Patent Application
20080126264
The present invention relates to price optimization systems. More particularly, the present invention relates to systems and methods of generating optimized prices using business segments. Optimized prices and price guidance are generated for each selected segment. A deal envelope is generated and used to guide price selection according to rules based on business policy parameters and overall business objectives. Business policy is used to determine business rules which guide the optimization.
Many businesses rely upon careful pricing in order to stay competitive and still realize a profit. Successful price setting may be the difference between a company's solvency and demise. Through proper pricing, market dominance may be obtained and held, even in very competitive markets.
There are major challenges in business to business (hereinafter “B2B”) markets which hinder the effectiveness of classical approaches to price optimization.
For instance, in B2B markets, a small number of customers represent the lion's share of the business. Managing the prices of these key customers is where most of the pricing opportunity lies. Also, B2B markets are renowned for being data-poor environments. Availability of large sets of accurate and complete historical sales data is scarce.
Furthermore, B2B markets are characterized by deal negotiations instead of non-negotiated sale prices (prevalent in business to consumer markets). There is no existing literature on optimization of negotiation terms and processes, neither at the product/segment level nor at the customer level.
Finally, B2B environments suffer from poor customer segmentation. Top-down price segmentation approaches are rarely the answer. Historical sales usually exhibit minor price changes for each customer. Furthermore, price bands within customer segments are often too large and customer behavior within each segment is non-homogeneous.
Product or segment price optimization relies heavily on the quality of the customer segmentation and the availability of accurate and complete sales data. In this context, price optimization makes sense only (i) when price behavior within each customer segment is homogeneous and (ii) in the presence of data-rich environments where companies' sales data and their competitors' prices are readily available. These conditions are met almost exclusively in business to consumer (hereinafter “B2C”) markets such as retail, and are rarely encountered in B2B markets.
On the other hand, customer price optimization relies heavily on the abundance of data regarding customers' past behavior and experience, including win/loss data and customer price sensitivity. Financial institutions have successfully applied customer price optimization in attributing and setting interest rates for credit lines, mortgages and credit cards. Here again, the aforementioned condition is met almost exclusively in B2C markets.
There are three major types of price optimization solutions in the B2B marketplace: revenue/yield management, price testing and highly customized optimization solutions.
Revenue/yield management approaches were initially developed in the airline context, and were later expanded to other applications such as hotel revenue management, car rentals, cruises and some telecom applications (e.g., bandwidth pricing). These approaches are exclusively concerned with perishable products (e.g., airline seats) and are not pricing optimization approaches per se.
Price testing approaches attempt to learn and model customer behavior dynamically by measuring customer reaction to price changes. While this approach has been applied rather successfully in B2C markets, where the benefits of price optimization outweigh the loss of a few customers, its application to B2B markets is questionable. No meaningful customer behavior can be modeled without sizable changes in customer prices (both price increases and decreases). In B2B markets, where a small fraction of customers represent a substantial fraction of the overall business, these sizable price-changing tests can have adverse impact on business. High prices can drive large customers away with potentially a significant loss of volume. Low prices on the other hand, even for short periods of time, can dramatically impact customer behavior, increase customers' price sensitivities and trigger a more strategic approach to purchasing from the customers' side.
Finally, in B2B markets, highly customized price optimization solutions have been proposed. These solutions have had mixed results. These highly customized price optimization solutions require significant consulting effort in order to address companies' unique situations including cost structure, customer and competitor behavior, and to develop optimization methods that are tailored to the type of pricing data that is available. Most of the suggested price changes from these solutions are not implemented. Even when they are implemented, these price changes tend not to stick. Furthermore, the maintenance of such pricing solutions usually requires a lot of effort. This effort includes substantial and expensive on-going consulting engagements with the pricing companies.
Traditionally, teams of marketing specialists, or the truly gifted businessperson, were needed to devise successful pricing schemes. Often such pricing suggestions were not competitive and too costly to generate.
With the advent of computers, automated pricing became a reality. However, such pricing schemes often did not have the desired level of utility, intuitiveness, and functionality as to be of any great improvement over more traditional methods of price setting. These solutions have failed primarily because of the lack of reliable price control and management systems. In fact, in B2B markets, reliable price control and management systems may be significantly more complex and more important than price optimization modules.
For the typical business, the above systems are still too inaccurate, unreliable, costly and intractable in order to be utilized effectively for price setting. Businesses, particularly those involving large product sets, would benefit greatly from the ability to have accurate and efficient price setting tools available that allows for accurate business segmentation.
Furthermore, instead of developing highly customized company-specific price optimization solutions, there remains a need for scalable and customizable price optimization solutions that vary by industry vertical.
In particular, in the context of business to business markets, effective price modeling and optimization schemes have been elusive given the scarcity of sales data and the relatively small pool of available customers. In this environment, it is important to include all available relevant data, including competitive behavior data, in order to develop robust price modeling and optimization schemes. It is also important to continuously loop back to update and calibrate the price modeling and optimization schemes with new sales data generated from deals consummated with the benefit of the instant price modeling and optimization schemes.
It is therefore apparent that an urgent need exists for an effective price control and management systems which provides for parameterization, calculation and deployment of optimized target prices and price guidance through analysis of risks and pricing power of business segments to calculate optimized target prices and price guidance, thereby enabling effective price modeling and optimization in the context of business to business markets.
The present invention provides systems and methods of generating optimized prices using business segments. Optimized prices and price guidance are generated for each selected segment. Such a system is useful for business to business markets.
One advantage of the present invention is that a user may work without building or tuning custom models. The present invention enables a clear optimization process which delivers an optimization process that is transparent to the business user.
The optimization of product prices using business segmentation is useful in association with products. The business is segmented into a plurality of selected segments. Each segment includes a subset of products. Segmenting utilizes fixed dimensions and variable dimensions. Fixed dimensions include geography, sales region, market group, customer size, customer type, industry, and deal type. Variable dimensions include customer class, product class, and deal class. Product class includes measures and levels. Measures includes volume, revenue, profit, margin, net price, purchase frequency, discount rates, compliance rates and customer behavior, and levels include quality and status.
Pricing power is computed for each segment. The pricing power is an ability to alter pricing of the products within the segment. Pricing power includes analyzing price variance, win rates, price yields and competitor pricing.
Likewise, pricing risk is computed for each segment. The pricing risk is a risk factor associated with an alteration to pricing of the products within the segment. Pricing risk includes analyzing sales revenue, sales trend, price distribution and customer spend.
Pricing objectives are generated for each segment by comparing the pricing power to the pricing risk of the segment. This includes performing a matrix analysis of pricing power and pricing risk.
Prices are optimized using the pricing objectives. Prices are set based on optimized prices. Price lists and policies may be managed, including negotiating of prices based on optimized prices. Additionally, the entire system may be linked to an enterprise resource system.
These and other features of the present invention may be practiced alone or in any reasonable combination and will be discussed in more detail below in the detailed description of the invention and in conjunction with the following figures.
In order that the present invention may be more clearly ascertained, one embodiment will now be described, by way of example, with reference to the accompanying drawings, in which:
The present invention will now be described in detail with reference to several embodiments thereof as illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without some or all of these specific details. In other instances, well known process steps and/or structures have not been described in detail in order to not unnecessarily obscure the present invention. The features and advantages of the present invention may be better understood with reference to the drawings and discussions that follow.
The present invention provides systems and methods for pricing processes including relating segmentation, pricing power, pricing risk, and pricing objectives to the calculation of optimized price guidance and deployment of guidance. Also disclosed is a novel method for the calculation of pricing power and risk for each segment; application of pricing objective to each segment; calculation of optimized price and deal envelope per segment; and deployment of optimized prices in the pricing process.
“Pricing Power”, or “Power”, indicates a business' ability to change prices. It is calculated using a combination of measures, including price variance (how much prices vary in a segment), price yield (invoice price as a percent of list price), percent of approval escalations and win ratios. In some embodiments, typical values for Price Power are high, med, and low. Of course, in some alternate embodiments, other scales for measuring Pricing Power may be utilized, such as a continuous graduated scale.
“Pricing Risk”, “Risk”, or “Risk Power”, indicates the business risk of changing prices. It may be calculated using another set of measures, including total sales (revenue), change in revenue (or quantity) and the price distribution (shape of the price band curve). In some embodiments, typical values for Pricing Risk are high, med, and low. Of course, like for Pricing Power, in some alternate embodiments other scales for measuring Pricing Risk may be utilized, such as a continuous graduated scale.
“Pricing Objective” may be used to guide the negotiated price in a business segment. In some embodiments, pricing objectives may be assigned to different combinations of pricing power and risk.
In some embodiments, pricing objectives are defined using percentile values, which is a simple yet powerful way to set consistent targets in a segment with varying prices. For example, a zero percentile may refer to the minimum price, 100 percentile may refer to the maximum price and 50 percentile may refer to the median price. A green line may be defined as the accumulative set of price points from zero to 100%.
Deal guidance contains pricing objective prices, which include target price, approval price(s) and floor prices for each product in a segment. It is calculated using the segments historical prices and the assigned pricing objective. In some embodiments, each pricing objective price (target, approval, and floor) may be defined as a percentile and when applied to a data set can be used to calculate price points. An optimizer calculates the optimal deal guidance prices for each segment using the calculated pricing power, risk and objective.
In some embodiments, the optimizer may output a list of target prices, approval prices and floor prices—one for each segment (and product.)
One value of the present optimizing solution is the ability to apply different objectives to each business segment to manipulate product demand curves in different ways by applying target, approval and floor prices at different levels.
A deal manager may guide sales representatives, using a number of analysis tools, to negotiate optimal prices.
Additionally, the system may calculate a score for each line item based on the deal guidance and calculated a weighted deal score. Either line score or deal score can be used for approval routing.
To facilitate discussion,
The User 120 may be a corporate officer, statistician, manager or other business planner. Alternatively, in some embodiments, User 120 may be an independent third party, such as a business planning consultant.
User 120 and Pricing System 110 may be located in a single location. Alternatively, in some embodiments, the Pricing System 110 may be accessed remotely by the User 120. Moreover, in some embodiments, the Pricing System 110 may be a diffuse system, capable of having components in various locations as required.
The Pricing System 110 may include an Interface 111, a Performance Tracker 112, a Segment Selector 113, an Optimizer 114, a Price Setter 115, a Price and Policy Manager 116, a Network Connector 117 and a Negotiator 118, each coupled to a Local Area Network 119. Of course this list of possible components is not exhaustive, and it is in the spirit of this application that additional or fewer components may be included as is desired for system functionality.
The Local Area Network 119 may provide interconnectivity between the components of Pricing System 110. In cases when the Pricing System 110 is located within a single unit, Local Area Network 119 may be a logical component. However, in some embodiments, when the components of the Pricing System 110 are diffusely located, Local Area Network 119 may include a corporate or other LAN, or WAN.
The Interface 111 may enable connectivity between the Pricing System 110 and the User 120. In some embodiments, the Interface 111 may enable the User 120 to configure the Pricing System 110, and view the output of the Pricing System 110.
The Performance Tracker 112 may track performance of the price setting and negotiated deals. Performance Tracker 112 may then provide feedback to the User 120. Additionally, the Performance Tracker 112 may, in some embodiments, provide feedback for fine tuning future pricing optimizations.
The Segment Selector 113 may define business segments. Such selection of business segments may include analyzing pricing risks and pricing powers. Business segments may include logical collections of products. Segment Selector 113 may provide the selected business segments to the Optimizer 114 for optimization. In some embodiments, the business segments may be dynamic, with products shifting from one business segment to another as is needed. For a typical business, 100s to 1000s of business segments may be identified. Of course, the system may function with any number of business segments.
In some embodiments, the Segment Selector 113 may additionally generate business objectives for each business segment. Segment Selector 113 may provide the selected business objectives to the Optimizer 114 for guiding optimization.
The Optimizer 114 may generate optimized pricing for the products within the business segment, relying upon the pricing objectives supplied by the Segment Selector 113. Such optimization may be performed utilizing statistical analysis, rule based approaches, Nash equilibrium, or any other suitable optimization method.
The Price Setter 115 may receive the optimization data from the Optimizer 114. The prices may then be set by the Price Setter 115. The Price Setter 115 may also deploy the set prices.
The Price and Policy Manager 116 may provide management of the products prices and deal negotiations. In some embodiments, the Price and Policy Manager 116 may be configured by the User 120.
The Network Connector 117 enables the Pricing System 110 to be coupled to the WAN 140. In some embodiments, Network Connector 117 may be a hardwire jack or a wireless enabled device.
The Negotiator 118 may provide guidelines and restrictions regarding deal negotiation to sales representatives based upon the prices and policies of the Price Setter 115 and Price and Policy Manager 116.
The Segment Selector 113 may receive Business Data 210. The Business Data 210 may be data from the User 120, the Price and Policy Manager 116, industry data, historic sales data and business data. The Business Data 210 may include information regarding the products and customers of the business. Business Data 210 may also include fixed dimensions and dynamic dimensions.
Business Data 210 may be received by a Segmentor 202. The Segmentor 202 may designate business segment and divide the products and customers into the business segment. Segmentor 202 may select business segment by utilizing the fixed dimensions and dynamic dimensions. Segmentor 202 may be coupled to the Pricing Power Engine 204 and the Pricing Risk Engine 206.
Each business segment may then be analyzed for pricing power and pricing risk by the Pricing Power Engine 204 and the Pricing Risk Engine 206, respectively. The Pricing Power Engine 204 and the Pricing Risk Engine 206 are each coupled to the Segmentor 202 and Pricing Objective Engine 208.
Results from the Pricing Power Engine 204 and Pricing Risk Engine 206 are received by the Pricing Objective Engine 208. The Pricing Objective Engine 208 may utilize the pricing power score and the pricing risk score for a given business segment to generate pricing objectives for the business segment. The business segment and pricing objectives are then output as the Business Segment with Pricing Objective Data 220.
The Fixed Dimension Selector 310 takes into account fixed dimensions in the generation of the business segments. The Fixed Dimension Selector 310 may include a Geography Module 311, a Sales Region Module 312, a Market Group Module 313, a Customer Size Module 314, a Customer Type Module 315, an Industry Module 316 and a Deal Type Module 317. Of course, additional or fewer modules may be included in the Fixed Dimension Selector 310 as is desired.
The Geography Module 311 may separate business segment by geography. The Sales Region Module 312 may separate business segment by sales regions. The Market Group Module 313 may separate business segment by market groups. The Customer Size Module 314 may separate business segment by customer size. The Customer Type Module 315 may separate business segment by customer type. The Industry Module 316 may separate business segment by industry type. The Deal Type Module 317 may separate business segment by deal type.
The Variable Dimension Selector 320 takes into account variable dimensions in the generation of the business segments. The Variable Dimension Selector 320 may include a Customer Class Module 321, a Deal Class Module 322, and a Product Class Module 323. Moreover, the Product Class Module 323 may include a Measures Module 324 and a Levels Module 325. Of course, additional or fewer modules may be included in the Variable Dimension Selector 320 as is desired.
The Customer Class Module 321 may separate business segment by customer class. The Deal Class Module 322 may separate business segment by deal class. The Product Class Module 323 may separate business segment by product class. In determining product class, the Measures Module 324 may separate business segment by product measures, and Levels Module 325 may separate business segment by product levels.
Product measures may include volume, revenue, profit, margin, net price, purchase frequency, discount rates, compliance rates and customer behavior to the product. Product levels may include quality and status levels. Of course, additional indices of product measure and level may be included as is desired.
The Pricing Power Balancer 410 may receive input for the modules to generate a pricing power score. Said score may be generated on a continuous gradient. For example pricing power may be provided as any real number within a range. Alternatively, in some embodiments, pricing power score may be a more simple scale, such as “high”, “medium” or “low”.
The Price Variance Module 402 calculates the extent of the ability for a product price to diverge. The Win Rate Module 404 calculates the extent of the product win ratio. Win ratio may also be referred to as win probability, or win/loss. Win ratio indicates the probability of success of a deal under particular conditions. Win ratios may be represented as a curve of expected deal success probability as a function of price, promotion or other index. The Approval Escalations Module 406 calculates product approval escalations impact upon pricing power. The Price Yield Module 408 calculates product price yield impact upon pricing power. The Competitive Module 412 calculates the impact competition has upon pricing power. The Additional Power Module 414 provides for the consideration of any additional module that would assist in generating an accurate pricing power score.
Historic data and industry standard data may be utilized by the Price Variance Module 402, the Win Rate Module 404, the Approval Escalations Module 406 the Price Yield Module 408, the Competitive Module 412 and the Additional Power Module 414 in order to generate accurate indices of pricing power for the Pricing Power Balancer 410 to balance into a cohesive pricing power score.
The Pricing Risk Balancer 510 may receive input for the modules to generate a pricing risk score. Said score may be generated on a continuous gradient. For example, pricing risk may be provided as any real number within a range. Alternatively, in some embodiments, pricing risk score may be a more simple scale, such as “high”, “medium” or “low”.
The Sales Revenue Module 502 calculates the sales revenue for a product. The Sales Trend Module 504 calculates the sales trend of the product. The Price Distribution Module 506 calculates product price distribution. The Customer Spend Module 508 calculates percent of total spend by the customer. The Additional Risk Module 512 provides for the consideration of any additional module that would assist in generating an accurate pricing risk score.
Historic data and industry standard data may be utilized by the Sales Revenue Module 502, the Sales Trend Module 504, the Price Distribution Module 506, the Customer Spend Module 508 and the Additional Risk Module 512 in order to generate accurate indices of pricing risk for the Pricing Risk Balancer 510 to balance into a cohesive risk score.
Between step 1110 and 1112 additional pricing power data may be received from the Additional Power Module 414. An example of such data may include purchase frequency data of a customer.
The process then proceeds to step 1112 where pricing power for the business segment is computed by the Pricing Power Balancer 410 by balancing the received pricing power data. The Pricing Power Balancer 410 may generate a “score” or other indicia of the level of pricing power the given business segment has. As previously discussed, said score may be generated on a continuous gradient, or may be a more simple scale, such as “high”, “medium” or “low”. The process then concludes by progressing to step 608 of
Between step 1208 and 1210 additional pricing risk data may be received from the Additional Risk Module 512. The process then proceeds to step 1210 where pricing risk for the business segment is computed by the Pricing Risk Balancer 510 by balancing the received pricing risk data. The Pricing Risk Balancer 510 may generate a “score” or other indicia of the level of pricing risk the given business segment has. As previously discussed, said score may be generated on a continuous gradient, or may be a more simple scale, such as “high”, “medium” or “low”. The process then concludes by progressing to step 610 of
The process then proceeds to step 1304 where pricing risk data for the business segment is received from the Pricing Risk Engine 206. Like pricing power, received pricing risk data may be in the form of a calculated pricing risk score.
Lastly, at step 1306, pricing objective may be generated for the given business segment. Pricing objectives may, in some embodiments, be generated by comparing the pricing power score to the pricing risk score on a matrix. The intersection of any given power score to a risk score may then correspond to a particular pricing objective that is optimal for the given business segment.
In the case of continuous pricing power and risk scores, the Pricing Objective Engine 208 may utilize fuzzy logic in order to generate a pricing objective.
The process then concludes by progressing to step 612 of
Historical sales data is used by the demand modeling step 1404 to model demand for a selected product or segment. The demand modeling step 1404 is followed by the price optimization step 1406. The optimization step 1406 uses the demand models provided in generating a set of preferred prices for the selected product or business segment. The optimization step 1406 is followed by the deal negotiation step 1408, where the preferred prices may be used by a sales force in negotiating deals with customers.
A learning and calibration process follows the completion of the deal negotiations. The resulting deals, (i.e., quoted prices with customers) may be provided back as deal history data for iterative optimization. The learning and calibration process is carried out in steps 1410 and 1412. Information from the negotiated deals may be used in the learning and calibration process to update and calibrate the demand modeling and price optimization processes.
Of course there are many ways of modeling demand functions, and it is intended that the present invention is flexible enough as to be able to utilize a variety of demand modeling methodologies as it becomes favorable to do so.
Deals are classified as wins or losses based upon a comparison between deal transactions (quotes and/or contracts) and order transactions. The matching logic compares things like deal effective date (from and to date), specific product or product group, customer account, and ship-to or billed-to. Deal win/loss classification data may be output at step 1706. The process then concludes by progressing to step 1606 of
In some embodiments, demand for a particular product/segment is estimated using the cleansed datasets discussed above to generate a price elasticity demand model and a win probability model. A demand model is selected which fits well statistically with the historical data. For example, any of the commonly used, externally derived, multivariate, parametric, non-separable algorithms may be used to create the price elasticity and win probability models. The model which best fits the historical data may be used.
The price optimization may be performed using the optimized business segment scheme discussed above. In order to decide which algorithm to use or give the best fit, the optimization may run all of them and selects the best algorithm, i.e. the one that has the highest statistical significance vis-à-vis the cleansed data set. All of the algorithms provided by the User 120 may be included to find the best fit given the actual data. The User 120 may use any of the commonly used algorithms discussed above and/or the User 120 may provide preferred models based on the particular dataset in question.
Output from the demand model to the optimization model may be a set of price elasticity curves and optionally a set of win probability curves. One embodiment of the instant optimization model selects the demand model which best fits the cleansed data as discussed above. Game theory may be used to model competitive behavior based on historical data. One embodiment of the instant optimization combines game theory with dynamic non-linear optimization to give optimized prices. The optimization may be performed subject to optimization goals and constraints provided by Price and Policy Manager 116. For instance, the goal may be to optimize pocket margin given a limited change in product volume or product price.
In some embodiments, it may also be important to provide optimization goals and constraints in any optimization scheme. The User 120 may decide to optimize for profit, sales or volume maximization. Once the optimization goal is selected, optimization constraints may be set. The User 120 may set the constraints in conformance with the particular business objectives as discussed above.
The User 120 may choose to constrain the following factors: maximum price increase, maximum price decrease for a business segment (e.g., Product Yearly Revenue Segment A) or intersection of business segments (e.g., Product Yearly Revenue Segment A and Biotech Industry Customers).
Optimization goals and constraints are provided at step 1904. Competitive behavior data along with selected optimization goals and constraints are used to optimize prices at step 1906. Previously generated and optimized pricing guidance is provided at step 1908. The optimized prices are reconciled with the optimized pricing guidance at step 1910. The process then concludes by progressing to step 1408 of
The resulting optimized, reconciled prices may be used in deal negotiations. The resulting deals, (i.e., quoted prices with customers) may be provided back as deal history data for iterative optimization. This continuous learning and calibration is done in order to fine tune the instant optimization process with real world data reflecting the actual results of incorporating the optimized prices into the deal negotiation process.
Next, at step 2104, a dynamic, non-linear optimization may be conducted using an iterative relaxation algorithm. The Nash equilibrium computation may be combined with the selected non-linear optimization model to give optimized prices subject to optimization goals and constraints. Optimized prices are output at step 2106. The process then concludes by progressing to step 2010 of
Pricing Power Header 2220 is shown. High Power Score 2222, Medium Power Score 2224 and Low Power Score 2226 are located under the Pricing Power Header 2220. High Power Score 2222 would indicate that the business segment has the ability to be priced aggressively. Medium Power Score 2224 indicates pricing of a business segment may be subject to some pricing changes. Low Power Score 2226, on the other hand, indicates that a business segment is capable of little pricing changes.
Likewise, Pricing Risk Header 2230 may be seen with High Risk Score 2232, Medium Risk Score 2234, and Low Risk Score 2236. High Risk Score 2232 would indicate that the business segment would be subject to a great amount of risk when there are pricing changes. Medium Risk Score 2234 indicates pricing of a business segment would be subject to some amount of risk. Low Power Score 2226, on the other hand, indicates that a business segment would be subject to a small amount of risk when there are pricing changes.
By comparing the level of power of a business segment to its pricing risk the pricing objectives may be determined. Pricing Objective 2210 may be seen. When the business segment has a High Power Score 2222 and a Low Risk Score 2236, the pricing objectives may include Aggressive Increase of Pricing 2212. Aggressive Increase of Pricing 2212 may include increasing all levels of the business segment substantially in order to capitalize on the business' strong pricing situation.
Likewise, when the business segment has a High Power Score 2222 and a Medium Risk Score 2234, the pricing objectives may include Moderate Increase of Pricing 2213. Moderate Increase of Pricing 2213 may include increasing all levels of the business segment moderately in order to capitalize on the business' moderate pricing situation.
Also, when the business segment has a High Power Score 2222 and a High Risk Score 2232, the pricing objectives may include Tighten Pricing Threshold 2214. Tighten Pricing Threshold 2214 may include narrowing the gap between target and floor levels. When the business segment has a Medium Power Score 2224 and either a Medium Risk Score 2234 or High Risk Score 2232, the pricing objectives may include Increase in Pricing Scrutiny 2215. Increase in Pricing Scrutiny 2215 may include the increase of approval levels. Contrary, when the business segment has a Medium Power Score 2224 or Low Power Score 2226 and a Low Risk Score 2236, the pricing objectives may include Increase in Pricing Autonomy 2217. Increase in Pricing Autonomy 2217 may include the reduction of approval levels. Lastly, when the business segment has a Low Power Score 2226 and either a Medium Risk Score 2234 or High Risk Score 2232, the pricing objectives may include Maintain Pricing 2216.
In the embodiments that include continuous scores for pricing power and pricing risk, such the pricing objectives selection will be less strictly defined. In these embodiments, a graduated set of pricing objectives may be more appropriate. Alternatively, fuzzy logic principles may be utilized in order to generate the appropriate pricing objectives.
Increase Scrutiny 2532 indicates when the approval levels are increased. Increase Scrutiny 2532 typically occurs when the business segment has a medium level of pricing power but also is subject to medium to high pricing risk.
Increase Autonomy 2534 indicates when the approval levels are reduced. Increase Autonomy 2534 typically occurs when the business segment has a low to medium level of pricing power but only has a low pricing risk.
Aggressive Increase 2536 indicates when the pricing is aggressive. Aggressive Increase 2536 typically occurs when the business segment has a high level of pricing power and a low pricing risk. Likewise, Moderate Increase 2538 indicates when the pricing is moderately increased. Moderate Increase 2538 typically occurs when the business segment has a high level of pricing power and a medium pricing risk.
Tighten Thresholds 2540 indicates when the pricing thresholds are tightened. Tighten Thresholds 2540 typically occurs when the business segment has a high level of pricing power and a high pricing risk.
Processor 2722 is also coupled to a variety of input/output devices, such as Display 2704, Keyboard 2710, Mouse 2712 and Speakers 2730. In general, an input/output device may be any of: video displays, track balls, mice, keyboards, microphones, touch-sensitive displays, transducer card readers, magnetic or paper tape readers, tablets, styluses, voice or handwriting recognizers, biometrics readers, or other computers. Processor 2722 optionally may be coupled to another computer or telecommunications network using Network Interface 2740. With such a Network Interface 2740, it is contemplated that the Processor 2722 might receive information from the network, or might output information to the network in the course of performing the above-described Price Optimization System with Business Segmentation 100. Furthermore, method embodiments of the present invention may execute solely upon Processor 2722 or may execute over a network such as the Internet in conjunction with a remote CPU that shares a portion of the processing.
In addition, embodiments of the present invention further relate to computer storage products with a computer-readable medium that have computer code thereon for performing various computer-implemented operations. The media and computer code may be those specially designed and constructed for the purposes of the present invention, or they may be of the kind well known and available to those having skill in the computer software arts. Examples of computer-readable media include, but are not limited to: magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROMs and holographic devices; magneto-optical media such as optical disks; and hardware devices that are specially configured to store and execute program code, such as application-specific integrated circuits (ASICs), programmable logic devices (PLDs) and ROM and RAM devices. Examples of computer code include machine code, such as produced by a compiler, and files containing higher level code that are executed by a computer using an interpreter.
While this invention has been described in terms of several preferred embodiments, there are alterations, modifications, permutations, and substitute equivalents, which fall within the scope of this invention. Although sub-section titles have been provided to aid in the description of the invention, these titles are merely illustrative and are not intended to limit the scope of the present invention.
It should also be noted that there are many alternative ways of implementing the methods and apparatuses of the present invention. It is therefore intended that the following appended claims be interpreted as including all such alterations, modifications, permutations, and substitute equivalents as fall within the true spirit and scope of the present invention.
This is a continuation-in-part of co-pending U.S. application Ser. No. 11/415,877 filed on May 2, 2006, entitled “Systems and Methods for Business to Business Price Modeling Using Price Elasticity Optimization”, which is hereby fully incorporated by reference. This application claims priority of U.S. Provisional Patent Application Ser. No. 60/865,643 filed on Nov. 13, 2006, which is hereby fully incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 60865643 | Nov 2006 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 11415877 | May 2006 | US |
| Child | 11938714 | US |
