Patent Publication Number: US-2018046987-A1

Title: Systems and methods of predicting fit for a job position

Description:
BACKGROUND 
     When hiring employees for a company, Human Resource (HR) departments and hiring managers have limited interactions with candidates to determine whether or not the candidate is a good fit for the company. Companies can use software tools, such as a hiring system, to assist HR departments and hiring managers in the hiring of candidates. These hiring systems can make it easier to screen and hire candidates. A recruiter or hiring manager uses a hiring system to gather data about candidates for use in manually matching candidates who may be a good fit for one or more jobs. Candidates can also be interviewed in person ensure the candidate gets along with the employees. 
     However, it is difficult to predict the compatibility of a candidate with the company based on short interactions. Personality traits of a candidate may make the candidate poorly suited for the position or likely to conflict with other employees. In addition, candidates that would be extremely successful may be dismissed for employment based on a missing keyword in a resume or a filtering process that is overly simplistic. It would be desirable to predict the future success of candidates in real time when evaluating candidates for a job opportunity. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Many aspects of the present disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, with emphasis instead being placed upon clearly illustrating the principles of the disclosure. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views. 
         FIG. 1  is a drawing of a performance analytics system according to various embodiments of the present disclosure. 
         FIGS. 2A-2C  are pictorial diagrams of example user interfaces rendered by a client device in the performance analytics system of  FIG. 1  according to various embodiments of the present disclosure. 
         FIG. 3  is a pictorial diagram of an example user interface rendered by a client device in the performance analytics system of  FIG. 1  showing an assessment report according to various embodiments of the present disclosure. 
         FIG. 4  is a pictorial diagram of another example user interface rendered by a client device in the performance analytics system of  FIG. 1  showing an assessment report according to various embodiments of the present disclosure. 
         FIG. 5  is a pictorial diagram of another example user interface rendered by a client device in the performance analytics system of  FIG. 1  according to various embodiments of the present disclosure. 
         FIGS. 6A and 6B  are pictorial diagrams of other example user interfaces rendered by a client device in the performance analytics system of  FIG. 1  according to various embodiments of the present disclosure. 
         FIG. 7  is a pictorial diagram of another example user interface rendered by a client device in the performance analytics system of  FIG. 1  according to various embodiments of the present disclosure. 
         FIG. 8  is a pictorial diagram of another example user interface rendered by a client device in the performance analytics system of  FIG. 1  according to various embodiments of the present disclosure. 
         FIG. 9  is a flowchart illustrating one example of functionality implemented as portions of a modeling service, a survey service, a data service, and an assessment service executed in a computing environment in the performance analytics system of  FIG. 1  according to various embodiments of the present disclosure. 
         FIG. 10  is a flowchart illustrating one example of functionality implemented as portions of a modeling service, a survey service, a data service, and an assessment service executed in a computing environment in the performance analytics system of  FIG. 1  according to various embodiments of the present disclosure. 
         FIG. 11  is an example performance analytics model according to various embodiments of the present disclosure. 
         FIG. 12  is a schematic block diagram that provides one example illustration of a computing environment employed in the performance analytics system of  FIG. 1  according to various embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure relates to a performance analytics system. A performance analytics system can use predictive analytics, intuitive user interfaces, and data integrations to allow recruiters and hiring managers to hire candidates more quickly and with greater confidence that the candidate will be a good fit for a particular job. 
     Generally, the performance analytics system can evaluate candidates based on a variety of inputs. One of the inputs can be employee answers to survey questions. As such, candidate answers to survey questions can be evaluated based on answers from employees to survey questions, and performance and work histories for those employees, among other inputs. Accordingly, the performance analytics system can generate a list of candidates, and identify a best fit between a candidate and a job position. A “best fit” can be determined, for example, by a prediction based on the performance analytics system performing a regression analysis on various quantitative inputs. The term “best fit” can refer to a candidate having a highest predicted score, a job position with which the candidate scores the highest predicted score, or other evaluation of a candidate&#39;s fit for a job position. 
     More specifically, the performance analytics system can include services, which can process and organize employee answers to survey questions, in combination with performance data, to create scales and/or generate weights for pre-existing scales. The performance analytics system can also analyze the inputs and scales to generate a predictive model. 
     A survey service can present survey questions to a candidate for a job position. The survey questions can be selected based on a variety of factors. In one example, survey questions for a candidate are based on what scales correlate to performance for a job position. A modeling service can calculate scores based on a predictive model and candidate answers to survey questions. Finally, an assessment service can calculate a score of a fit of a job candidate. Accordingly, the assessment service can provide a list of job candidates ranked by score. 
     It is understood that an employee can be a job candidate for a job position. Additionally, a job candidate can be an employee of a company. Further, the terms candidate and/or employee can be used to refer to a previous employee, a current employee, a perspective employee, or any other person. Further, when specifying either a candidate or an employee herein, the same functionality can be performed with respect to an employee or a candidate, respectively. As such, the usage of the terms candidate and employee are not meant to be limiting. 
     With reference to  FIG. 1 , shown is a performance analytics system  100  according to various embodiments. The performance analytics system  100  includes a computing environment  103  and one or more client device  106 , which are in data communication with each other via a network  109 . Various applications and/or functionality may be executed in the computing environment  103  according to various embodiments. The applications executed on the computing environment  103  include a modeling service  115 , a data service  118 , a survey service  121 , an assessment service  124 , and other applications, services, processes, systems, engines, or functionality not discussed in detail herein. Also, various data can be stored in a data store  112  that is accessible to the computing environment  103 . The data store  112  may be representative of multiple data stores  112  as can be appreciated. 
     The data store  112  can store industry data, company data, organization data, employee data, job position or role data, job openings, model, coefficient, and other analytics data, as can be fully appreciated based on the disclosure contained herein. The data stored in the data store  112  includes, for example, surveys  127 , scales  130 , job positions  133 , outcomes  136 , employee data  139 , candidate data  142 , and potentially other data. The scales  130  can include a value evaluating a skill, trait, attribute, competency, attribute of a job position  133 , attribute of a company, or other aspect of a user. Several example scales  130  include “Quantitative,” “Creative,” “Social,” “Organized,” “Stressful,” “Self Starting,” “Broad Thinking,” “Trust,” “Confidence,” “Precision,” Organization,” and other scales. 
     Additionally, the data store  112  can include meta data  145 , which can be generated by the modeling service  115 , manually entered by an administrator, or modified by the administrator. The meta data  145  can includes a specification  148 , coefficients  151 , and plugin code  154 . The data stored in the data store  112 , for example, is associated with the operation of the various applications and/or functional entities described below. 
     The one or more client devices  106  can be configured to execute various applications such as a survey access application  157  and/or other applications. The survey access application  157  can be executed in a client device  106 , for example, to access network content served up by the computing environment  103  and/or other servers, thereby rendering a user interface on the display  160 . To this end, the survey access application  157  can be a browser, a smart phone app, a dedicated application, or another application. The user interface can include a network page, an application screen, or another interface. The client device  106  can be configured to execute applications beyond the survey access application  157  such as, for example, email applications, social networking applications, word processors, spreadsheets, and/or other applications. 
     The client device  106  can include a processor-based system such as a computer system. Such a computer system may be embodied in the form of a desktop computer, a laptop computer, personal digital assistants, cellular telephones, smartphones, set-top boxes, music players, web pads, tablet computer systems, game consoles, electronic book readers, or other devices with like capability. The client device  106  may include a display  160 . The display  160  may comprise, for example, one or more devices such as liquid crystal display (LCD) displays, gas plasma-based flat panel displays, organic light emitting diode (OLED) displays, electrophoretic ink (E ink) displays, LCD projectors, or other types of display devices, etc. 
     The network  109  can include, for example, the internet, intranets, extranets, wide area networks (WANs), local area networks (LANs), wired networks, wireless networks, or other suitable networks, etc., or any combination of two or more such networks. For example, such networks may comprise satellite networks, cable networks, Ethernet networks, and other types of networks. 
     Regarding operation of the various components of the performance analytics system  100 , the survey service  121  can present a survey  127  to a client device  106 . The survey  127  can include survey questions, answers to survey questions (survey answers), categorical information corresponding to each survey question, and other survey related information. The categorical information can include a scale  130  that each survey question is intended to evaluate. The survey questions can be selected from a survey  127  using the survey service  121 . The surveys  127  can include survey questions and answers to survey questions (survey answers). 
     Additionally, a data service  118  can be used to correlate and populate other data stored in the data store  112 . The data service  118  can provide import, export, and data management services. Another aspect of the data service  118  is the ability to gather performance data, such as, for example, metrics representing the performance of an employee in a given job position or role. As one example, the data service  118  can receive data describing employees. The data describing the employees can be mapped to the employee data  139 . 
     The data service  118  can store data describing the employee in the employee data  139 . Specifically, data management services of the data service  118  can access employee data fields stored outside the computing environment  103 , such as organization name, organizational units, employee lists, employee groups, employee names, job codes, job titles, salaries, start dates, lengths of employment, performance reviews, and other relevant data. The performance data for employees can be used to determine a job performance metric or a job success metric. The job performance metric can be a weighed value based on performance reviews of the employee. The job success metric can be based on the performance reviews and various other factors, such as, for example, a personality profile of the employee, length of employment, organizational information, and other data. 
     The survey service  121  can present a survey  127  to a user of a client device  106 . The survey  127  can include survey questions corresponding to one or more scales  130  that relate to the user. For example, some of the survey questions can correspond to a “Job Engagement” scale  130  for the user. In one embodiment, the survey service  121  can select survey questions that correspond only to specific scales  130 . As an example, the survey service can limit the number of scales  130  that survey questions are selected from for a candidate based on the meta data  145  for a job position  133 . In this example, the number of questions in a survey  127  can be reduced by only evaluating scales  130  with the greatest impact on the outcome  136 . 
     The survey service  121  can present a series of questions from the survey  127 . The series of questions can be provided through a single page web application. The survey service  121  can receive answers to each of the series of questions to gather a survey result. The survey service  121  can provide a score for a candidate instantaneously upon receiving answers to the survey questions. As an example, upon completing a survey  127 , the survey service  121  can use the meta data  145  to generate an outcome  136  for the candidate without further user interaction required. The survey service  121  can present the time elapsed and a progress of the survey  127  or a task within the survey  127 . In one example, the survey service  121  gathers survey results from some or all employees at a company. 
     The survey service  121  can facilitate the creation of survey  127 . Different surveys  127  can be created for different job positions  133 . The survey service  121  can select survey questions from a bank of questions within surveys  127 . The selection can be based on various factors, such as, for example, a score of how important each scale  130  is for a job position  133 . In one example, the company can select competencies from a pool, and the survey service  121  can select survey questions based on the company selections. The assessment service  124  can benchmark and evaluate the predicted outcomes  136  to determine a success rate of the assessments, such as, for example, the success of a predictive model that is based on the company selections. The importance of each scale  130  can be determined based on the meta data  145 . The survey service  121  can receive a selection of survey questions from the bank of questions through an administrative user interface. By creating a survey  127  for a particular job position  133 , one or more scale  130  can be used to determine a success profile for the job by which potential candidates can be evaluated. The success profile can be based on personality traits, motivators, soft skills, hard skills, attributes, and other factors or scales  130 . 
     According to one example, the survey service  121  selects survey questions corresponding to a “Preference for Quantification” scale  130  from the bank of questions within surveys  127 . The selection of the “Preference for Quantification” scale  130  can be selected for surveys  127  to evaluate candidates. The selection of the “Preference for Quantification” scale  130  can occur in response to determining a correlation between the “Preference for Quantification” scale  130  and performance in a job position  133 , such as, for example, when generating the meta data  145 . The assessment service  124  can use answers to the selected survey questions to evaluate the “Preference for Quantification” scale  130  for a user. The modeling service  115  can use the scale  130  for the user to evaluate an importance of the “Preference for Quantification” scale  130  for a job position  133 . 
     In one embodiment, the survey service  121  can generate user interfaces to facilitate the survey  127  of a user of a client device  106 . As an example, the survey service  121  can generate a web page for rendering by the survey access application  157  on the client device  106 . In another example, the survey service  121  can send the survey questions to the survey access application  157 , and the survey access application  157  can present the questions on the display  160 . 
     The modeling service  115  can generate a predictive model based on various data. The modeling service  115  can store data describing the model within meta data  145 . The modeling service  115  can calculate the predictive model by analyzing the employee data  139  and scales  130 . As such, the modeling service  115  can provide a step-wise modeling feature, a reduced step-wise modeling feature, a linear modeling feature, and other modeling features. Accordingly, the modeling service  115  can create a predictive model that can be used by the computing environment  103  to generate predictions of likely outcomes  136  for candidates. By creating a predictive model that can be used to grade candidates, a validated fit between a candidate and a job position can be determined based on a success profile for the candidate. 
     The modeling service  115  can create the meta data  145 . As an example, meta data  145  can include specification  148 , coefficients  151 , and plugin code  154 . A specification  148  includes the data elements that define a predictive model, including various constants and relevant relationships observed between data elements. The modeling service  115  can create the specification  148  including a model definition, such as, for example, performance analytics model  1100  in  FIG. 11 . The model definition can be in any suitable format (e.g., a JSON format, other open-source XML format, or other proprietary format). 
     The coefficients  151  can be defined by a name and value pair. The coefficients  151  can individually correspond to a particular scale  130 . As a non-limiting example, a coefficients  151  for scales  130  related to a particular “Sales” job position  133  can have a name series of “Leadership,” “Networking,” “Prospecting,” “Negotiation,” “Dedication,” “Sales Strategy,” “Teamwork,” “Business Strategy,” “Problem Solving,” and “Discipline.” In another example, a coefficients  151  for scales  130  related to a particular healthcare job position  133  can have a name series of “Simplifying Complexity,” “Business Strategy,” “Physician Communication,” “Patient Focused Care,” “Computers,” “Multitasking,” “Competitive Research,” and “Medical Products,” or other names. Each name of a coefficient  151  can have an associated value containing a real, rational number. 
     Another operation of the computing environment  103  is to calculate a predicted outcome  136  for a candidate applying for a job position  133 . An outcome  136  can relate to the result of the assessment service  124  applying a predictive model to a candidate. For example, the assessment service  124  can apply a predictive model to the answers to the survey questions provided by the candidate to generate an outcome  136  for the candidate. In another example, the outcome  136  for a job position  133  can be determined without the candidate applying for the job position  133 . In one embodiment, the candidate can be an employee within the organization. For example, answers to survey questions from employees can be used to evaluate the employees for a job position  133  after the modeling service  115  generates the meta data  145  corresponding to that job position  133 . Thus one operation of the computing environment  103  can be to calculate multiple predictive outcomes  136 , based on a predictive model, for an employee within the organization for multiple job positions  133 . 
     The plugin code  154  is executed by the computing environment  103  to provide certain customization features for standard and non-standard employee job positions  133 . The plugin code  154  can be input by a user or generated by the modeling service  115 . For example, certain industries have special job positions  133  that require a customized candidate grading system. The plugin code  154  can execute custom logic when evaluating a candidate. The plugin code  154  can be executed by the assessment service  124  to modify or extend the predictive model. 
     The assessment service  124  can generate a score predicting a fit for a candidate in a job position  133  and store the score as an outcome  136 . The assessment service  124  can also score candidates based on a number of different inputs including meta data  145 . The assessment service  124  can score candidates based on a candidate&#39;s answers to survey question, in combination with a predictive model previously described, according to a specification  148  and coefficients  151 . As an example, the assessment service  124  or the survey service  121  can score the candidate on one or more scales  130  based on the candidate&#39;s answers to the survey questions. 
     The assessment service  124  can determine an outcome  136  predicting a fit of the candidate in the job position  133  based on multiply coefficients  151  by the respective scale scores calculated from the answer from the candidate. The assessment service  124  can determine and provide outcomes  136  for one or more candidates. The assessment service  124  can generate a user interface with a ranked list of job candidates ranked by scores. 
     In one embodiment, the client device  106  runs a survey access application  157  to provide a mechanism for an employee or candidate to read survey questions from a display  160 . Thus, an employee or candidate can use the survey access application  157  to answer survey questions selected by the survey service  121 . The questions answered by the employee or candidate using the survey access application  157  can be from a survey  127 . The survey answers can be evaluated based on the meta data  145  including the specifications  148  and the coefficients  151 . For example, a candidate can answer questions related to a multiple scales  130  including Patient Focused Care. Thus, an outcome  136  for the candidate performance can be determined using the meta data  145 . 
     A data service  118  can receive employee data  139  describing an employee at a company. A survey service  121  can receive answers to survey questions from the employee using the survey access application  157 . A survey service  121  can calculate scales  130  for the employee based on the answers to survey questions. In one example, the scales  130  can also be based on the employee data  139 . The modeling service  115  can generate meta data  145  for a performance analytics model based on the scales  130  and employee data  139 . The survey service  121  can receive candidate data  142  including candidate answers to survey questions from a job candidate. The assessment service  124  can calculate scale scores for the job candidate based on the candidate answers. Finally, an assessment service  124  can predict an outcome  136  of a fit of the job candidate based on the scale scores for the candidate and the performance analytics model. 
     Turning now to  FIGS. 2A-2C , shown are examples of user interfaces depicting a score for a candidate, such as an outcome  136 . With reference first to  FIG. 2A , shown is a user interface diagram depicting an example of a portion of an assessment report generated by assessment service  124  rendered on display  160 . The assessment report can include a textual element  203  representing a score for a given candidate, as well as a graphical element  206  corresponding to the score. In the embodiment, the textual element displays a raw score, and the graphical element  206  displays a radial chart. In another embodiment, the textual element could be an average score, a standard deviation, or some other score. Additionally, the graphical element could be a pie chart, bar chart, histogram, or some other graphical representation. 
     With reference to  FIG. 2B , shown is a user interface depicting a portion of an assessment report, this time showing a bar chart of candidate performance for several scales  130 , with performance grouped by competency type  209  (e.g., Soft and Interpersonal). Each competency can be further broken down by the categories of Experience, Skills, and Preference, or other categories. The bar indicator  212  can represent a scale  130  of a candidate for a Preference category of the Soft and Interpersonal competency type  209 , and can accordingly be used to effectively score or screen a particular candidate for a job position  133 . 
     Finally,  FIG. 2C  shows a user interface depicting yet another portion of an assessment report including a personality outline screen of an assessment report, broken down by a given personality attributes of a candidate. In this embodiment, personality attributes are shown on a scale between low, average, and high. The personality attribute  215  represents a score for a “Dedication” personality attribute. In this embodiment, the personality attribute  215  can be compared to an average score, an ideal score, or some other score. The vertical indicator  218  (e.g., High) can represent a candidate&#39;s score compared to a standard deviation for that personality attribute. 
     Referring next to  FIG. 3 , shown is another user interface diagram depicting an example of a portion of an assessment report generated by assessment service  124  rendered on a display  160 . In a first section of the user interface (e.g., Interpersonal Skill Levels), skill levels are plotted in a bar chart. Skill Details are presented in a section of the user interface in a list by Competency Name, along with a score for each of an Experience, a Skills, and a Preference, where the score shown may range from “Low,” “Moderate,” and “High.” In this example, a bar indicator  303  represents a score for a candidate grouped by interpersonal skills according to candidate preference, on a level from one to five. 
     The bar indicator  303  can represent a five-point range (e.g., Likert range), some other range, or some other representation of a score. In another example, each skill can be grouped by a competency. The example of  FIG. 3  shows a competency of “Complex problem solving” with candidate performance detailed across “Experience,” “Skills,” and “Preference.” Here, a preference indicator  309  indicates a candidate preference for complex problem solving is high, which can indicate a good fit between a candidate and a job position  133  that includes complex problem solving. As an example, an outcome  136  can be higher for a first candidate that has a high complex problem solving in contrast to a second candidate with a low complex problem solving when a complex problem solving scale  130  correlates to performance for a job position  133 . 
     Referring next to  FIG. 4 , shown is another user interface diagram depicting an example of a portion of an assessment report generated by assessment service  124 . In this embodiment, a personality details report shows a table of trait names, trait descriptions, and level for a particular candidate. Thus, a name  403  for a scale  130  (e.g., Grit) can be presented near a textual value indicating the candidate&#39;s score for that scale  130  (e.g.,  79 ). A description  406  can also be provided to help explain the given scale  130 . A visual indicator  409  for a scale score can be presented corresponding with the textual value for the scale score, using a temperature gauge, a radial chart, a bar chart, or some other graphical representation of a score. 
     Referring next to  FIG. 5 , shown is a user interface diagram depicting an example of a survey generated by a survey service  121  accessed by a survey access application  157  and rendered on display  160 . Here, a candidate or an employee (a “client”) has accessed a survey using client device  106 . In this embodiment, a task progress indicator  503  shows the progress of the client through the survey. The task progress indicator  503  could indicate progress based on progress through a static number of pages, or alternatively, the task progress indicator  503  could indicate progress based on answers to survey questions (e.g., an adaptive test). Survey questions (e.g.,  150  questions) have been prepared for the client. The client can tap, press, or otherwise manipulate the user interface to answer the questions presented. Client answers to survey questions are collected through survey access application  157  and can be stored as employee data  139 , candidate data  142 , or both. The example interface shown in  FIG. 5  allows the client to answer survey questions using a five-point responsive range  506  (e.g., Likert range), although the responsive range could also be a three-point range, a seven-point range, other range, or other input methods (e.g., text fields). A client can use the interface to navigate through a survey (e.g., using a back button to go to a previous page listing survey questions). Accordingly, the interface shown in  FIG. 3  allows a client to start a survey, select survey questions, provide answers to survey questions, navigate, and track progress. Additionally, the interface shown can also be used to collect responses from candidates or other people. 
     Referring to  FIG. 6A , shown is a user interface diagram depicting an example of an assessment report generated by assessment service  124  rendered on display  160  according to  FIG. 1 . In this embodiment, a hiring manager or other administrator has performed a search for candidates and the performance analytics system has returned a list of candidates. The list of candidates is presented in a table that includes data elements relating to that particular candidate (e.g., candidate name, candidate title, date, candidate status) along with a candidate grade. The candidate grade in this embodiment presents as a textual element  603 , but in other embodiments it can present as a graphical element (e.g., a graph detailing statistical properties of scores of fit of candidates for jobs), or in other ways as can be readily understood. Additionally, a search box  606  allows searching on many data elements and is not limited to those data elements enumerated above. The user interface diagram can include a  609  graph detailing statistical properties for the candidates. As shown, the assessment report can be sorted by candidate status, but it could be sorted by other data elements. Accordingly, a hiring manager can use the example user interface of  FIG. 6A  to create a list of candidates who may be a good fit for a job. 
     With reference to  FIG. 6B , shown is a user interface diagram depicting an example of a candidate compatibility report generated by assessment service  124  rendered on display  160  according to  FIG. 1 . In this embodiment, a hiring manager or other administrator can view a list of potential job positions  612   a - e  for a single candidate or employee. The outcomes  136  for multiple job positions  133  can be determined without the candidate applying for each of the job positions  133 . As an example, a hiring manager can select to evaluate a candidate for multiple job positions  133 . The assessment service  124  can generate a predicted outcome  136  for each of the job positions  133  for the candidate. 
     The list of potential job positions  133  can be presented in a graph that includes a candidate grade of the candidate, such as an outcome  136 , for each of the job positions  612   a - e . In one embodiment, the assessment service  124  can automatically score all employees and candidates for all job positions  133 . As an example, the assessment service  124  can score all employees and candidates for all job positions  133  without receiving a request from a hiring manager to evaluate a candidate. Accordingly, a hiring manager can use the example user interface of  FIG. 6B  to view a compatibility report for a candidate in view of multiple job positions  133 . 
     Referring next to  FIG. 7 , shown is a user interface diagram depicting an example of a portion of an assessment report generated by assessment service  124  rendered on display  160 . This example report depicts, among other things, a way to view a particular candidate&#39;s overall grade  703  together with his or her individual grades broken down by scale  130 . The user interface for an “Organization” attribute  706  can display a textual element and a graphical element representing the score of a candidate relating to organizational skills. Here, the user interface has presented information (e.g., a description for scale score types) about the organizational skills to describe the professional fit and potential risks characteristics of the organizational skill. In this embodiment, a hiring manager or other administrator can send a message to the candidate, assign the candidate more tests, rank the candidate, assign tags to the candidate, or perform various other features as disclosed herein. 
     With reference next to  FIG. 8 , shown is a user interface diagram depicting an example of a survey generated by a survey service  121  accessed by a survey access application  157  and rendered on display  160 . Here, an employee has accessed a survey  127  using a client device  106 . Survey questions (e.g.,  150  questions) have been prepared for the employee to help assess the employee&#39;s fit for either the employee&#39;s current position, a different position, or both. 
     The example interface shown in  FIG. 8  allows an employee to answer survey questions using a five-point responsive range  803  (e.g., Likert range), although the responsive range could also be a three-point range, a seven-point range, other range, or other input methods (e.g., text fields). An employee can use the interface to navigate through a survey  127  (e.g., using a back button to go to a previous page listing survey questions). Accordingly, the interface shown in  FIG. 8  facilitates an employee to starting a survey  127 , providing answers to survey questions, navigating a survey  127 , and tracking progress through the survey  127 . The answers to survey questions can be stored by survey access application  157 . 
     Referring next to  FIG. 9 , shown is a flowchart that provides one example of the operation of a portion of the performance analytics system  100  according to various embodiments. It is understood that the flowchart of  FIG. 9  provides merely an example of the many different types of functional arrangements that may be employed to implement the operation of the portion of the performance analytics system  100  as described herein. As an alternative, the flowchart of  FIG. 9  may be viewed as depicting an example of elements of a method implemented in the computing environment  103  ( FIG. 1 ) according to one or more embodiments. 
     Beginning with box  903 , the computing environment  103  receives employee answers to survey questions. As described above, the employee may answer survey questions using a survey access application  157 . Employee answers may be gathered using a five-point responsive range (e.g., Likert range), three-point range, seven-point range, or another input method. 
     At box  906 , a modeling service  115  or survey service  121  calculates scales  130  based on employee answers. The scale  130  can be based on a number of questions answered affirmatively that correspond to the scale  130 . 
     At box  909 , the modeling service  115  creates a predictive model that includes the specification  148  of a preferred candidate. The modeling service  115  can generate a predictive model including meta data  145 . The predictive model can be based, among other things, on employee data  139  and scales  130 . The meta data  145  of the preferred candidate, or the specification  148 , can include a model definition in any suitable format (e.g., a JSON format, other open-source XML format, or other proprietary format). 
     At box  912 , the survey service  121  receives candidate answers to survey questions. The candidate can answer survey questions using a survey access application  157 . Candidate answers can be gathered using a five-point responsive range (e.g., Likert range), three-point range, seven-point range, or other input method. In some embodiments, a candidate can save a survey  127  and later resume the survey  127  using the survey service  121 . 
     At box  915 , the assessment service  124  calculates a score based on candidate answers to survey questions. For example, the assessment service  124  can calculate an outcome  136 . The calculation of the score can be performed in a number of different ways. The score can be based on a comparison of candidate answers to with a specification  148 . In one example, a number of employees within a given organization provided answers to survey questions, which are used to generate the specification  148 . Thus, a score can be based on a comparison of scales  130  for a candidate to scales  130  of one or more employees. Additionally, in another embodiment, a score can be based on objective criteria such as a comparison of candidate answers to correct answers. This embodiment can be useful for determining a candidate&#39;s proficiency in a particular knowledge area. 
     At box  918 , the assessment service  124  generates a ranked list of candidates. The results from one or more candidates taking a survey can be stored as candidate data  142  in the data store  112 . A hiring manager or some other person can navigate a user interface (see, e.g.,  FIG. 6 ) to display a ranked list of candidates on a client device  106 . One objective of displaying a ranked list of candidates is to identify a candidate who is a best fit for a job position  133 . Thus, the ranked list can display, from among all the candidates with candidate data  142  in the data store  112 , only those candidates who meet certain criteria. In this way, the number of potential candidates can be reduced by only evaluating the candidates who are the best fit with a job. The candidates can also be filtered based on which candidates are currently seeking employment and other factors configured to the needs and requirements of the end user. 
     Referring next to  FIG. 10 , shown is a flowchart that provides another example of the operation of a portion of the performance analytics system  100  according to various embodiments. It is understood that the flowchart of  FIG. 10  provides merely an example of the many different types of functional arrangements that may be employed to implement the operation of the portion of the performance analytics system  100  as described herein. As an alternative, the flowchart of  FIG. 10  may be viewed as depicting an example of elements of a method implemented in the computing environment  103  ( FIG. 1 ) according to one or more embodiments. 
     In one embodiment of the system, the performance analytics system  100  is used to assign training for an employee. At box  1003 , the assessment service  124  can receive a request to determine training for an employee. In one example, a trainer submits a request to find a threshold quantity of employees required to offer a specific training program that corresponds to improving one or more scale  130 . In another embodiment, a user can submit a request to determine what training materials to offer employees. In yet another embodiment, an employee can request a ranked list of training programs that would provide the biggest improvement to an outcome  136  of the employee for a current or potential job position  133  of the employee. 
     At box  1006 , the assessment service  124  identifies a target scale score for training for an employee. According to some examples, the assessment service  124  iterates through each scale  130  for the employee and calculates a theoretical outcome  136  for the employee if the scale  130  from the current iteration were greater by a predefined amount. Then, the assessment service  124  identifies the scale  130  that improved the outcome  136  by a greatest amount as the target scale score. The iterations can be limited to scales  130  corresponding to training courses currently offered. Further, the predefined amount from the calculation of the theoretical outcome  136  can be different for each scale  130 , such as, for example, based on a projected improvement to the scale  130  from a training course. In another example, the assessment service  124  identifies the target scale score as the scale  130  that corresponds to a greatest coefficient  151 . 
     A survey  127  can be given to participants in training programs before and after to evaluate the improvement of the participant on given scales. When determining a target scale score for training, the predefined amount added to a scale  130  of the employee can be based on the improvement of past participants. As an example, the employee may be projected to improve “Confidence” by 12 points by taking a training program entitled “Lead with Confidence,” but only improve “Multitasking” by 2 points by taking “Secrets to Multitasking,” where the projections are based on the past improvements of participants taking the training programs. However, in one example, the target scale score can still be “Multitasking” scale  130  if adding 2 points improves the outcome  136  by a greater amount than adding 12 to the “Confidence” scale  130 . 
     At box  1009 , the assessment service  124  assigns a training program to an employee. The assessment service  124  can assign a training program corresponding to the target scale score to the employee. In one embodiment, the assessment service  124  can assign training to a threshold quantity of employees for a training program. In another embodiment, the assessment service  124  can schedule training programs for a company based on which target scale scores are identified for employees within the company. 
     Turning to  FIG. 11 , shown is a performance analytics model  1100  according to various embodiments of the present disclosure. The performance analytics model  1100  includes subcomponents  1103   a ,  1103   b , and  1103   c . Each of the subcomponents can include a coefficients section  1106  and a statistical data section  1109 . The coefficients section  1106  can include one or more coefficients  151 , each of which can include a name value pair. The coefficients  151  contained in a given coefficients section  1106  can be based on whether a correlation, partial correlation or other statistical relationship exists between a scale  130  and performance data in employee data  139 . As a non-limiting example, if a scale  130  that corresponds to years of experience strongly correlates as a dependent variable on predicting an independent variable of skill for a job position  133 , then a “abc_personal_exp” coefficient  151  can be included with a value of 8 in a coefficients section  1106  that is used to predict skill for the job position  133 , as shown in subcomponent  1103   c.    
     Moving on to  FIG. 12 , shown is a schematic block diagram of the computing environment  103  according to an embodiment of the present disclosure. The computing environment  103  includes one or more computing devices  1203 . Each computing device  1203  includes at least one processor circuit, for example, having a processor  1215  and a memory  1212 , both of which are coupled to a local interface  1218 . To this end, each computing device  1203  may comprise, for example, at least one server computer or like device. The local interface  1218  may comprise, for example, a data bus with an accompanying address/control bus or other bus structure as can be appreciated. 
     Stored in the memory  1212  are both data and several components that are executable by the processor  1215 . In particular, stored in the memory  1212  and executable by the processor  1215  are list of main applications, and potentially other applications. Also stored in the memory  1212  may be a data store  112  and other data. In addition, an operating system may be stored in the memory  1212  and executable by the processor  1215 . 
     It is understood that there may be other applications that are stored in the memory  1212  and are executable by the processor  1215  as can be appreciated. Where any component discussed herein is implemented in the form of software, any one of a number of programming languages may be employed such as, for example, AJAX, C, C++, C#, Objective C, Java®, JavaScript®, Perl, PHP, Visual Basic®, Python®, Ruby, Flash®, or other programming languages. 
     A number of software components are stored in the memory  1212  and are executable by the processor  1215 . In this respect, the term “executable” means a program file that is in a form that can ultimately be run by the processor  1215 . Examples of executable programs may be, for example, a compiled program that can be translated into machine code in a format that can be loaded into a random access portion of the memory  1212  and run by the processor  1215 , source code that may be expressed in proper format such as object code that is capable of being loaded into a random access portion of the memory  1212  and executed by the processor  1215 , or source code that may be interpreted by another executable program to generate instructions in a random access portion of the memory  1212  to be executed by the processor  1215 , etc. An executable program may be stored in any portion or component of the memory  1212  including, for example, random access memory (RAM), read-only memory (ROM), hard drive, solid-state drive, USB flash drive, memory card, optical disc such as compact disc (CD) or digital versatile disc (DVD), floppy disk, magnetic tape, or other memory components. 
     The memory  1212  is defined herein as including both volatile and nonvolatile memory and data storage components. Volatile components are those that do not retain data values upon loss of power. Nonvolatile components are those that retain data upon a loss of power. Thus, the memory  1212  may comprise, for example, random access memory (RAM), read-only memory (ROM), hard disk drives, solid-state drives, USB flash drives, memory cards accessed via a memory card reader, floppy disks accessed via an associated floppy disk drive, optical discs accessed via an optical disc drive, magnetic tapes accessed via an appropriate tape drive, and/or other memory components, or a combination of any two or more of these memory components. In addition, the RAM may comprise, for example, static random access memory (SRAM), dynamic random access memory (DRAM), or magnetic random access memory (MRAM) and other such devices. The ROM may comprise, for example, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other like memory device. 
     Also, the processor  1215  may represent multiple processors  1215  and/or multiple processor cores and the memory  1212  may represent multiple memories  1212  that operate in parallel processing circuits, respectively. In such a case, the local interface  1218  may be an appropriate network that facilitates communication between any two of the multiple processors  1215 , between any processor  1215  and any of the memories  1212 , or between any two of the memories  1212 , etc. The local interface  1218  may comprise additional systems designed to coordinate this communication, including, for example, performing load balancing. The processor  1215  may be of electrical or of some other available construction. 
     Although the performance analytics system  100 , and other various systems described herein may be embodied in software or code executed by general purpose hardware as discussed above, as an alternative the same may also be embodied in dedicated hardware or a combination of software/general purpose hardware and dedicated hardware. If embodied in dedicated hardware, each can be implemented as a circuit or state machine that employs any one of or a combination of a number of technologies. These technologies may include, but are not limited to, discrete logic circuits having logic gates for implementing various logic functions upon an application of one or more data signals, application specific integrated circuits (ASICs) having appropriate logic gates, field-programmable gate arrays (FPGAs), or other components, etc. Such technologies are generally well known by those skilled in the art and, consequently, are not described in detail herein. 
     The flowcharts of  FIGS. 9 and 10  show the functionality and operation of an implementation of portions of the application. If embodied in software, each block may represent a module, segment, or portion of code that comprises program instructions to implement the specified logical function(s). The program instructions may be embodied in the form of source code that comprises human-readable statements written in a programming language or machine code that comprises numerical instructions recognizable by a suitable execution system such as a processor  1215  in a computer system or other system. The machine code may be converted from the source code, etc. If embodied in hardware, each block may represent a circuit or a number of interconnected circuits to implement the specified logical function(s). 
     Although the flowcharts of  FIGS. 9 and 10  show a specific order of execution, it is understood that the order of execution may differ from that which is depicted. For example, the order of execution of two or more blocks may be scrambled relative to the order shown. Also, two or more blocks shown in succession in  FIGS. 9 and 10  may be executed concurrently or with partial concurrence. Further, in some embodiments, one or more of the blocks shown in  FIGS. 9 and 10  may be skipped or omitted. In addition, any number of counters, state variables, warning semaphores, or messages might be added to the logical flow described herein, for purposes of enhanced utility, accounting, performance measurement, or providing troubleshooting aids, etc. It is understood that all such variations are within the scope of the present disclosure. 
     Also, any logic or application described herein, including a performance analytics system  100 , that comprises software or code can be embodied in any non-transitory computer-readable medium for use by or in connection with an instruction execution system such as, for example, a processor  1215  in a computer system or other system. In this sense, the logic may comprise, for example, statements including instructions and declarations that can be fetched from the computer-readable medium and executed by the instruction execution system. In the context of the present disclosure, a “computer-readable medium” can be any medium that can contain, store, or maintain the logic or application described herein for use by or in connection with the instruction execution system. 
     The computer-readable medium can comprise any one of many physical media such as, for example, magnetic, optical, or semiconductor media. More specific examples of a suitable computer-readable medium would include, but are not limited to, magnetic tapes, magnetic floppy diskettes, magnetic hard drives, memory cards, solid-state drives, USB flash drives, or optical discs. Also, the computer-readable medium may be a random access memory (RAM) including, for example, static random access memory (SRAM) and dynamic random access memory (DRAM), or magnetic random access memory (MRAM). In addition, the computer-readable medium may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other type of memory device. 
     Further, any logic or application described herein, including list of main applications, may be implemented and structured in a variety of ways. For example, one or more applications described may be implemented as modules or components of a single application. Further, one or more applications described herein may be executed in shared or separate computing devices or a combination thereof. For example, the applications described herein may execute in the same computing device  1203 , or in multiple computing devices in the same computing environment  1200 . Additionally, it is understood that terms such as “application,” “service,” “system,” “engine,” “module,” and so on may be interchangeable and are not intended to be limiting. 
     Disjunctive language such as the phrase “at least one of X, Y, or Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present. 
     It should be emphasized that the above-described embodiments of the present disclosure are merely possible examples of implementations set forth for a clear understanding of the principles of the disclosure. Many variations and modifications may be made to the above-described embodiment(s) without departing substantially from the spirit and principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure and protected by the following claims.