How One Vendor Parlays Price Variation into Profit Improvement Opportunities

Zilliant is a data-driven price management software provider that aims to help business-to-business (B2B) companies maximize revenue and margins using advanced price segmentation, optimization, and execution capabilities. To learn more, please see part one of this series What if Companies Could Use Science to Align Prices to Market and Maximize Margins?

The Pricing Challenges that B2B Participants Face

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.

In B2B markets, it is crucial to get the best price possible on every deal in order to maximize margins. Yet most B2B companies do not perform a deep enough analysis of pricing data to recognize opportunities that can improve margins and revenues. The data that is generated from the countless combinations of products, customers, promotions, channels, and terms is too complex for an analysis based on manual techniques. The result of using manual techniques is price management decisions that are highly subjective, and therefore suboptimal. For more information, see Advancing the Art of Pricing with Science.

Data (Not Hunch) Should Be in Pricing's Driver Seat

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.

A Profit-Maximizing, Science-based Foundation—Precision Price Segmentation

Precision Price Segmentation harnesses the power of variable price response by identifying, classifying, and organizing all customer, product, and order attributes that correlate with price sensitivity in a given market. To date, Zilliant's Precision Price Segmentation has catalogued over fifty customer, product, and order attributes that commonly drive price response for B2B companies. It is typical, though, that only about half a dozen of these attributes prove meaningful for any given deployment. For example, a company may learn that the combination of circumstances related to the end-customer's industry; the product's end-use; the product's category, group, and stock-keeping unit (SKU); order size; competitive intensity; and product mix are what drive price in their industry. Even with just five or six attributes, the combinations of their values can yield a massive (and therefore precise) number of unique price segments.

Two factors promote precision within Precision Price Segmentation. For one, while many companies already consider deal attributes when making pricing decisions, they typically do so in an arbitrary, qualitative fashion. For example, different orders may be eligible for different discounts depending upon whether the order is "small," "medium," or "large" according to subjective order size buckets. In contrast, Precision Price Segmentation quantifies and categorizes order breakpoints based on statistics that reflect the actual differences in market price response. Furthermore, Precision Price Segmentation augments these attributes with previously unconsidered attributes also proven to influence price outcomes, thereby increasing the overall precision and impact.

Given that each attribute may have up to several hundred discrete values (or even more, as in cases where the product attributes are characterized at the SKU level), the number of resultant precision price segments is usually in the thousands, or even tens of thousands, as shown in table 1. While the number of resultant actionable price segments may seem daunting, it certainly points out how "off the market" (imprecise) companies can be in their existing "broad brush" price policies and negotiation guidelines.

User Company Type Qualitative Segment Considerations (pre-Zilliant) Zilliant Precision Segmentation Attributes Approximate Number of Actionable Pricing Segments
High-tech distributor estimate of annual spend annual spend, manufacturer's rebate, margin category, product segment 6,000
Industrial manufacturer pricing group, job size, project type pricing group, job size, project type, dominant product class, channel, market size, inventory 30,000
Food distributor customer spend zone customer spend zone, cuisine type, region type 300,000
Construction equipment provider product-dealer country, product-dealer, competitive region 9,000
Medical devices manufacturer contract volume, product type contract volume, product type, wallet share, customer type, product bundle 120,000

Table 1. Examples of Precision Price Segmentation (Zilliant, 2006)

To mitigate the impact of data sparsity (that is, setting prices where too little data exists), the second precision aspect concerns the concept of actionable price segments. While every possible combination of attribute values defines a unique price segment, Precision Price Segmentation automatically filters out any combinations that do not occur frequently enough to generate a statistically significant data set.

To that end, Zilliant's patent-pending Dynamic Data Aggregation (DDA) capability ensures that price-segment driven operations (benchmarking and optimization, for example) are carried out at the most granular (precise) level within the segmentation tree given the transaction velocities in each segment. In other words, DDA ensures that where little data exists (as is the case with slow-moving or new products), the area is pooled with the most appropriate segment (along with its parent product category, for example), which is determined by the hierarchy. As soon as sufficient history is accumulated, DDA automatically begins grouping the data into a more precise (lower-level) segment based exclusively on its individual transactions.

Ultimately, Zilliant sees price segments as the scientific foundation for data-driven price analysis, setting, and execution, since well-ascertained price segments enable companies to benchmark and optimize prices, thereby improving all facets of price decision making and driving significant increases in margins and profits. As explained in The Rise of Price Management, a company has a choice of different pricing processes or software categories (price execution, price enforcement, price visibility, price optimization, pricing management, etc.) available to it depending on which exact pricing problem (or in which selling phase) it is trying to solve.

This is the part two of the series What if Companies Could Use Science to Align Prices to Market and Maximize Margins?, which takes an in-depth look at the price management software provider, Zilliant, and its enterprise pricing solutions.

In the next part of this series, Zilliant's ZPPS and its applications will be explored and explained in greater detail.

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