Abstract:
In a retail business environment, sales employees interact with customers and generate retail sales. Employee skills, product knowledge, and performance are particularly relevant to sales generated, and hence to cash flow. Periodic assessments are typically performed to monitor the progress and growth of the business and to identify areas of strength and flag potential weaknesses. Such assessments typically take the form of sales data outlining generated revenue, for example, gross receipts or the net profit of a particular employee or store. A system for measuring and analyzing performance data including collecting data indicative of performance and analyzing the data to compute an aggregate synopsis of performance of an employee provides quantitative data for providing employee specific feedback and direction to the employee for improving performance. A hypothesis is formed from the aggregate synopsis to determine one or more actions, such as training and skill practice, directed to improving performance. The determined actions are pursued with the employee under evaluation, and a change in the performance as a result of applying the actions is measured. Measurement and analysis is repeated in an iterative manner to continually assess and develop employee skills toward increased performance and hence, increased sales.

Description:
RELATED APPLICATION(S) 
     This application claims the benefit of U.S. Provisional Application No. 60/270,563, filed on Feb. 21, 2001. The entire teachings of the above application(s) are incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     In a business environment, periodic assessments are typically performed to monitor the progress and growth of the business and to identify areas of strength and flag potential weaknesses. In a retail business environment, such assessments typically take the form of sales data outlining generated revenue, such as the gross receipts or the net profit of a particular store. 
     A business environment includes a number of employees, each contributing their skills and knowledge to further the business. Maximum productivity and efficiency occurs when each employee is functioning to utilize their skills and knowledge to their fullest potential. An employee undertaking a role that does not invoke all their skills and knowledge may be underutilized, while an employee having insufficient skill or knowledge for a particular role will likely not be productive or efficient. 
     Periodic assessment of the contribution of each employee is typically performed to identify achievement for items such as bonuses and merit increases. Such an assessment is reported to a central home office or accounting office. However, employee specific feedback concerning skills and knowledge is typically not communicated back to the employee for future development. Achievement may be recognized in the form of a bonus or large merit increase, while shortfalls may be met with a lower than average merit increase. Employee specific feedback regarding skill and knowledge development, and tracking employee development over time, is not provided. Further, some organizations may not even communicate employee specific sales data to the home office, but rather employ only global figures for a particular site or store, leaving the individual employee merit determinations up to the local manager. 
     Accordingly, a typical periodic assessment does not provide individual direction through formal training and proactive skill development. It would be beneficial to provide a system in which employee specific feedback and direction is provided in an iterative manner to develop individual skills and knowledge to increase productivity and efficiency, which in turn increases sales and revenue and allows the business to grow. 
     SUMMARY OF THE INVENTION 
     In a retail business environment, sales employees interact with customers and generate retail sales. A system and method for measuring and analyzing performance data including collecting transaction data indicative of performance factors, and analyzing the transaction data to compute an aggregate synopsis of performance of a subject under observation, or employee, provides quantitative data for providing feedback to the subject under observation for improving performance. A hypothesis is formed from the aggregate synopsis to determine one or more recommended actions directed to improving performance. The determined recommended actions are applied to the subject under observation, and a change in the performance as a result of applying the recommended actions is measured. Measuring and analyzing is repeated in an iterative manner to continually assess and develop employee skills toward increased performance and hence, increased sales. 
     The transaction data collected includes field performance data to determine sales per hour, revenue per sale transaction and total customer flow, training data such as applied curriculum and skill development provided by the system, and external legacy data such as corporate administrative, retail store and human resource data. The aggregate synopsis provides quantitative performance scores within an expected performance range, and is employed to develop a hypothesis for improvement. The aggregate synopsis may be provided via predetermined reports aimed at identifying specific performance areas, or may be provided through ad-hoc reports generated via a query engine. 
     The hypothesis is developed by a rule based expert systems, by manual inspection of the aggregate synopsis, or by other means, and is used to map the performance score to one or more recommended actions. The recommended actions are directed toward multimedia educational curriculum and identified skills to be developed. A feedback mechanism, such as collection and analysis of subsequent transactional data, is employed to assess the effectiveness of the determined recommended actions. 
     The transactional data is collected in a data store including a relational database of raw transaction data and a knowledge base including an aggregated transformation of the transaction data in a multidimensional database. The multidimensional database renders the data in an on-line analytical processing (OLAP) form adapted for hierarchical (drill-up/drill-down) access at a variety of granularity levels, as is known to those skilled in the art. 
     The aggregated synopsis outputs a performance score indicative of a ranking within a performance range of expected scores in the experience category of the sales employee, such as low, medium, and high. A mapping is performed including the ranking and the experience category, and may include other factors such as store attributes and customer throughput. The mapping includes associating skills, knowledge, and sales data to recommended actions for improving performance. A hypothesis is developed concerning identified areas for improvement, and mapped to a predetermined set of recommended actions for developing the identified areas. 
     The recommended actions include skills for practice on the sales floor and review or training via a learning center. The learning center provides a library of multimedia curriculum including magnetic, optical, and printed mediums of instruction and feedback, and is specifically designed to target the identified skills and knowledge for improvement. 
     In this manner, performance data concerning a sales employee is gathered and analyzed, and areas for improvement identified. The identified areas are addressed by recommended actions via a manager or coach, and developed through the learning center. Iterative application of skill and knowledge development allows feedback to be introduced to target and revisit specific areas for each sales employee. Improvement is measured by examining the transaction data at each cyclic application, thereby identifying increased performance and revenue in response to the applied recommended actions. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. 
         FIG. 1  is a context diagram of the performance improvement measurement system (PIMS) as defined herein; 
         FIG. 2  is a block diagram of PIMS inputs and outputs; 
         FIG. 3  is a diagram of the iterative data and information flow in the system of  FIG. 2 ; 
         FIG. 4  is a block diagram of the system of  FIG. 2  showing data population and analysis in greater detail; 
         FIG. 5  is a block diagram of report output retrieval; 
         FIG. 6  is a data flow diagram of the development cycle iteration; 
         FIG. 7   a  is a flowchart of the operation of the system of  FIG. 4 ; 
         FIG. 7   b  is a flowchart of data gathering; 
         FIG. 7   c  is a flowchart of the analysis of results; 
         FIG. 7   d  is a flowchart of development analysis for determination of recommended actions; 
         FIGS. 8   a - 8   j  show reports generated by the system of  FIG. 4 ; 
         FIGS. 9   a - 9   d  are performance matrices for mapping of performance scores to recommended actions; and 
         FIG. 10  is a matrix showing scaling of performance standards. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     A description of preferred embodiments of the invention follows. 
     In a retail sales environment, a system and method for measuring and analyzing transaction data of sales activity allows individual feedback to be provided to each employee concerning specific actions for knowledge and skill development for improving performance.  FIG. 1  shows a context diagram of the Performance Improvement Measurement System (PIMS). Referring to  FIG. 1 , PIMS  12  is shown in the business environment  10  context. The environment  10  includes employee associates or sales employees  14 , a learning center  16  for implementing the skill and knowledge development, described further below, corporate management  18 , a plurality of field locations  20 , typically retail stores, each with a point of sale (POS) data system for gathering field data, and home office computing systems  22 , providing information such as revenue goals, accounting, and human resource data. 
       FIG. 2  shows a block diagram of inputs and outputs of the system  12 . Referring to  FIG. 2 , the system  12  receives data from a plurality of sources, including external and legacy data  24  from the home office such as corporate administrative and human resource data, field performance data  26  including POS data, and training data  28  such as educational curricula provided by a learning center and associated learning progress, described further below. The raw data  30  received by the system is employed to produce reports  32  containing a quantitative analysis of the raw data, also described further below. The reports  32  provide an aggregated synopsis of the raw data, shown by arrow  34 , organized at different levels of granularity, which are used to generate a hypothesis for improvement  36  for a subject under observation, such as an sales employee  14  ( FIG. 1 ). The hypothesis for improvement  36  determines particular skills or knowledge which the sales employee should improve on, based on the reports  32 , and indicates specific recommended actions to be undertaken, shown by arrow  38 . The recommended actions  38  selected are focused on the particular knowledge and/or skills which the individual employee should further develop, and include simple coaching from management, skills to practice at the retail location, or specific coursework curriculum to be initiated or reviewed. The recommended actions  38  are undertaken by the particular sales employee  14 . A subsequent evaluation will gather data from each of the sources  24 ,  26  and  28  to produce periodically updated sets of raw data  30  for reevaluation after application of the recommended actions  38 . 
       FIG. 3  is a diagram of the iterative data and information flow in the system of  FIG. 2 . Referring to  FIGS. 2 and 3 , a data capture phase  40  includes gathering the raw data  30  from the data sources  24 ,  26 , and  28 . The reports  32  generated from the raw data comprise development information  42  about each subject under observation. Development planning  44  includes developing a hypothesis for improvement  36 , and may include other aspects such as staffing and business goals. The development planning  44  is implemented via coaching activity  46 , which includes applying the determined recommended actions  38  to the employee  14   n . The coaching activity  46  results in developmental learning  48 , including skills, knowledge, and practice. The developmental learning is applied by floor activity  50 , in which the sales employee  14  applies and demonstrates the result of the developmental learning  48 . The information and data flow therefore follows a full feedback cycle through PIMS which continually monitors performance and determines steps for improvement. 
       FIG. 4  is a block diagram of a particular embodiment of the system of  FIG. 2  showing data population and analysis in greater detail. Referring to  FIG. 4 , the transaction data is gathered from several sources. External and legacy data ( 24 ,  FIG. 2 ) further comprises corporate administrative  52  and human resource  54  data. Field performance data ( 26 ,  FIG. 2 ) further comprises customer traffic count  56  and point-of-sale (POS) data  58 , and training data ( 28 ,  FIG. 2 ) further comprises learning center  60  data, described further below. 
     The system  12  includes a database server  62  which receives the transaction data via the raw data interface  30 . The raw data interface is an offline batch interface which periodically uploads the transaction data by a variety of means, but may also comprise a real-time connection. The database server  62  includes a relational database  64  and a multidimensional knowledge base  66 . The transaction data  52 ,  54 ,  56 ,  58 , and  60  is stored in the relational database  64 , and a subset of the data translated to a multidimensional form in the knowledge base  66 . The knowledge base stores the data in a data cube manner operable for on-line analytical processing (OLAP) manipulation by an OLAP processor as is known to those skilled in the art. The knowledge base allows the data to be aggregated and extracted in a drill-up/drill down manner as required by the reports, described further below. 
     An analysis engine  68  incorporated within the reporting function operates to access the transaction data via the database server  62  for generating reports  32  ( FIG. 2 ). The reports include a set of standard reports  70   a - h  and ad-hoc reports  72 . The standard reports,  70  generally, contain an aggregate synopsis of quantitative computations about a particular subject under observation, such as an employee or retail store, according to a predetermined sequence of computations, and are described further below with respect to  FIGS. 8   a - 8   j . The standard reports  70  are typically computed in real-time on demand. The ad-hoc reports  72  are provided via a third party reporting tool to retrieve the data from the database server  62 . In either case the reports  36  are employed in developing a hypothesis from which to derive developmental planning for the subject under observation. 
     The reports  36 , from among the standard reports  70 , are invoked by a hypothesizer  74 . The hypothesizer  74  interprets the quantitative information in the reports  36  to allow qualitative comparisons and conclusions to be determined. The reports  36  include a number of fields about a subject under observation. Each field in the report has a performance score about the subject under observation. For each field, there is also a performance range indicative of a typical score and the variation from that score. A classifier  76  in the hypothesizer  74  compares the performance score to the performance range to derive a performance rank of the subject under observation for the concerned field. The performance rank is enumerated into several tiers across the performance range. In a particular embodiment the performance rank may have three tiers of low, average, and high (L, A, H), indicative of the performance of the subject under observation in that performance field. In alternate embodiments the performance rank may have other tiers, such as a two tiered yes/no (Y,N) scale or a ten point scale, for example. 
     Each of the reports can be generated on a variety of levels of granularity, including granularity with respect to scope and with respect to time. Specifically, reports can be generated per employee, store, district, region, division, and company. Reports can also be generated to cover timing intervals including daily, weekly, monthly, quarterly, and yearly progress. 
     The classifier  76 , therefore, indicates a set of performance ranks concerning a particular subject under observation for a plurality of fields. For the particular subject under observation, the performance ranks are employed along with a type of the subject under observation to map to recommended actions ( 38 ,  FIG. 2 ) to be taken to improve the performance of the subject under observation. The type of the subject under observation includes sales employees and retail stores. Further, sales employees include the types of associate, advisor, and consultant. A mapper  78  in the hypothesizer  74  receives the performance ranks and the type of the subject under test to map to the recommended actions  38 . Based on the performance ranks and the type of the subject under observation, one or more recommended actions  38  are determined from the mapping. In a particular embodiment, the mapping is a matrix form, described further below, which correlates the type of the subject under observation, the performance ranks, and the recommended actions. Other factors could be employed. 
     The operation performed by the hypothesizer  74  can be performed in several ways. In a particular embodiment, the quantitative reports are manually inspected and compared to the expected performance range. The mapping further comprises reference to the matrix of possible values and a listing of the corresponding recommended actions. Manual inspection in this manner further allows qualitative assessments to be made regarding performance rankings within the expected performance range. In alternate embodiments, the hypothesizer  74  comprises an expert system (not shown) which receives the performance scores and type of the subject under observation, and computes the recommended actions according to a rule-based mechanism. 
     The recommended actions  38  determined by the hypothesizer  74  indicate skills and knowledge which the subject under observation excels at, and should be rewarded for, and those for which improvement is called for. The recommended actions are sent to a manager  80  of the subject under observation for review and implementation. The recommended actions also indicate specific curriculum or review  82  to be undertaken at the learning center  60 . The learning center has a variety of curricula targeted at improvement of specific knowledge or skills indicated by the recommended actions. The recommended actions may also indicate skills and knowledge for practice  84 , to be undertaken at the point of sale  58 . Depending on the particular recommended action, manager  80  involvement, curriculum review  82  at the learning center, or skills and knowledge for practice  84 , or a combination, may be indicated. Manager  80  involvement in decision making for implementing the recommended actions  38  from among the variety of curricula available via the learning center provides another level of qualitative assessment for the skill and knowledge development of the subject under observation. 
       FIG. 5  is a block diagram of output retrieval. Referring to  FIGS. 4 and 5 , a manager  80  may access the system reports  36  via a variety of connections, such as the local store system  88 , a home office  92 , or other remote location  94 . The local store system  88 , may, for example, also include the POS system  58  for the local store. Access to PIMS  12  occurs via a web/user interface  86  to access the reports  36  over the Internet  90  or other remote access network. A security schema  85 , described further below, is employed to regulate access to PIMS  12 . 
     The security schema  85  defines a role and privilege mechanism for viewing data from PIMS  12 . Users are authenticated through a password or other authentication process. For each user, privileges are defined based on the user&#39;s role within the organization, their need-to-know, and the tenuity and specificity of the data sought. For example, a new entry-level associate might be able to access their own performance scores, but not those of a colleague. Similarly, a manager needs to have access to data and reports concerning all sales employees under her, but need not have access to employee sales data from another region outside their responsibility. Other rules and checks can also be employed in the security schema. 
     The learning center  60  comprises multimedia educational curricula focused at improving the specific skills and knowledge of the sales employee. The particular curriculum is selected based on the determined recommended action. The curriculum may be delivered electronically on the display of the learning center  60  from DVDs (Digital Video Discs), CDs (Compact Discs), and other optical and magnetic media, by print medium, such as workbooks and texts, or by a combination. Curriculum feedback such as exercises and tests may likewise be provided by a plurality of mediums. In this manner, the learning center represents a library of educational resources including software, visual, audio, and printed mediums designed and tailored to address a particular skill or knowledge targeted for improvement. In a particular embodiment, the learning center is a library of multimedia educational curricula marketed commercially by The Yacobian Group, LLC of Maynard, Mass., however, other developmental mechanisms for addressing skill and knowledge improvement may also be employed. 
       FIG. 6  is a data flow diagram of the development cycle iteration similar to the data described above with respect to  FIG. 6 . The association of PINS analysis is shown in relation to the multimedia learning center  60  activities aimed at improving the skills and knowledge of the subject under observation. Referring to  FIGS. 6 and 4 , the PIMS data inputs  102  are received via the transactional data interface, and comprise the transactional data sources  52 ,  54 ,  56 ,  58 , and  60 . The PIMS output  110  includes the reports  36  and is employed in developing further hypotheses for improvement  104 . The recommended actions  38  suggested by the hypothesis are taken  106 , and the feedback mechanism  98  employed for the subsequent data measurement  108  via PIMS  12 . The output  100  comprises fresh transaction data  30  which complements the iterative cycle. The gathering, analysis and processing occurs within PIMS as shown by the line  112 , while the hypothesizing and actions occur in conjunction with the learning center  60 , as shown by the line  114 . 
       FIGS. 7   a - 7   d  show flowcharts of the operation of PIMS system of  FIG. 4 . Referring to  FIGS. 7   a  and  4 , transaction data is gathered from a plurality of sources, as depicted at step  300 . The raw transaction data is transmitted via an interface  30  to the database server  62 , as shown at step  302 . The analysis engine  62  analyzes the data from the database server  62  to generate quantitative reports  36 , as depicted at step  304 . As indicated above, the reports may take a variety of forms, including the standard reports  70  predetermined to correspond to typical performance fields, and ad-hoc reports  72 , designed to allow custom queries by the user. The reports  36  are employed by a hypothesizer  74  to determine recommended actions  38  for improving skills and knowledge as proscribed by the reports, as disclosed at step  306 . From the hypothesizer, the recommended actions  38  are applied, as depicted at step  308 . The recommended actions may be implemented by a manager  80 , through multimedia curricula  82  via the learning center  60 , or by skill practice  84 , on the sales floor, as disclosed at steps  310 ,  312 , and  314 , respectively. Following implementation, the results are measured as transaction data during the next gathering of transaction data gathering, as depicted at step  316 , and control reverts to step  300  for further analysis in an iterative manner. 
       FIG. 7   b  shows a flowchart of data gathering. Referring to  FIGS. 7   b  and  4 , a plurality of data interfaces to remote systems are identified, as depicted at step  320 , collectively  30 . For each of the identified interfaces, data is gathered, or uploaded, to the database server  62  in PIMS  12 . The interface may be offline batch interfaces, real time, data entry, or other mode of communication, depending on the remote system. For each of the remote systems, one or more data sets are uploaded. Corporate administrative, human resource, and sales and revenue projection/goal data is uploaded from the home office systems, as shown at step  322 . Learning center data based on the recommended actions pursued is uploaded, such as courses completed, tests taken, and skills attained, as shown at step  324 . Detailed POS data to determine performance matrices, such as units per transaction (UPT), dollars per transaction (DPT), and transactions per hour (TPH), described further below, is uploaded from each local store system, as disclosed at step  326 . Customer traffic count data, which may also be from a local store system or from a separate sensor-based system, is uploaded, as shown at step  328 . Home office data, such as merchandising, store attribute, and human resource data for staffing information, is uploaded, as depicted at step  330 . Each data set is then written to the relational database  64  in a normalized form, as disclosed at step  331 . For each data set, a check is performed to see if the data set is to be dimensionalized and stored in the multidimensional knowledge base  66 , as shown at step  332 . If the data is not to be mirrored in the knowledge base  66 , then control advances to step  338 . If it is, than the data set is transformed to a multidimensional data set, as depicted at step  334 . The data is then stored in the knowledge base  66 , as shown at step  336 . A check is performed to determine if there are any more uploaded data sets, as shown at step  338 . If there are more data sets, control reverts to step  332 , otherwise the database server is populated for this iteration of transaction data, as shown at step  340 . 
       FIG. 7   c  shows a flowchart of the analysis of results. Referring to  FIGS. 7   c  and  4 , PIMS  12  receives a request to generate a report, as depicted at step  350 . The analysis engine  68  determines which of the reports  36  to generate, as shown at step  352 . Depending on which report, the analysis engine selectively invokes generation of the particular report, as shown at steps  360 - 368 . A detail level for the report is selected as depicted at step  370 . The detail levels include sales employee, store, district, region, division, and company. Other levels could be similarly defined or omitted. The pertinent data is fetched from the database server  62 , as disclosed at step  372 . The quantitative performance fields for the report are generated, as shown at step  374 , and the report output, as depicted at step  376 . 
       FIG. 7   d  shows a flowchart of mapping and determination of recommended actions. Referring to  FIGS. 7   c  and  4 , a performance report is requested from PIMS  12 , as depicted at step  380 . A check is performed to determine if the report concerns a subject under observation of store or employee, as shown at step  382 . The subject under observation may be, for example, one of the three tiers of sales employees: associate, advisor, or consultant, or a store (POS) location. If the subject under observation is an employee, then key employee performance measures are determined, as depicted at step  384 . If the subject under observation is a store, then key store performance measures are determined, as depicted at step  385 . For each of one or more performance measures, applicable to the subject under observation, a predetermined performance range corresponding to the performance field is retrieved, as disclosed at step  386 . The performance range indicates expected values and typical statistical deviations, as derived from performance measures previously attained and other factors. The performance measures are then analyzed in light of the performance range, as shown at step  388 , to generate a performance score. A performance ranking is determined by mapping the performance score to the performance range, as shown at step  390 , to determine a discrete bin, such as “L”, “M”, and “H” for this performance field. The performance rankings are mapped to recommended actions using a recommended action matrix, as depicted at step  392  and discussed further below with respect to  FIGS. 9   a - 9   d . The appropriate recommended actions are then implemented, as shown at step  394 , and may be implemented via curriculum and review  82  from the learning center  60 , by skill practice at the POS  84 , from managerial coaching  80 , or by a combination, depending on the particular recommended action, as depicted at steps  395 - 397 . The corresponding databases for storing the transaction data are then updated by another transaction data gathering, as shown at step  398 . 
       FIGS. 8   a - 8   j  show the standard reports  70  ( FIG. 4 ) generated by PIMS. As indicated above, each of the reports concerns a particular subject under observation, such as a particular employee or store. Referring to  FIG. 8   a , a daily productivity analysis  200  is shown. Each subject under observation, individual stores in this report, is shown in column  202 . The relevant performance fields are shown in the columns  204 , which indicate the performance scores of each subject under observation  202  for each performance field. Additional comparison fields for the last year and current week are also shown in columns  206  and  208 , respectively. Further, graphical illustrations of particular performance fields are shown. A net sales contribution  210  graph shows the relative contribution of each subject under observation, and a histogram of goal variance  212  illustrates relative achievement to facilitate comparison between subjects under evaluation. 
     For each of the performance fields  204 , the performance scores can be compared to a predetermined performance range for the particular performance field. A performance ranking of low, medium, or high (L, M, H) is derived from the relative performance score within the performance range. As indicated above, other tier structures could also be employed. For example, a net sales performance range might specify low (“L”) as 0-100, average (“A”) as 101-150, and high (“H”) as 151+. In this example, Connecticut  202   b  and Boston  202   c  have achieved an “A”, the Massachusetts/Rhode Island area have achieved an “H” ranking, and New England has achieved a “L” ranking. The ranking is employed in mapping to recommended actions, described further below. 
       FIG. 8   b  shows another productivity analysis, encompassing a weekly timeframe, in which the subject under observation is the individual sales employee. Referring to  FIG. 8   b , the weekly productivity analysis  500  includes the performance fields # of transactions  501 , Units Per Transaction  502 , and Dollars Per Transaction  504 . These fields will be discussed further below with respect to  FIGS. 9   a - 9   d.    
     Referring to  FIG. 8   c , a monthly opportunity analysis  214  is shown. For each subject  202 , columns  216  show the performance scores, and columns  218  further refine the scores on an hourly basis. A graph of the conversion percentage performance score  220  is also shown. 
       FIG. 8   d  shows a weekly opportunity analysis  214 ′ reporting the performance of individual sales employees. This report is similar to the report in  FIG. 8   c , except that the subject under observation  202 ′ is at the sales employee level, and the report covers a weekly timeframe. The report fields  218 ′ are similar to the report fields  218  in  FIG. 8   c.    
     Referring to  FIG. 8   e , a development analysis  220  is shown. This report concerns an individual subject under observation  202 , and includes performance fields shown by columns  222 . Of particular relevance are the performance scores for DPH  224 , UPT  226 , and # of transactions  228 , as these fields will be employed below in mapping to recommended actions. 
     Referring to  FIG. 8   f , a staff status report  230  is shown. This report outlines the number of sales employees at each level. The number of each level of sales employee is shown in the fields  232 , and a graph showing the relative percentage  234  is also shown. This information may be employed in developing a staffing profile. 
     Referring to  FIG. 8   g , a performance ranking  236  is shown. The performance ranking report ranks the top subjects under evaluation according one of the performance fields, net sales  240  in this case, however other fields could be employed in determining the ranking. The remaining performance fields are also shown in columns  238 . 
     Referring to  FIG. 8   h , a leader board report  242  is shown. Each of the subjects under evaluation, store locations S 1 -S 12  in this case, is shown in column  244 . The column  244  is ordered based on the net sales performance field shown in column  246 , and a histogram of the net sales  248  is also shown, thus showing relative differences between the subjects under evaluation. 
     Referring to  FIG. 8   i , a trending analysis report  250  is shown. Each of the subjects under evaluation is listed in column  252 . The net sales performance field is shown in columns  254  for both actual and goal figures, allowing comparison between the actual and goal figures along with the performance metrics SPH, DPT, UPT, and TPH. This report, in an alternate embodiment, can also show proactive customer activities and staffing profile trends. 
     Referring to  FIG. 8   j , a learning history  256  report is shown for a particular subject under observation  257 . This data comprises the learning center transaction data for indicating which curriculum have been undertaken a sales employee. A curriculum description  258  indicates the knowledge or skills that the course is intended to improve. As there may be multiple educational curricula targeted at a specific knowledge or skill, a actual course description  262  is also provided. The sequence description  260  indicates the type of sales employee that the course is designed for, associate, advisor, or consultant. The course results are displayed in columns  264 , and are employed to indicate whether satisfactory results have been achieved for the particular skill or knowledge that the course is targeted. 
     It should be noted that the performance ranking may be adjusted to allow for qualitative assessments to be made based on the quantitative reports. In the reports, one or more of the performance scores may be employed in determining a ranking for mapping the subject to the recommended actions. Graphs are generally shown for performance scores deemed to be more pertinent to a particular performance ranking, but can be generated for other performance scores as well. Further, ad hoc reports (not shown) may also be employed in determining a ranking of a particular subject under observation before mapping the subject to recommended actions. 
     The reports illustrated in  FIGS. 8   a - 8   j  are intended as illustrative; a variety of other detail levels and reporting time intervals can be provided. Appendix A illustrates further examples of reports which may be employed. In alternate embodiments, other permutations and combinations of reports may be provided; Appendix A is not intended to limit the present invention except as recited in the appended claims. 
       FIGS. 9   a - 9   d  show mapping of performance range scores to recommended actions. Referring to  FIGS. 9   a - 9   d ,  8   b  and  4 , and example of recommended action mapping is shown. An analysis of sales employee (subject under evaluation) E 1  is shown as report entry  510 . According to the productivity analysis  500 , performance scores are as follows. For Number of Transactions  501 , a score of 260. For Units Per Transaction  502 , 2.5, and for Dollars Per Transaction DPT  504 , a performance score of 104. Sales employee E 1  is of sales employee type of associate, also as indicated by entry  510 . Accordingly, the associate recommended action table  512  is employed in conjunction with the associate performance range shown in Table I. It should be noted that Table I is for illustrative purposes only; other performance ranges and standards could be employed. 
                                                                     TABLE I                           Performance Score                Low   Medium   High           (L)   (M)   (H)                            # Trans    0-10   11-50    51+           UPT     0-1.2   1.3-2.5    2.6+           DPT    0-40    41-110   111+                        
Referring to the report  500 , and to Table 1, comparing the performance score (260) with table 1 yields a rank of H (51+). For UPT, the performance score (2.5) also yields a rank of M (1.3-2.5). For DPT, the performance score of 104 yields a rank of M (41-110). Mapping the performance ranks into the recommended actions table for associates  512  indicates entry  516  (H, A, A), which determines two recommended action entries of 3 and a coaching action of “Congratulate on great job.” The coaching activity will be undertaken by the manager  80 , and the recommended action 3 maps further to a recommended action list  514 . The recommended action list  514  lists entry  518 , corresponding to curriculum  82  in sharing information to be pursued at the learning center  60 .
 
     The learning center  60  includes both a mechanism for administering the educational curricula and a mechanism for administering evaluations, or tests, which are indicative of the level of proficiency in a particular skill to which the curricula is directed at. The mechanism for administering the educational curricula is a PC platform coupled with a library of dedicated multimedia curriculum. The library of multimedia curriculum includes educational software and optional printed materials corresponding to each curriculum offering. Typically the PC platform would be located at the store (POS) and may be integrated with the POS data system. Alternately, the PC platform may be independent. 
     The tests are also provided by the learning center PC platform, and are administered to the sales employees and a resultant score stored as part of the transaction data from the learning center  60 . The test scores are used to indicate proficiency at a particular skill, which is in turn used to indicate understanding of a particular skill set. The sales employee practices the skill set on the sales floor. With satisfactory results, the manager advances the sales employee to the next sales employee level. 
     The sales employees as defined herein include three types, or levels. Other levels may be defined, to include additional levels such as pre-associate, for example. Each sales employee level has a corresponding set of skills. Advancement to the next associate level is contingent upon proficiency in the core skill set of the current associate level. The three levels are as follows. An associate is a base level sales employee who has been certified by a store manager as having completed the associate learning curriculum, outlined below. A fourth level, pre-associate, may refer to a sales employee-in-training who is in the process of attaining proficiency in the skill set of an associate. An advisor has attained proficiency in the associate level skill set and the advisor skill set, which is focused on advanced client activity as enumerated below. A consultant has attained proficiency in the associate skill set, advisor skill set and the consultant skill set, which is focused on proactive client activity. Other skill sets and sales employee advancement criteria may be defined. Skill set categories and the skills within them for each sales employee are illustrated in Tables II, III, and IV for associates, advisors, and consultants, respectively. 
     
       
         
               
               
             
           
               
                   
                 TABLE II 
               
               
                   
                   
               
               
                   
                 Associate Level 
               
               
                   
                   
               
             
             
               
                   
                 Preparing Yourself: 
               
               
                   
                 Perform Strategic Activity 
               
               
                   
                 Acknowledge Clients 
               
               
                   
                 Assess Readiness 
               
               
                   
                 Engaging Clients: 
               
               
                   
                 Conduct Passby 
               
               
                   
                 Conduct Mirroring 
               
               
                   
                 Prepare Re-Engage 
               
               
                   
                 Share Information: 
               
               
                   
                 Gather Information 
               
               
                   
                 Share Knowledge 
               
               
                   
                 Say Goodbye 
               
               
                   
                   
               
             
          
         
       
     
     
       
         
               
               
             
           
               
                   
                 TABLE III 
               
               
                   
                   
               
               
                   
                 Advisor Level 
               
               
                   
                   
               
             
             
               
                   
                 Understanding Needs: 
               
               
                   
                 Recognize Triggers 
               
               
                   
                 Unlock Triggers 
               
               
                   
                 Gather Information 
               
               
                   
                 Creating Solutions: 
               
               
                   
                 Select Products 
               
               
                   
                 Create Stories 
               
               
                   
                 Present Solutions 
               
               
                   
                 Balancing Experiences: 
               
               
                   
                 Assess Readiness 
               
               
                   
                 Disengage Client 
               
               
                   
                 Engage Client 
               
               
                   
                   
               
             
          
         
       
     
     
       
         
               
               
             
           
               
                   
                 TABLE IV 
               
               
                   
                   
               
               
                   
                 Consultant Level 
               
               
                   
                   
               
             
             
               
                   
                 Offering Services: 
               
               
                   
                 Identify Candidates 
               
               
                   
                 Offer Opportunities 
               
               
                   
                 Record Information 
               
               
                   
                 Delighting Clients: 
               
               
                   
                 Set Next Step 
               
               
                   
                 Contact Clients 
               
               
                   
                 Conduct Appointments 
               
               
                   
                 Driving Business: 
               
               
                   
                 Develop Plan 
               
               
                   
                 Measure Activity 
               
               
                   
                 Grow Business 
               
               
                   
                   
               
             
          
         
       
     
       FIG. 10  shows scaling of performance standards. Other factors may exist which affect the performance model shown above beyond the individual sales employee. For example for each of the sales employee types, there may exist several experience levels, such as beginner and advanced. Also, store volume and store location may also affect the ability of a sales employee to meet certain performance criteria. Referring to  FIG. 10 , a set of performance standards  550  is shown for each of the three sales employee types. Each set of the performance standards  550   a ,  550   b , and  550   c , corresponding to associate, advisor, and consultant, respectively, is shown with a beginner (B) set and an advanced (A) set. Each set of performance standards (Table 1) is modified by a scaling matrix  552  indicative of store characteristics which are pertinent to sales activity at the store. The characteristics are represented by scaling factors in the scaling matrix. The scaling matrix  552  has an axis representing store volume  554  and an axis representing store location type  556 . The store volume  554  indicates the relative number of sales in reference to other stores. The store location type  556  reflects relative customer profile and trading area characteristics. As shown in the matrix  552 , a sales employee working in a store located in a mall having average sales volume would be subject to a performance score scaling factor of 1.2, as shown by entry  558 . Other dimensions representing additional scaling factors may also be added to the scaling matrix. For example, a time component representing monthly or quarterly adjustments may be incorporated. Such a dimension can recognize trends such as, for example, retail sales tend to increase in December due to winter solstice holidays, followed by a slowdown in January. The sales employee&#39;s actual performance is then measured against the scaled performance standards and used to determine the L, A, H levels shown in  FIGS. 9   a - 9   c    
     Performance reports are also employed to determined optimal staffing profiles for a store, in addition to individual performance improvement and measurement as described above. Depending on store workflow, there is an optimal mix of sales employees at various levels. Some stores may depend more on proactive sales typically generated by consultants, while other stores may experience significant foot traffic. Further, factors such as the conversion percentage—the number of foot traffic customers that result in a transaction—can indicate the likelihood that a customer will generate a transaction for an associate. These and other factors displayed in the reports are used to determine a staffing profile that determines the number of associates, advisors, and consultants that should be working at a particular time of day to maximize revenue. 
     For example, a mall store may experience significant foot traffic, but few repeat customers. Therefore, the mall store may benefit from a large number of associates to accommodate the high number of foot traffic customers, but may not have a large base of repeat customers for proactive sales by consultants. Conversely, a suburban store may experience less foot traffic, and therefore need fewer associates and advisors, but may rely more heavily on proactive sales generated by the consultants. The staffing profile is employed to optimize the mix of employee types and hence the customer-focused skill sets on the floor, based on the transaction data and the POS environment. 
     Those skilled in the art should readily appreciate that the programs for performance improvement and measurement as defined herein are deliverable to a computer in many forms, including but not limited to a) information permanently stored on non-writeable storage media such as ROM devices, b) information alterably stored on writeable storage media such as floppy disks, magnetic tapes, CDs, RAM devices, and other magnetic and optical media, or c) information conveyed to a computer through communication media, for example using baseband signaling or broadband signaling techniques, as in an electronic network such as the Internet or telephone modem lines. The operations and methods may be implemented in a software executable by a processor or as a set of instructions embedded in a carrier wave. Alternatively, the operations and methods may be embodied in whole or in part using hardware components, such as Application Specific Integrated Circuits (ASICs), state machines, controllers or other hardware components or devices, or a combination of hardware, software, and firmware components. 
     While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.