HR Analytics & Reporting

HR Analytics & ReportingTrendwise Analytics

Trendwise Analytics

ContentsAbout Trendwise analyticsBackground and objectives Need of HR analytics & reportingTrendwise Analytics HR analytics capabilitiesHR Reporting & Analytics Level-1Dashboards & Descriptive analysisHow to use Level -1 analysis for making business decisionsHR Reporting & Analytics Level-2Derived Metrics & RatiosHow to use Level -2 analysis for making business decisionsHR Reporting & Analytics Level-3Attrition forecastingAttrition segmentation & Hotspot identificationTop performer segmentationCompensation analysis & Fair compensation toolVoice of employee analysis & drivers of employee satisfaction

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About Trendwise analytics

Trendwise Analytics

Services and Technology

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Background and objectives Need of HR analytics & reportingMany organizations have high quality HR data (residing with a multitude of systems, such as the HRMS, performance management, learning, compensation, survey, etc.) but still struggle to use it effectively to predict workforce trends, minimize risks and maximize returns.

The costs of attrition, poor hiring, sub-optimal compensation, keeping below par employees, bad training & learning strategies are just too high

Data-driven insights to make decisions are always better than judgmental (subjective) HR practices in terms of how to recruitwhom to hire how to onboard and train employeeshow they keep employees informed and engaged through their tenure with the organization Hence regular tracking and prediction of crucial HR metrics is indispensable ObjectivesPredict attrition especially amongst high performers.Forecast the right fitment for aspiring employeePredict how compensationvalueswill pan out.Establish linkages between Employee engagement score and C-Sat scores(Work in progress)

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Trendwise Analytics HR analytics capabilitiesThree levels of HR analytics and reporting

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HR Reporting and Analytics: Level-1HR Dashboards & Descriptive analysis Basic frequencies & percentages of some HR related variables Head count and Attrition numbers by Region ,Country, Business, Process, Service centers, Grade of service ,Age ,Gender , Ethnicity, Tenure and Special segment (e.g. Ratings/Talents)Training and learning dashboards, Program Enrollment / Registration & CompletionPerformance tracking reports , Absences ,Event Grievances / Disciplinary Actions Employee Appraisal / Review / AccomplishmentsRequisition tracking, Vacancy / skills matching / competenciesPayroll related reports, Injury illness, Time and laborAll the above reports will generated using SAS procedures like PROC FREQ, UNIVARAITE, MEANS etc.,. Automation of all these reports using SAS/REPORT to generate monthly dashboards in desired format

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How to use Level-1 analysis?ReportsInsightsAction points

Turnover rates are above acceptable levels in last two quartersCompensation and location shift are two main reasons Revise compensation strategies, time to concentrate on incentives and employee retention strategies

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HR Reporting and Analytics: Level-2HR metrics and ratiosHR operational metrics will help us tracking the efficiency of various functions in HR department. We can define control limits to each of these metrics and track them on regular basisTurnover ratio(Number of attritions in a year)/ (Average head count in a year)Joiners rate(Accession ratio)(Number of joiners in a year)/ (Average head count in a year)Stability index(Number of FTE with >3 years tenure in current organization)/ (Current head count)Low performer management Denominator : Employees with low performance rating in last yearNumerator: Distribution of above employees across Improved performance rating in current yearSame performance rating in current yearLeavers in current yearPromotion ratio(Number of promotions in a period of time)/ (Average head count over same period)

A high number indicates hidden costs and delays, which damage productivityJoiners Rate: The ratio of new and replacement hires as the percentage of total employmentMetricInsightsAction pointsFocus on new hire and employee retention strategies

How to use Level-2 analysis?

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Availability historical HR data gives us lot of scope to analyze past patterns and predict future behaviors Attrition forecasting : Given historical attrition trends, we can estimate future attrition percentages up to a certain confidence levelAttrition Segmentation : Segmentation will be done based on employee profiles & attrition rates. Most impacting employee characteristics on attrition will be identified Top performer segmentation: Segmentation of employees based on their profile data and performance indices. This will help us to identify top performing employees and their characteristicsCompensation Analysis and compensation tool: A tool that predicts optimal compensation for a given employee based on his capabilities, company policies, market conditions. New hire strategies: New hire strategies will be build by performing attrition segmentation in combination with top performer analysis Voice of employee analysis & drivers of employee satisfaction

HR Reporting and Analytics: Level-3

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HR Reporting and Analytics: Level-3 Attrition forecastingPredicting/forecasting near future attrition numbers by identifying patterns in historical attrition data

illustration

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HR Reporting and Analytics: Level-3 Attrition segmentationIdentifying segments with high/low attrition rates and employee characteristics in each segment FTE Segment with highest Attrition %FTE Segment with least Attrition %illustration

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HR Reporting and Analytics: Level-3 Top performer segmentationIdentifying High /Low performing employee segments and their characteristics (subjected to availability of necessary performance measures) FTE Segment with High % of top performersFTE Segment with least % top performersillustration

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HR Reporting and Analytics: Level-3 Fair compensation toolProject StageDescriptionStage-1Divide overall compensation into four major components; Company, Employee, Market and general followed by identification of top drivers in each quadrantStage-2Study historical data to find the relation between compensation and attributes in each quadrant , using SASStage-3Use predictive analysis in SAS(multiple linear regression) to quantify the relation between compensation and attributesStage-4Using above models, build a fair compensation prediction tool that covers all the relevant attributes from each quadrantStage-5Use the results obtained from predictive analysis to estimate the optimal compensation for a given employee

ApproachList main drivers of compensation, find the impact of each of these on compensation using historical data, use these models and build a tool that predicts compensation

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HR Reporting and Analytics: Level-3 Fair compensation tool & algorithm

gap between Maximum and minimum salaryCompany 30%Employee 35%Market 25%Others 10%Divided into four quadrants based on weightsBudgetUrgencyImpactIdentifying top attributes in each quadrantsCompany component in final compensationAssign weights to each of these components based on statistical analysis of historical dataFinal CompensationDo the same excessive for four quadrants

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HR Reporting and Analytics: Level-3 Voice of employee survey analysis & drivers of satisfactionReporting: Descriptive statistics like overall satisfaction, satisfaction by various cuts(regions, processes etc.,)Driver Analysis (part-1): Identification of main drivers of employee satisfaction based on survey dataE.g: If we have five sub questions in survey, we try to identify the top two factors which are impacting overall employee satisfaction. We find out these by using multivariate logistics regressionDriver Analysis (part-2): Merging of survey responders data with employee profile and performance data. Identification of main drivers of satisfaction from non surveyed variablesE.g ; We consider variables like employee tenure with the company, employee performance, skill sets & some other demographic variables to see weather one or more of these are impacting on overall employee satisfactionAnalysis of verbatim comments:Descriptive analysis of positive , negative and neutral commentsIdentification of frequently mentioned topics and their positive negative frequencies

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Predictive Analysis using SAS- Examples with dummy data Attrition forecasting using SASAttrition segmentation using SAS

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SIMPLE ATTRITION FORECASTING MODEL

Data:

Month Attrition%

Jan-09 1.0%

Feb-09 2.7%

Mar-08 3.1%

Apr-08 1.1%

May-08 3.3%

Jun-08 4.0%

Jul-08 1.0%

Aug-08 1.5%

Sep-08 3.0%

Oct-08 1.9%

Nov-08 2.1%

Dec-08 3.0%

Jan-09 0.9%

Feb-09 2.9%

Mar-09 3.2%

Apr-09 0.5%

May-09 2.0%

Jun-09 4.0%

Jul-09 0.5%

Aug-09 1.5%

Sep-09 2.5%

Oct-09 0.2%

Nov-09 1.0%

Dec-09 2.0%

Jan-10 1.0%

Feb-10 1.0%

Mar-10 1.9%

Apr-10 1.0%

May-10 2.5%

Jun-10 3.9%

Jul-10 0.8%

Aug-10 1.5%

Sep-10 3.3%

Oct-10 1.0%

FORECASTING USING SAS:

Specify data set and develop time series model

Model goodness of fit

Parameters of the model

Forecasts

Forecasts

ATTRITION SEGMENTATION IN SAS

Data

Variables # Variable Type Len Format Informat Label

1 Emp_id Num 8 Emp_id

2 Att_ind Num 8 Att_ind

3 Age Num 8 Age

4 Tenure Num 8 Tenure

5 Educational_background Num 8 Educational_background

6 Gender Char 6 $6. $6. Gender

7 Overall_Experience Num 8 Overall Experience

8 Region_code Num 8 Region_code

9 Process_code Num 8 Process_code

10 Emp_ref_index Num 8 Emp_ref_index

11 Top_perf_index Num 8 Top_perf_index

Segmentation using SAS E-miner

Select data

Select type of analysis

Results of analysis

Goodness of fit and other statistics