiated (determined deal-by-deal) and are largely opaque (that is, not published to the market). In B2B environments with exception-based pricing, a smart and informed company can easily adopt a more sophisticated approach to price differentiation based on price segmentation to maximize margins.
2. Paralleling the product and price complexity and the number of combinations is the complexity of transactional processes and systems. The typical scenario usually includes a combination of standard transactions processed in multiple enterprise resource planning (ERP) and order management systems combined with a large number of ad hoc exceptions executed through spreadsheets, manual system overrides, and post-transaction credits and debits. The plethora of data that is produced is inconsistent, dirty, and complicated, and thus obscures segment-specific price responses. In many cases, the data makes it hard just to determine whether individual deals are profitable or not. Specifically, it is common for net prices to reflect as many as half a dozen inputs, including several manual and discretionary variables. On top of that, most manufacturing and distribution enterprise applications were designed and implemented with the "from the shop out and inside out" mindset rather than the "from the customer in and outside in" one. Meaning, these applications favor the old-time equation of product cost plus profit margin equals customer price, instead of allowing the customer and the market to determine prices. As a result, getting the right price, and determining whether or not the company made money after the fact (by calculating and tracking the net realized price and margin at the product level), are well beyond the vast majority of manufacturing and distribution companies' means. There again, on the positive side, firms that can effectively measure and analyze segment-specific price response and profitability should be able to leverage this insight to a competitive advantage.
3. Final prices are heavily influenced by the negotiation process, unlike the "take it or leave it" pricing common in B2C industries. The term negotiated prices here refers to variable price outcomes that result from discretionary decisions made by salespeople on discounts and other financial terms. Many of these companies have tenured salespeople who negotiate based more on habit and relationships than on verified market information and customer value. The good news here though, is that with better information and specific, actionable guidance, such behaviors can be modified, producing higher price points regardless of a salesperson's experience or preexisting bias. In other words, improving deal-level sales decision making should also considerably increase profit margins.
Zilliant contends that there is a better way to price—a more analytical (scientific) and automated approach that it calls data-driven price management. This approach reportedly not only helps sales professionals to recognize and take advantage of opportunities that will improve margins (and likewise for marketing and pricing operations), but it also makes the pricing process more streamlined and efficient. Companies that have adopted a data-driven price management approach have not only improved gross margins, but they have also increased pricing agility and control.
With their greater use of enterprise resource planning (ERP), customer relationship management (CRM), and order management solutions in recent years, enterprises have amassed an enormous amount of transactional pricing data. This data can now be processed and combined using the latest innovations in pricing science to reveal where and how to improve price management. The science-based insights synthesized from this data, when paired with analytical, optimization, and process automation software, generates more accurate, effective pricing policies and guidance to increase revenues and profits.
To that end, Zilliant's offering, Zilliant Precision Pricing Suite (ZPPS), is a broad solution for price segmentation, analysis, setting (including price optimization), and execution. ZPPS identifies the four steps to establishing a strategic pricing process:
1. price segmentation—understanding what factors affect price response, and using these criteria to filter, benchmark, and set optimized pricing with precise, transaction-level granularity
2. sensing (analysis)—the process of measuring and comparing how price response and margin performance varies across a company's customers, products, and programs
3. setting—the process of establishing list and target prices, discounts, promotions, negotiating guidance, and other policies
4. enforcing—the method a company uses to implement its pricing policies, guidelines, or targets inside of transactional processes and across sales channels
Every company, knowingly or not, goes through these steps when setting and negotiating pricing, although most companies do not do it as effectively as they could because they rely on rudimentary methods or flawed techniques.
Zilliant's roots and initial focus have long been on the sales decision-support side (price analysis and planning, optimization, and negotiations). Over the last two years, the vendor has added several applications on the operations side of the sales process that include price list administration, deal execution, and policy enforcement. As the segmentation model is based on measurable, deal-specific attributes, it can be applied to these operational activities as well, improving decisions and margins at every turn. This characteristic is what makes price segmentation the foundation for effective, data-driven price management, and is why all ZPPS applications have been designed and built with Precision Price Segmentation as their scientific foundation.
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