The process of price segmentation naturally starts with data integration. This means collecting quote or order transactional data, and enriching this data with extended information on customers, products, and competition. Moreover, price segmentation—determining how price outcomes vary based on customer, product, and order attributes—represents the foundation of some price management vendors' offerings that assist in making effective price decisions throughout most phases of the sales process.
In simple terms, price segmentation leverages advanced pricing science to sift through volumes of historical pricing data and to cull out price response patterns. These variations in price response are correlated with circumstances under which the deals were priced, and reflect market forces such as competition, relative solution value, customer-supplier relationship strength, and price elasticity. Understanding how price response varies across different customer, product, and deal circumstances enables companies to establish differentiated, profit-maximizing pricing policies. In other words, the goal of price segmentation is to increase the consistency of prices for deals sharing similar circumstances, while maximizing price differentiation where different deal circumstances permit.
According to Wikipedia, in economics, elasticity is the ratio of the incremental percentage change in one variable with respect to an incremental percentage change in another variable, and is usually expressed as a negative number, but shown as a positive percent value. One typical application of the concept of elasticity is to consider what happens to consumer demand for a good (a product or service) when the price of that good increases. As the price of a good rises, consumers will usually demand lower quantities of that good—perhaps by consuming less of the good, substituting it with other goods, and so forth. The greater the extent to which demand falls as price rises, the greater is the price elasticity of demand. However, there may be some goods that consumers either require, cannot consume less of, or cannot find substitutes for, even if prices rise (for example, certain prescription drugs). For such goods, the price elasticity of demand is considered inelastic.
The concept of elasticity has a wide range of applications in economics. In particular, an understanding of elasticity is useful in understanding the dynamic response of supply and demand in a market in order to either achieve an intended result, or to avoid an unintended result. For example, a business considering a price increase of its product might find that doing so lowers profits if demand is highly elastic, as sales would fall sharply. Similarly, a business considering a price cut to its product might find that it does not increase sales if demand for the product is price-inelastic.
While price elasticity explains differences in price response, determining it and applying it to transactional pricing, especially in B2B enterprises, is extremely difficult. Some price management vendors have developed techniques and technologies that leverage price elasticity in ways that can be used in real-world applications to differentiate pricing based on subsegments related to all the factors driving elasticity. Transactional price segmentation reveals differences in price response that can lead to increased profits when dealing with more amenable-to-pay or needy customers (while not necessarily losing profit on the less willing-to-pay customers).
Thus, price segmentation enhances the practice of charging different prices in different segments of the market. It is an approach already employed in B2B markets (albeit in a rudimentary way), with the underlying economic phenomenon being that customers attach different values to goods and services under different market conditions and transactional circumstances.
The central premise of price segmentation is quite intuitive (although the calculations behind it are of the "rocket science" sort). The premise is this: pricing should be consistent for similar deals as opposed to being based only on the more general, one-dimensional elasticity factors like volume or product category. Aligning prices to precise segments is fundamental to maximizing margins, but most B2B companies take little advantage of price segmentation because of the complexity of their businesses. Discerning how price sensitivity varies across the innumerable combinations of products, customers, channels, agreement terms, promotions, and costs is a daunting task. As a result, price segmentation is generally practiced only in its most basic form: one-dimensional revenue discounting—the bigger the order or customer, the bigger the discount.
The breakthrough is that effective price segmentation can quantify similarities and differences in elasticity by empirically determining which deal circumstances affect price response in specific markets. This quantitative understanding of what drives price outcomes makes it possible to cluster historical transactions and to benchmark contemplated future prices against previous deals that were truly similar. Benchmarking (and its more advanced cousin—price optimization) helps decision makers to understand and target the best (most profitable) prices that can likely be achieved for a given set of deal circumstances.
To better illustrate these deal circumstances, in B2B environments, pricing outcomes reflect the combined effect of customer needs, seller motivations, and competitive dynamics around each deal (quote, contract, or purchase agreement). While the exact influence of each of these factors on a deal is difficult to pinpoint, most can be inferred from the associated circumstances. Some examples of deal circumstances that commonly influence pricing outcomes include
* customer attributes—for example, company size, industry, market size, type, wallet share, competition, purchase history and frequency, and geography;
* product attributes—for example, product mix, life cycle stage, degree of commoditization, end-use, category or group, specialization, and previous product; and
* order attributes—for example, level of competition, order size and sales channel, sales representative or agency, product mix, expedite indicator, promotion or rebate, and season.
Each distinct combination of deal attributes determined to affect outcomes defines a price segment. Price segments are used to cluster transactions that share customer, product, and order attributes, and therefore should produce similar price outcomes. Price segments contain the best subset of historical pricing data for benchmarking and targeting prices for new deals with matching circumstances. It is possible to identify and measure different price response patterns with the use of advanced statistical analysis and pricing science.
The first step involves combining transaction history (quotes and orders) with market information (customers, products, and competitive data) to create detailed price observations. In the next step, this data is statistically regressed to determine how price sensitivity varies across price segments as a function of the selected customer, product, and order attributes, such as industry, product mix, and competition. Price management vendor Zilliant aptly explains that "price segmentation uses these attributes to reveal different �micro-markets,' much like a prism reveals the spectrum of colors hidden within white light." The final result is a price segmentation model that more precisely groups and benchmarks each price observation against a well-defined micro-market of highly similar transactions.
From a qualitative angle, price segments are defined by transactional attributes that correlate with more precise price responses to such questions as
* What type of business is the customer in?
* What is the user company's relationship with the customer?
* How sophisticated are the customer's purchasing processes?
* How dynamic is the local market?
* Who does the user company compete with?
* How will the product be used? What is its economic value in this application?
* What substitute products are available?
* Are the products new, maturing, or in decline?
* How large is the order? When is it placed? Will there be future orders?
* What channel is the order coming through?
* What mix of products is on an order?
* Are there special terms? Rebates?
* How urgent is it?
Price segmentation is at the heart of profitable pricing because knowing how to divide and group transactions (past and future) can be used to tune prices to all differences in market price response. The key to price segmentation is in creating pricing segments that group deals (quotes, order, agreements), not just customers, by their circumstances that affect price. The belief in most companies is that price segmentation is the same as marketing segmentation—that is, they pigeonhole customers as, for example, the "consumer" versus "business" or "southeast" versus "northwest."
Another simplistic view of price segmentation that companies often take is based on the frequency of purchases or purchase volume, or on other variables, such as product category or geographic region. The reality is that a one-dimensional segmentation is nearly meaningless, since pricing segments cut across these variables, and the possible permutations are so numerous that nobody could manually analyze and comprehend all the data.
Price analysis (sensing) is about synthesizing and interpreting pricing and margin data to determine how and where to improve profitability. It is the act of gaining an understanding of pricing realities and relative price performance by monitoring customer sensitivity to pricing, and testing customer reaction to price changes. This monitoring and testing is a continuous, never-ending process, and a company must have visibility into every transaction. This means tracking successful and unsuccessful quotes, analyzing counterproposals that were made by customers during negotiations, identifying patterns of price sensitivity, and measuring campaign effectiveness. Such data paints a detailed picture of the marketplace so that a company can see not only what the market will bear at a macro-level, but what customers in each pricing segment will bear as well. For example, high-volume, repeat customers may be less sensitive to small price adjustments than new, low-volume customers.
However, accurately assessing the pricing and margin characteristics of customers, products, and deals is a complex challenge. This is further complicated by a preponderance of special terms, packaging, promotions, off-invoice adjustments, and one-off costs that are common in B2B transactions. Logically, few companies can accurately measure the net prices and margins of products, customers, and channels at a detailed level.
Data-driven price analysis leverages price segmentation to overcome this challenge. It uses the price segments' attributes to normalize and group pricing and margin data (transactional data) for customers, products, and channels, and it produces richer insights into the relative performance of each against peer groups. In addition, data-driven price analysis uses the segmentation attributes to score each customer, product, and channel using key performance indicators (KPIs). KPIs refer to a short list of metrics that a company's managers have identified as being the most important variables that reflect mission success or organizational performance. This detailed view of profitability, outliers, and trends at a granular level provides an actionable view of relative pricing and profitability, and allows pricing decision makers to more quickly and easily identify business areas that are ripe for pricing improvement.
Once a company has an understanding of what is happening in the marketplace, the company has the foundation on which to set prices and discount policies. Prices and policies should be established to achieve specific objectives (that is, to improve margins, grow top-line revenue, or strengthen strategic channel relationships).
Price setting, when rooted in quantitative data and market knowledge, is based on a combination of statistical analysis, mathematical modeling and optimization algorithms, best practices, deal scoring, and KPIs. Like sensing, price setting should be an ongoing, iterative process. As customer buying behavior changes, pricing policies must be reset. This is particularly true for discounting policies in negotiated sales environments.
Deal scoring is a pricing best practice that involves the comparison of transaction business terms in order to score or rank them against a predefined set of criteria. The process of deal scoring can help create an objective set of measures to compare similar or dissimilar transactions to determine value, profitability, or "goodness" against a set of objectives.
Setting refers to the establishment of pricing targets and policies for price lists, contracts, agreements, and spot quotes, but these targets and policies are fine-tuned to the characteristics of individual market segments, contracts, and orders. The idea here is to establish market-segment-, contract-, and deal-specific pricing and policies that support go-to-market objectives. This scientific, market-data-driven approach is in contrast to many companies' reliance on basic price setting heuristics, such as "cost plus," "meet competition," and "across the board discount policies." These legacy, undifferentiated pricing approaches inevitably "leave money on the table" (loss of dollars) in almost every deal because these approaches inadequately reflect the unique value of each price segment.
Compounding the problem, ineffective price planning and analysis obscure the financial impact of pricing on revenue, market share, and margin. As a result, many companies do not effectively align prices with costs, competition, and other market dynamics. In contrast, quantitative data-driven price setting uses scientific demand modeling and price segmentation, as well as optimization to determine the best prices for each micro-market. Price optimization sets prices and policies both within and across price segments to maximize total margin or revenue factoring in high-level pricing strategies and market and customer constraints, and in overall corporate objectives. Furthermore, it is regularly refreshed so that prices stay in line with changing market conditions.
Last but not least, price execution is about enhancing quote and negotiation decisions, and enforcing policies across sales channels to meet revenue and margin targets by helping sales people reach target prices on every deal. Simply knowing the optimal price and margin targets does not guarantee they will be achieved in the market. With limited information, manual processes, and wide discretion, typical price execution activities can leak significant margin through excessive discounting, poor control, and administrative errors.
In contrast, companies that adopt a more quantitative, disciplined approach to price execution can eliminate unnecessary concessions and recapture lost profits without adversely affecting win rates. Through the combined use of quantitative benchmarks and end-to-end automation, data-driven price execution should help sales teams and pricing analysts make more informed pricing decisions, ensure consistent and accurate quoting, and enforce key pricing policies company-wide.
After prices and discount policies are set, companies need to execute and enforce them by implementing a systematic approach that is embedded in the flow of customer dialogue and negotiation. Execute refers to the publishing, quoting, and enforcing of prices and policies across sales channels. Key instruments of price execution include price lists, negotiation guidelines, and exception review and approval policies.
The idea here is to systematically enforce pricing policies to streamline deal reviews and approvals, whereby superior price execution eliminates overdiscounting and administrative errors. In many companies, target prices and margins are set aside when negotiating important deals, and sales discretion then becomes the sole basis for setting discounts and other terms.
Even when centralized price operations have nominal authority over final pricing decisions, the exception-based nature of the process makes this oversight difficult. As a result, price execution is often a large source of margin leakage. Enforcement requires an organizational commitment and a software system that enables users to follow pricing policies in all deals and under all circumstances to ensure optimal pricing for each individual deal. The best pricing policies will be rendered meaningless if maverick salespeople are allowed to offer exaggerated discounts or unchecked incentives.
This does not necessarily mean that there is no room for exceptions, since situations involving special circumstances will always arise. However, with a segmented pricing model, such situations will occur much less often. When they do arise, these situations should not be treated with subjective, one-time decisions. Rather, they should be handled according to well-defined rules and processes consistent with organizational goals and sales workflows. While the art of deal negotiation will always play an important role in any sales transaction, a more quantitative, disciplined approach to deal pricing can eliminate unnecessary concessions and recapture lost profits without adversely affecting win rates.
This is the part three of the series Know Thy Market Segment's Price Response. In the next part of this series, the pros and cons of price segmentation and price optimization will be discussed, along with user recommendations and the types of enterprises that can benefit most from this science-based, statistical approach.
In simple terms, price segmentation leverages advanced pricing science to sift through volumes of historical pricing data and to cull out price response patterns. These variations in price response are correlated with circumstances under which the deals were priced, and reflect market forces such as competition, relative solution value, customer-supplier relationship strength, and price elasticity. Understanding how price response varies across different customer, product, and deal circumstances enables companies to establish differentiated, profit-maximizing pricing policies. In other words, the goal of price segmentation is to increase the consistency of prices for deals sharing similar circumstances, while maximizing price differentiation where different deal circumstances permit.
According to Wikipedia, in economics, elasticity is the ratio of the incremental percentage change in one variable with respect to an incremental percentage change in another variable, and is usually expressed as a negative number, but shown as a positive percent value. One typical application of the concept of elasticity is to consider what happens to consumer demand for a good (a product or service) when the price of that good increases. As the price of a good rises, consumers will usually demand lower quantities of that good—perhaps by consuming less of the good, substituting it with other goods, and so forth. The greater the extent to which demand falls as price rises, the greater is the price elasticity of demand. However, there may be some goods that consumers either require, cannot consume less of, or cannot find substitutes for, even if prices rise (for example, certain prescription drugs). For such goods, the price elasticity of demand is considered inelastic.
The concept of elasticity has a wide range of applications in economics. In particular, an understanding of elasticity is useful in understanding the dynamic response of supply and demand in a market in order to either achieve an intended result, or to avoid an unintended result. For example, a business considering a price increase of its product might find that doing so lowers profits if demand is highly elastic, as sales would fall sharply. Similarly, a business considering a price cut to its product might find that it does not increase sales if demand for the product is price-inelastic.
While price elasticity explains differences in price response, determining it and applying it to transactional pricing, especially in B2B enterprises, is extremely difficult. Some price management vendors have developed techniques and technologies that leverage price elasticity in ways that can be used in real-world applications to differentiate pricing based on subsegments related to all the factors driving elasticity. Transactional price segmentation reveals differences in price response that can lead to increased profits when dealing with more amenable-to-pay or needy customers (while not necessarily losing profit on the less willing-to-pay customers).
Thus, price segmentation enhances the practice of charging different prices in different segments of the market. It is an approach already employed in B2B markets (albeit in a rudimentary way), with the underlying economic phenomenon being that customers attach different values to goods and services under different market conditions and transactional circumstances.
The central premise of price segmentation is quite intuitive (although the calculations behind it are of the "rocket science" sort). The premise is this: pricing should be consistent for similar deals as opposed to being based only on the more general, one-dimensional elasticity factors like volume or product category. Aligning prices to precise segments is fundamental to maximizing margins, but most B2B companies take little advantage of price segmentation because of the complexity of their businesses. Discerning how price sensitivity varies across the innumerable combinations of products, customers, channels, agreement terms, promotions, and costs is a daunting task. As a result, price segmentation is generally practiced only in its most basic form: one-dimensional revenue discounting—the bigger the order or customer, the bigger the discount.
The breakthrough is that effective price segmentation can quantify similarities and differences in elasticity by empirically determining which deal circumstances affect price response in specific markets. This quantitative understanding of what drives price outcomes makes it possible to cluster historical transactions and to benchmark contemplated future prices against previous deals that were truly similar. Benchmarking (and its more advanced cousin—price optimization) helps decision makers to understand and target the best (most profitable) prices that can likely be achieved for a given set of deal circumstances.
To better illustrate these deal circumstances, in B2B environments, pricing outcomes reflect the combined effect of customer needs, seller motivations, and competitive dynamics around each deal (quote, contract, or purchase agreement). While the exact influence of each of these factors on a deal is difficult to pinpoint, most can be inferred from the associated circumstances. Some examples of deal circumstances that commonly influence pricing outcomes include
* customer attributes—for example, company size, industry, market size, type, wallet share, competition, purchase history and frequency, and geography;
* product attributes—for example, product mix, life cycle stage, degree of commoditization, end-use, category or group, specialization, and previous product; and
* order attributes—for example, level of competition, order size and sales channel, sales representative or agency, product mix, expedite indicator, promotion or rebate, and season.
Each distinct combination of deal attributes determined to affect outcomes defines a price segment. Price segments are used to cluster transactions that share customer, product, and order attributes, and therefore should produce similar price outcomes. Price segments contain the best subset of historical pricing data for benchmarking and targeting prices for new deals with matching circumstances. It is possible to identify and measure different price response patterns with the use of advanced statistical analysis and pricing science.
The first step involves combining transaction history (quotes and orders) with market information (customers, products, and competitive data) to create detailed price observations. In the next step, this data is statistically regressed to determine how price sensitivity varies across price segments as a function of the selected customer, product, and order attributes, such as industry, product mix, and competition. Price management vendor Zilliant aptly explains that "price segmentation uses these attributes to reveal different �micro-markets,' much like a prism reveals the spectrum of colors hidden within white light." The final result is a price segmentation model that more precisely groups and benchmarks each price observation against a well-defined micro-market of highly similar transactions.
From a qualitative angle, price segments are defined by transactional attributes that correlate with more precise price responses to such questions as
* What type of business is the customer in?
* What is the user company's relationship with the customer?
* How sophisticated are the customer's purchasing processes?
* How dynamic is the local market?
* Who does the user company compete with?
* How will the product be used? What is its economic value in this application?
* What substitute products are available?
* Are the products new, maturing, or in decline?
* How large is the order? When is it placed? Will there be future orders?
* What channel is the order coming through?
* What mix of products is on an order?
* Are there special terms? Rebates?
* How urgent is it?
Price segmentation is at the heart of profitable pricing because knowing how to divide and group transactions (past and future) can be used to tune prices to all differences in market price response. The key to price segmentation is in creating pricing segments that group deals (quotes, order, agreements), not just customers, by their circumstances that affect price. The belief in most companies is that price segmentation is the same as marketing segmentation—that is, they pigeonhole customers as, for example, the "consumer" versus "business" or "southeast" versus "northwest."
Another simplistic view of price segmentation that companies often take is based on the frequency of purchases or purchase volume, or on other variables, such as product category or geographic region. The reality is that a one-dimensional segmentation is nearly meaningless, since pricing segments cut across these variables, and the possible permutations are so numerous that nobody could manually analyze and comprehend all the data.
Price analysis (sensing) is about synthesizing and interpreting pricing and margin data to determine how and where to improve profitability. It is the act of gaining an understanding of pricing realities and relative price performance by monitoring customer sensitivity to pricing, and testing customer reaction to price changes. This monitoring and testing is a continuous, never-ending process, and a company must have visibility into every transaction. This means tracking successful and unsuccessful quotes, analyzing counterproposals that were made by customers during negotiations, identifying patterns of price sensitivity, and measuring campaign effectiveness. Such data paints a detailed picture of the marketplace so that a company can see not only what the market will bear at a macro-level, but what customers in each pricing segment will bear as well. For example, high-volume, repeat customers may be less sensitive to small price adjustments than new, low-volume customers.
However, accurately assessing the pricing and margin characteristics of customers, products, and deals is a complex challenge. This is further complicated by a preponderance of special terms, packaging, promotions, off-invoice adjustments, and one-off costs that are common in B2B transactions. Logically, few companies can accurately measure the net prices and margins of products, customers, and channels at a detailed level.
Data-driven price analysis leverages price segmentation to overcome this challenge. It uses the price segments' attributes to normalize and group pricing and margin data (transactional data) for customers, products, and channels, and it produces richer insights into the relative performance of each against peer groups. In addition, data-driven price analysis uses the segmentation attributes to score each customer, product, and channel using key performance indicators (KPIs). KPIs refer to a short list of metrics that a company's managers have identified as being the most important variables that reflect mission success or organizational performance. This detailed view of profitability, outliers, and trends at a granular level provides an actionable view of relative pricing and profitability, and allows pricing decision makers to more quickly and easily identify business areas that are ripe for pricing improvement.
Once a company has an understanding of what is happening in the marketplace, the company has the foundation on which to set prices and discount policies. Prices and policies should be established to achieve specific objectives (that is, to improve margins, grow top-line revenue, or strengthen strategic channel relationships).
Price setting, when rooted in quantitative data and market knowledge, is based on a combination of statistical analysis, mathematical modeling and optimization algorithms, best practices, deal scoring, and KPIs. Like sensing, price setting should be an ongoing, iterative process. As customer buying behavior changes, pricing policies must be reset. This is particularly true for discounting policies in negotiated sales environments.
Deal scoring is a pricing best practice that involves the comparison of transaction business terms in order to score or rank them against a predefined set of criteria. The process of deal scoring can help create an objective set of measures to compare similar or dissimilar transactions to determine value, profitability, or "goodness" against a set of objectives.
Setting refers to the establishment of pricing targets and policies for price lists, contracts, agreements, and spot quotes, but these targets and policies are fine-tuned to the characteristics of individual market segments, contracts, and orders. The idea here is to establish market-segment-, contract-, and deal-specific pricing and policies that support go-to-market objectives. This scientific, market-data-driven approach is in contrast to many companies' reliance on basic price setting heuristics, such as "cost plus," "meet competition," and "across the board discount policies." These legacy, undifferentiated pricing approaches inevitably "leave money on the table" (loss of dollars) in almost every deal because these approaches inadequately reflect the unique value of each price segment.
Compounding the problem, ineffective price planning and analysis obscure the financial impact of pricing on revenue, market share, and margin. As a result, many companies do not effectively align prices with costs, competition, and other market dynamics. In contrast, quantitative data-driven price setting uses scientific demand modeling and price segmentation, as well as optimization to determine the best prices for each micro-market. Price optimization sets prices and policies both within and across price segments to maximize total margin or revenue factoring in high-level pricing strategies and market and customer constraints, and in overall corporate objectives. Furthermore, it is regularly refreshed so that prices stay in line with changing market conditions.
Last but not least, price execution is about enhancing quote and negotiation decisions, and enforcing policies across sales channels to meet revenue and margin targets by helping sales people reach target prices on every deal. Simply knowing the optimal price and margin targets does not guarantee they will be achieved in the market. With limited information, manual processes, and wide discretion, typical price execution activities can leak significant margin through excessive discounting, poor control, and administrative errors.
In contrast, companies that adopt a more quantitative, disciplined approach to price execution can eliminate unnecessary concessions and recapture lost profits without adversely affecting win rates. Through the combined use of quantitative benchmarks and end-to-end automation, data-driven price execution should help sales teams and pricing analysts make more informed pricing decisions, ensure consistent and accurate quoting, and enforce key pricing policies company-wide.
After prices and discount policies are set, companies need to execute and enforce them by implementing a systematic approach that is embedded in the flow of customer dialogue and negotiation. Execute refers to the publishing, quoting, and enforcing of prices and policies across sales channels. Key instruments of price execution include price lists, negotiation guidelines, and exception review and approval policies.
The idea here is to systematically enforce pricing policies to streamline deal reviews and approvals, whereby superior price execution eliminates overdiscounting and administrative errors. In many companies, target prices and margins are set aside when negotiating important deals, and sales discretion then becomes the sole basis for setting discounts and other terms.
Even when centralized price operations have nominal authority over final pricing decisions, the exception-based nature of the process makes this oversight difficult. As a result, price execution is often a large source of margin leakage. Enforcement requires an organizational commitment and a software system that enables users to follow pricing policies in all deals and under all circumstances to ensure optimal pricing for each individual deal. The best pricing policies will be rendered meaningless if maverick salespeople are allowed to offer exaggerated discounts or unchecked incentives.
This does not necessarily mean that there is no room for exceptions, since situations involving special circumstances will always arise. However, with a segmented pricing model, such situations will occur much less often. When they do arise, these situations should not be treated with subjective, one-time decisions. Rather, they should be handled according to well-defined rules and processes consistent with organizational goals and sales workflows. While the art of deal negotiation will always play an important role in any sales transaction, a more quantitative, disciplined approach to deal pricing can eliminate unnecessary concessions and recapture lost profits without adversely affecting win rates.
This is the part three of the series Know Thy Market Segment's Price Response. In the next part of this series, the pros and cons of price segmentation and price optimization will be discussed, along with user recommendations and the types of enterprises that can benefit most from this science-based, statistical approach.
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