Patent Publication Number: US-8121883-B2

Title: Method and system for automatically prioritizing opportunity based customer requirements

Description:
FIELD OF THE INVENTION 
     The present invention relates to a method and system for automatically prioritizing opportunity based customer requirements, and more particularly to a computer-implemented technique for automatically prioritizing customer requirements via priority scores that utilize normalization formulas and weighting factors. 
     BACKGROUND OF THE INVENTION 
     Conventionally, multiple requests (e.g., Requests for Price Quotations from a sales team) are fulfilled by test and development efforts according to a prioritization of customer requirements associated with each request. The prioritization of customer requirements includes a first come-first served approach and/or a subjective approach, such as a utilization of best practices and human judgment or a “squeaky wheel” scheme based on a particular person&#39;s or group&#39;s sponsorship of a request. These known customer requirement prioritization approaches provide an inefficient and ineffective deployment of resources for meeting customer requirements, especially in a high volume environment. In the case of the known subjective approaches, prioritization is inconsistently applied, thereby resulting in additional inefficiencies regarding resource deployment. Moreover, conventional customer requirement prioritization approaches hinder test and development teams from making complete and effective determinations of resource deployment costs. Thus, there exists a need to overcome at least one of the preceding deficiencies and limitations of the related art. 
     SUMMARY OF THE INVENTION 
     The present invention provides a computer-implemented method of automatically prioritizing a plurality of customer requirements, comprising: 
     receiving, by a computing unit, a request for fulfilling a customer requirement associated with a business opportunity, wherein the customer requirement includes one or more modifications to a product; 
     obtaining, by the computing unit, a set of prioritization data associated with the customer requirement, wherein the set of prioritization data includes a plurality of revenue opportunity sizes, a plurality of time constraints, an age of the request, an operating system value indicating whether the product requires an operating system included in a predefined list including a plurality of strategic operating systems, and an opportunity lost value indicating whether an amount of revenue associated with the business opportunity is lost if the request is rejected; 
     determining, by the computing unit, a plurality of component scores for a plurality of variables associated with the customer requirement, wherein the determining comprises evaluating a plurality of formulas that include the plurality of variables, and wherein the evaluating comprises applying a plurality of weights to the plurality of variables in a one-to-one correspondence and substituting the set of prioritization data for the plurality of variables; and 
     automatically determining, by the computing unit, a priority score for prioritizing the customer requirement relative to a plurality of other customer requirements, wherein the automatically determining the priority score comprises determining a sum of the plurality of component scores. 
     A system, computer program product and a process for supporting computing infrastructure that provides at least one support service corresponding to the above-summarized method are also described and claimed herein. 
     Advantageously, the present invention provides a technique for automatically prioritizing opportunity based customer requirements via a technique that eliminates or decreases guesswork related to selecting which customer requirement requests are queued with highest priority. Further, the present invention ranks customer requirements so that the effort expended in fulfilling a request for a customer requirement is aligned with a business value realized by fulfilling the request. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a system for automatically prioritizing opportunity based customer requirements, in accordance with embodiments of the present invention. 
         FIG. 2  is a flow diagram of asynchronous operations used by a customer requirements prioritization process implemented by the system of  FIG. 1 , in accordance with embodiments of the present invention. 
         FIG. 3  is a flow diagram of a customer requirements prioritization process that uses the operations of  FIG. 2  and that is implemented by the system of  FIG. 1 , in accordance with embodiments of the present invention. 
         FIG. 4  is a table of variables used in component score formulas used in the system of  FIG. 1  and scoring effects generated by varying values of the variables, in accordance with embodiments of the present invention. 
         FIG. 5  is a table of component score formulas used in the system of  FIG. 1  and in the process of  FIG. 3 , in accordance with embodiments of the present invention. 
         FIG. 6  is a table of sample data, normalization factors, weights and component scores used to determine a priority score in the process of  FIG. 3 , in accordance with embodiments of the present invention. 
         FIG. 7  is a table of normalization formulas used in the system of  FIG. 1 , in accordance with embodiments of the present invention. 
         FIG. 8  is a block diagram of a computing system having a computing unit that is included in the system of  FIG. 1  and that implements the operations of  FIG. 2  and the process of  FIG. 3 , in accordance with embodiments of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Overview 
     The present invention provides a technique for automatically prioritizing opportunity based customer requirements, which are associated with requests to fulfill the customer requirements. The prioritization technique disclosed herein determines customer requirement priority scores using priority score formulas that utilize normalization and weighting methods. The normalization method is performed across disparate, unassociated factors to equalize the factors, where the factors are associated with customer requirements. The weighting method is utilized to set business priority between the factors. The factors utilized by the disclosed technique include, but are not limited to, return on investment factors. The factors also include time-based factors, Boolean factors and adjustments based on human judgment. The priority scores rank opportunity based customer requirements. 
     As used herein, an opportunity based customer requirement is defined as a description of one or more modifications and/or one or more additions to the functional capabilities of a product, such as a hardware product for a computing system or a computing device. An opportunity based customer requirement is a requirement desired by a customer and is associated with a request such as a Request for Price Quotation (RPQ), where fulfilling the request provides a revenue opportunity. In one embodiment, RPQs are received by a test and development team from a sales team. An opportunity based customer requirement is also referred to herein simply as a customer requirement. As used herein, an action of fulfilling a customer requirement is equivalent to fulfilling a request associated with the customer requirement. 
     Customer Requirements Prioritization System 
       FIG. 1  is a block diagram of a system for automatically prioritizing opportunity based customer requirements, in accordance with embodiments of the present invention. System  100  includes a computing unit  102  that includes a customer requirements prioritization engine  104 . System  100  also includes inputs to engine  104  that include component score formulas  106 , normalization formulas  108 , weights  110 , adjustments  112 , customer requirements from a customer requirements repository  114  and prioritization data  116  used as values of variables in component score formulas  106 . Furthermore, system  100  includes output from engine  104  that includes priority scores  118  and a prioritized list of customer requirements  120 . 
     Component score formulas  106  are utilized to determine component scores for a set of predefined variables associated with a customer and a revenue opportunity. Normalization formulas  108  determine normalization factors that are utilized in component score formulas  106 . Weights  110  are applied in component score formulas  106  to designate the relative importance of each of the aforementioned predefined variables. Adjustments  112  are one or more optional user-defined adjustment factors that are applied to priority scores determined by engine  104  using customer requirements from repository  114 . Prioritization data  116  includes a name of a product to be supplied to a customer, an product installation date desired by the customer, a product opportunity size, a total opportunity size, an amount of time (e.g., days) left to commit, a value indicating whether a revenue opportunity is lost if the associated request to fulfill a customer requirement is rejected, a value indicating whether the product requires a strategic operating system (OS), an age of the request, an amount of time until a product installation (e.g., product installation date), a cost sizing, odds to win a bid for fulfilling the customer requirement, and a user-defined, manually input adjustment factor. To avoid skewed results derived from component score formulas  106 , the monetary-based variables (i.e., product opportunity size, total opportunity size and cost sizing) in prioritization data  116  are expressed in terms of the same monetary unit (e.g., all of the aforementioned monetary-based variables are expressed in terms of thousands of dollars). Similarly, the time-based variables (i.e., amount of time left to commit, request age, and amount of time until a product installation) in prioritization data  116  are expressed in terms of the same time-based unit of measure (e.g., all of the aforementioned time-based variables are expressed in terms of days). The functionality of the aforementioned components of system  100  is described in more detail below relative to  FIGS. 2 &amp; 3 . 
     Computing unit  102  is a computer that operates, for example, as a standalone computing system or as a client or server in a client-server computing environment. Repository  114  is, for instance, a Lotus® Notes® database. 
     Customer Requirements Prioritization Process 
       FIG. 2  is a flow diagram of asynchronous operations used by a customer requirements prioritization process implemented by the system of  FIG. 1 , in accordance with embodiments of the present invention. The asynchronous operation process of  FIG. 2  starts at step  200 . In step  202 , weights  110  (see  FIG. 1 ) that are applied to variables in component score formulas  106  (see  FIG. 1 ) are initialized or updated. Weight updates performed in step  202  provide for administrative management of skewing of component score formula variables (hereinafter, also referred to simply as “variables”) where external influences change the relative weight of certain variables over time (e.g., currency based variables which change due to inflation, pricing, etc.) while weights of other variables are held constant (e.g., time based variables). 
     In step  204 , normalization values (a.k.a. normalization factors) determined by normalization formulas  108  (see  FIG. 1 ) are initialized or updated. In step  206 , strategic operating systems are identified and stored (e.g., in a repository coupled to computing unit  102  of  FIG. 1 ) so that any component score formula  106  (see  FIG. 1 ) that depends upon identifying whether an operating system required for a revenue opportunity is strategic or not utilizes the strategic operating systems identified in step  206 . In optional step  208 , a manually input, user-defined adjustment for a priority score of a selected customer requirement is initialized or updated. Step  208  is repeated for other customer requirements if the priority scores for the other customer requirements require an adjustment. The steps of  202 ,  204 ,  206  and  208  may be performed in any order. The process of  FIG. 2  ends at step  210 . 
     An administrator or other user of system  100  (see  FIG. 1 ) performs steps  202 ,  204  and  206  asynchronously to the automatic prioritization process of  FIG. 3 , which is described below. Furthermore, step  208  is performed asynchronously to the process of  FIG. 3  by, for example, a member of a sales team or another user of system  100  (see  FIG. 1 ). 
       FIG. 3  is a flow diagram of a customer requirements prioritization process that uses the operations of  FIG. 2  and that is implemented by the system of  FIG. 1 , in accordance with embodiments of the present invention. The customer requirements prioritization process begins at step  300 . In step  302 , customer requirements prioritization engine  104  (see  FIG. 1 ) obtains a customer requirement from repository  114  (see  FIG. 1 ). The customer requirement obtained in step  302  identifies one or more modifications that a product requires because of an identified gap in the product&#39;s capability. 
     In step  304 , customer requirements prioritization engine  104  (see  FIG. 1 ) collects prioritization data  116  (see  FIG. 1 ) associated with variables of component score formulas  106  (see  FIG. 1 ). 
     In step  306 , engine  104  (see  FIG. 1 ) substitutes values from the collected prioritization data for the associated variables of component score formulas  106  (see  FIG. 1 ) and evaluates the component score formulas using weights input in step  202  (see  FIG. 2 ) and normalization factors input in step  204  (see  FIG. 2 ). Normalization factors are used in step  306  to prevent external influences from skewing relative weights of component score formula variables, to prevent price differences between products from skewing priorities towards higher priced products, to allow for differences in length of sales cycle or time it takes to implement the requirement for various products being prioritized in a relative group, and to allow the value of all component score formula variables to start out having equal weight, before the weights  110  (see  FIG. 1 ) are applied. As one example of the skewing being avoided by normalization, consider that an average deal size increases each year due to inflation, improved clothing rates, etc., with all other component score formula variables being equal. In this case, as the average deal size increases, the relative impact on the priority score increases. 
     Engine  104  (see  FIG. 1 ) also dynamically and automatically determines a total priority score in step  306  for the customer requirement obtained in step  302  by following these steps: (1) adding the results of the evaluated component score formulas to determine a sum; (2) optionally multiplying a value of an odds to win variable by the sum to obtain a priority score; and (3) optionally adjusting the obtained priority score by a user-defined adjustment factor to determine the total priority score (e.g., by adding the adjustment factor to the obtained priority score). If there is no adjustment factor associated with the customer requirement obtained in step  302 , then the priority score obtained after multiplying the odds to win by the sum of the evaluated component score formulas is the total priority score for the obtained customer requirement. Step  306  is performed dynamically in response to any updates performed in the process of  FIG. 2 , in response to any update of prioritization data  116  (see  FIG. 1 ) that is used as a value of a component score formula variable or in response to passage of time on a daily interval. Step  306  is performed by engine  104  (see  FIG. 1 ) executing, for instance, Microsoft® Excel® commands. 
     In step  308 , customer requirements prioritization engine  104  (see  FIG. 1 ) dynamically and automatically generates a prioritized list of customer requirements that ranks the customer requirement obtained in step  302  relative to other customer requirements according to the priority score determined in step  306  and other priority scores determined for the other customer requirements in other, previous executions of the process of  FIG. 3 . For example, the prioritized list is generated and displayed in a highest to lowest priority score order. Step  308  is performed by engine  104  (see  FIG. 1 ) executing, for instance, Microsoft® Excel® commands. 
     In step  310 , a user (e.g., a project manager) selects a customer requirement from the prioritized list generated in step  308  based on the placement of the selected customer requirement in the list (i.e., based on the priority score of the customer requirement). For example, the user selects the customer requirement in the prioritized list that has the highest priority score. Also in step  310 , resources (e.g., equipment and personnel resources) and funding are assigned to fulfill the selected customer requirement. The assignment of resources to the customer requirement having the highest priority score provides the most business-related benefits for the funds and resources expended in fulfilling the customer requirement. The particular customer requirement selected in step  310  is a definable, repeatable result of the customer requirement prioritization process of  FIG. 3 . The prioritization process of  FIG. 3  ends at step  312 . 
     In one embodiment, step  310  includes a test and development team identifying requests that have the highest priority scores on the prioritization list generated in step  308 . The test and development team then determines for each of the identified requests whether or not to apply equipment and people resources during an upcoming testing phase. 
       FIG. 4  is a table  400  of variables used in component score formulas used in the system of  FIG. 1  and scoring effects generated by varying values of the variables, in accordance with embodiments of the present invention. Each component score formula is used to generate a component score associated with a variable of a plurality of component score formula variables. The variable associated with a component score is referred to herein as an associated component variable. The generated component score is a component of a customer requirement&#39;s priority score determined in step  306  of  FIG. 3 . The variables and the scoring effects included in table  400  are described below. In each of the following descriptions of the component score formula variables, a reference to the customer requirement or customer&#39;s requirement refers to the customer requirement obtained in step  302  of  FIG. 3 . 
     Product opportunity size: An estimated amount of revenue (e.g., a dollar amount) to be realized as a result of providing to a customer the product whose functionality is modified and/or added to according to a customer requirement provided by the customer. A larger product opportunity size corresponds to a higher associated component score and a smaller product opportunity size corresponds to a lower associated component score. 
     Total opportunity size: An estimated amount of total revenue (e.g., a dollar amount), which includes the product opportunity size associated with a product and an estimated additional amount of revenue to be realized as a result of providing one or more other products to a customer, where the need for the one or more other products is driven by the customer&#39;s need for the product. For example, if the product opportunity size is an estimated amount of revenue from providing a SAN Volume Controller (SVC) engine to a customer, then total opportunity size includes the product opportunity size plus estimated revenue from sales of additional storage which is driven by the customer&#39;s need for the SVC engine. The SVC engine is offered by International Business Machines Corporation located in Armonk, N.Y. A larger total opportunity size corresponds to a higher associated component score and a smaller total opportunity size corresponds to a lower associated component score. 
     Amount of time left to commit: An amount of time (e.g., number of days) remaining before a commitment to fulfill the customer requirement must be delivered to the customer in order to preserve an ability to fulfill the customer requirement. For example, if the amount of time left to commit is 2 days, then the test and development team must commit to fulfilling the customer requirement within 2 days or the revenue opportunity will be lost. A smaller amount of time left to commit corresponds to a higher associated component score and a larger amount of time left to commit corresponds to a lower associated component score. It should be noted that the amount of time left to commit is distinguished from an amount of time left to deliver the product to the customer. In another embodiment, the amount of time left to deliver the product is added to table  400  as another component score formula variable. 
     Opportunity lost if rejected?: A Yes or No value that indicates whether the revenue opportunity for fulfilling the customer requirement sometime in the future is lost if the request for fulfilling the customer requirement is rejected. For example, if the revenue opportunity is lost if the request is rejected, then the value is 1 (i.e., a Yes value) and otherwise the value is 0 (i.e., a No value). The associated component score is higher if the opportunity lost if rejected value is Yes and lower if the value is No. 
     Strategic operating system?: A Yes or No value that indicates whether an operating system required by the product being modified by the customer requirement is included in a predefined list of strategic operating systems. Inclusion in the predefined strategic operating system list indicates that the product&#39;s operating system is one of a predefined set of standard operating systems that are, for example, commonly used by the test and development team that deploys resources for fulfilling the customer requirement. If the customer requirement describes a function that requires one of the predefined strategic operating systems, then the likelihood is high that that there are multiple requests for the same function and the likelihood is low that the required function is unique and requires customization work. Therefore, fulfilling a customer requirement associated with a standard operating system is a relatively easy task for the test and development team and is ranked higher, as opposed to fulfilling a customer requirement by building a customized function associated with a non-standard operating system, which is a relatively harder task that is ranked lower. For example, if the product&#39;s operating system is a standard operating system included in the predefined strategic operating system list, then the strategic operating system value is 1 (i.e., a Yes value), otherwise the value is 0 (i.e., a No value). The associated component score is higher if the strategic operating system value is Yes and lower if the value is No. 
     Request age: An age of the request for a fulfillment of the customer requirement, where the age is expressed in, for example, a number of days. The request is, for instance, an RPQ. An older (i.e., larger) request age corresponds to a higher associated component score and a newer (i.e., smaller) request age corresponds to a lower associated component score. 
     Amount of time left before product installation: An amount of time (e.g., number of days) remaining before an installation of the product whose functionality is modified and/or added to according to the customer requirement. A smaller amount of time left before the product installation corresponds to a higher associated component score and a larger amount of time left before the product installation corresponds to a lower associated component score. 
     Cost sizing: An estimate of a cost (e.g., a dollar amount) of a test for fulfilling the customer requirement and of related development work and software error fixes, where the test is performed by, for example, a test and development team. The cost sizing includes a cost of resources (e.g., equipment and/or personnel) required for the test (e.g., hardware rentals and short-term contractors needed for the test, travel costs, etc.). A smaller cost sizing corresponds to a higher associated component score and a larger cost sizing corresponds to a lower associated component score. 
     Odds to win: An odds value of winning a bid for fulfilling the customer requirement. The odds value is an estimate of the probability of winning the bid for fulfilling the customer requirement provided by, for example, a member of a sales team. A larger odds value corresponds to a higher priority score associated with the customer requirement and a smaller odds value corresponds to a lower priority score. For example, the odds to win value is determined by a sales team that is submitting the customer requirement to a test and development team. 
     User-defined adjustment factor: An optional user-defined adjustment value that is added to an initial priority score to generate a final priority score for the customer requirement. The initial priority score is generated by multiplying the odds value (e.g., expressed as a percentage) by a sum of all the component scores (e.g., the aforementioned associated component scores associated with product opportunity size, total opportunity size, amount of time left to commit, opportunity lost if rejected, strategic operating system, request age, amount of time left before product install date, and cost sizing). A larger user-defined adjustment factor corresponds to a higher priority score associated with the customer requirement and a smaller user-defined adjustment factor corresponds to a lower priority score. The user-defined adjustment factor is determined by, for example, a member of a sales team or an administrator of system  100  (see  FIG. 1 ). 
     The formulas associated with each component score formula variable listed above are included in  FIG. 5 , which is described below. 
       FIG. 5  is a table  500  of component score formulas used in the system of  FIG. 1  and in the process of  FIG. 3 , in accordance with embodiments of the present invention. A component score is determined in step  306  (see  FIG. 3 ) for each component score formula variable in the first column of table  500  by evaluating the corresponding component score formula in the second column of table  500 . For example, a component score for the Product opportunity size variable is determined by evaluating the formula: (PRODUCT OPPORTUNITY SIZE/NORMALIZATION FACTOR)×WEIGHT. In the various formulas disclosed herein, the multiplication symbols of * and × are used interchangeably. 
     Examples of evaluating the component score formulas of table  500  are discussed below relative to  FIG. 6 . A condition in the third column of table  500  indicates that if the component score formula (1/(AMOUNT OF TIME LEFT TO COMMIT/NORMALIZATION FACTOR)) ×WEIGHT (i.e., the component score formula associated with the Amount of time left to commit variable) evaluates to a negative value, then the priority score for the Amount of time left to commit variable is set to a predefined value (e.g., a value of 10). 
     It should be noted that each occurrence of NORMALIZATION FACTOR and WEIGHT in the component score formula column of table  500  does not refer to the same normalization factor and the same weight. Rather, for a first group of component score formula variables that include Product opportunity size, Total opportunity size, Amount of time left to commit, Opportunity lost if rejected, Strategic operating system, Request age, Amount of time before product install date, and Cost sizing, the WEIGHT values occurring in component score formulas associated with the aforementioned first group of variables are associated with a plurality of weights in a one-to-one correspondence, where the plurality of weights are included in weights  110  (see  FIG. 1 ). Moreover, for a second group of component score formula variables that includes Product opportunity size, Total opportunity size, Amount of time left to commit, Request age, Amount of time before product install date, and Cost sizing, the NORMALIZATION FACTOR values occurring in the component score formulas associated with the aforementioned second group of variables are associated with a plurality of normalization factors in a one-to-one correspondence, where the plurality of normalization factors are derived from normalization formulas  108  (see  FIG. 1 ). Examples of normalization formulas  108  are presented below relative to  FIG. 7 . 
     The present invention contemplates other component score formulas that are equivalent to the component score formulas presented in table  500 . For example, in the component score formula corresponding to the Product opportunity size variable, dividing by NORMALIZATION FACTOR can be replaced by multiplying by the reciprocal of NORMALIZATION FACTOR. 
     Further, the present invention contemplates other Yes and No values of the Opportunity lost if rejected and Strategic operating system variables besides the 1 and 0 values shown in the Comments/Conditions column of table  500 . 
     EXAMPLE 
       FIG. 6  is a table  600  of sample data, normalization factors, weights and component scores used to determine a priority score in the process of  FIG. 3 , in accordance with embodiments of the present invention. Sample data in the second column of table  600  is data for each of the component score formula variables in the first column of table  600 . The component score formula variables in the first column of table  600  are discussed above relative to  FIGS. 4 and 5 . For example, the product opportunity size variable in the example of  FIG. 6  is assigned a value of  600  (i.e., the  600  in the Sample data column). Sample normalization factors and sample weights used in the evaluation of the component score formulas of  FIG. 5  are shown in the Normalization factor and Weight columns, respectively, of table  600 . Finally, the Component score column of table  600  includes the component scores evaluated using the corresponding data from the Sample data, Normalization factor and Weight columns of table  600  in the associated component score formulas of  FIG. 5 . 
     In table  600 , the sample data for the product opportunity size, total opportunity size and cost sizing variables are expressed in the same monetary unit (not shown). Similarly, the sample data in table  600  for the amount of time left to commit, request age, and amount of time until product installation variables are expressed in terms of the same time-based unit of measure (not shown). 
     The component scores of the Component score column of table  600  are evaluated as follows, using 1 to indicate Yes for the Opportunity lost if rejected and Strategic operating system variables:
 
Product opportunity size component score=(PRODUCT OPPORTUNITY SIZE/NORMALIZATION FACTOR)×WEIGHT=(600/300)×0.05=0.10
 
Total opportunity size component score=(TOTAL OPPORTUNITY SIZE/NORMALIZATION FACTOR)×WEIGHT=(800/400)×0.2=0.40
 
Amount of time left to commit component score=(1/(AMOUNT OF TIME LEFT TO COMMIT/NORMALIZATION FACTOR))×WEIGHT=(1/(7/14))×0.25=0.50
 
Opportunity lost if rejected component score=OPPORTUNITY LOST IF REJECTED VALUE×WEIGHT=1×0.1=0.10
 
Strategic operating system component score=STRATEGIC OS VALUE×WEIGHT=1 ×0.1=0.10
 
Request age component score=(REQUEST AGE/NORMALIZATION FACTOR)×WEIGHT=60/30×0.05=0.10
 
Amount of time before product installation component score=(1/(AMOUNT OF TIME BEFORE PRODUCT INSTALLATION/NORMALIZATION FACTOR))×WEIGHT=(1/(60/30))×0.1=0.05
 
Cost sizing component score=((COST SIZING/NORMALIZATION FACTOR)×WEIGHT)×(−1)=((12/25)×0.15)×(−1)=−0.072
 
     The sum of the evaluated component scores listed above is determined in step  306  (see  FIG. 3 ). In the example of  FIG. 6 , the sum is 0.10+0.40+0.50+0.10+0.10+0.10+0.05+−0.072 (i.e., 1.278). The sum is then multiplied by the Odds to win value expressed as a percentage. That is, 1.278 is multiplied by 100 (i.e., the entry in the Sample data column of table  600  for the Odds to win variable) to determine an initial priority score of 127.80, which is shown in the last column of table  600  in the row corresponding to the Odds to win variable. Following the determination of the initial priority score in step  306  (see  FIG. 3 ), the user-defined adjustment factor is added to the initial priority score to determine a final priority score. That is, the user-defined adjustment factor of 1 (i.e., the Sample data entry for the user-defined adjustment factor variable of table  600 ) is added to 127.80 to obtain the final priority score of 128.80, which is shown in the last column of table  600  in the row labeled Priority score. 
     Normalization Factors 
       FIG. 7  is a table  700  of normalization formulas used in the system of  FIG. 1 , in accordance with embodiments of the present invention. Table  700  includes examples of normalization formulas  108  (see  FIG. 1 ). For instance, the normalization factor in the total opportunity size component score formula of (TOTAL OPPORTUNITY SIZE/NORMALIZATION FACTOR)×WEIGHT is determined by the normalization formula in the second column of table  700  that corresponds to the total opportunity size variable in the first column of table  700 . That is, the normalization factor in the total opportunity size component score formula is determined from the formula AVERAGE TOTAL OPPORTUNITY SIZE/NORMALIZATION FACTOR=1. 
     Computing System 
       FIG. 8  is a block diagram of a computing system  800  having a computing unit  102  that is included in the system of  FIG. 1  and that implements the operations of  FIG. 2  and the process of  FIG. 3 , in accordance with embodiments of the present invention. Computing unit  102  generally comprises a central processing unit (CPU)  802 , a memory  804 , an input/output (I/O) interface  806 , and a bus  808 . Computing system  800  includes I/O devices  810  and a storage unit  812  coupled to computing unit  102 . CPU  802  performs computation and control functions of computing unit  102 . CPU  802  may comprise a single processing unit, or be distributed across one or more processing units in one or more locations (e.g., on a client and server). 
     Memory  804  may comprise any known type of data storage and/or transmission media, including bulk storage, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), a data cache, a data object, etc. Cache memory elements of memory  804  provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. Storage unit  812  is, for example, a magnetic disk drive or an optical disk drive that stores data. Moreover, similar to CPU  802 , memory  804  may reside at a single physical location, comprising one or more types of data storage, or be distributed across a plurality of physical systems in various forms. Further, memory  804  can include data distributed across, for example, a LAN, WAN or storage area network (SAN) (not shown). 
     I/O interface  806  comprises any system for exchanging information to or from an external source. I/O devices  810  comprise any known type of external device, including a display monitor, keyboard, mouse, printer, speakers, handheld device, printer, facsimile, etc. Bus  808  provides a communication link between each of the components in computing unit  102 , and may comprise any type of transmission link, including electrical, optical, wireless, etc. 
     I/O interface  806  also allows computing unit  102  to store and retrieve information (e.g., program instructions or data) from an auxiliary storage device (e.g., storage unit  812 ). The auxiliary storage device may be a non-volatile storage device (e.g., a CD-ROM drive which receives a CD-ROM disk). Computing unit  102  can store and retrieve information from other auxiliary storage devices (not shown), which can include a direct access storage device (DASD) (e.g., hard disk or floppy diskette), a magneto-optical disk drive, a tape drive, or a wireless communication device. 
     Memory  804  includes program code for customer requirements prioritization engine  104 . Further, memory  804  may include other systems not shown in  FIG. 8 , such as an operating system (e.g., Linux) that runs on CPU  802  and provides control of various components within and/or connected to computing unit  102 . 
     The invention can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In a preferred embodiment, the invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc. 
     Furthermore, the invention can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code  104  for use by or in connection with a computing system  800  or any instruction execution system to provide and facilitate the capabilities of the present invention. For the purposes of this description, a computer-usable or computer-readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. 
     The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, RAM  804 , ROM, a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk—read-only memory (CD-ROM), compact disk—read/write (CD-R/W) and DVD. 
     Any of the components of the present invention can be deployed, managed, serviced, etc. by a service provider that offers to deploy or integrate computing infrastructure with respect to the method of automatically prioritizing a plurality of customer requirements. Thus, the present invention discloses a process for supporting computer infrastructure, comprising integrating, hosting, maintaining and deploying computer-readable code into a computing system (e.g., computing system  800 ), wherein the code in combination with the computing unit is capable of performing a method of automatically prioritizing a plurality of customer requirements. 
     In another embodiment, the invention provides a business method that performs the process steps of the invention on a subscription, advertising and/or fee basis. That is, a service provider, such as a Solution Integrator, can offer to create, maintain, support, etc. a method of automatically prioritizing a plurality of customer requirements. In this case, the service provider can create, maintain, support, etc. a computer infrastructure that performs the process steps of the invention for one or more customers. In return, the service provider can receive payment from the customer(s) under a subscription and/or fee agreement, and/or the service provider can receive payment from the sale of advertising content to one or more third parties. 
     The flow diagrams depicted herein are provided by way of example. There may be variations to these diagrams or the steps (or operations) described herein without departing from the spirit of the invention. For instance, in certain cases, the steps may be performed in differing order, or steps may be added, deleted or modified. All of these variations are considered a part of the present invention as recited in the appended claims. 
     While embodiments of the present invention have been described herein for purposes of illustration, many modifications and changes will become apparent to those skilled in the art. Accordingly, the appended claims are intended to encompass all such modifications and changes as fall within the true spirit and scope of this invention. For example, the customer requirement prioritization system and process disclosed herein can be applied to a service desired by a customer, where customer requirements describe one or more modifications and/or one or more additions to the service.