hr analytics, done right
Post on 17-Oct-2014
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A brief overview of the HR Analysis methods of Trendwise Analytics.TRANSCRIPT
Trendwise Analytics
HR Analytics & ReportingTrendwise Analytics
Trendwise Analytics
Contents About Trendwise analytics
Background and objectives
Need of HR analytics & reporting
Trendwise Analytics – HR analytics capabilities
HR Reporting & Analytics Level-1
Dashboards & Descriptive analysis
How to use Level -1 analysis for making business decisions
HR Reporting & Analytics Level-2
Derived Metrics & Ratios
How to use Level -2 analysis for making business decisions
HR Reporting & Analytics Level-3
Attrition forecasting
Attrition segmentation & Hotspot identification
Top performer segmentation
Compensation analysis & Fair compensation tool
Voice of employee analysis & drivers of employee satisfaction
Trendwise Analytics
About Trendwise analytics
• Trendwise is formed by a group of technocrats whose experiences from the industry forms a strong foundation of the company. The founder members of Trendwise had been a part of early CRM evolution and hence establishes an authority over CRM analytics. Our focus would be set on the newer aspects of analytics which is yet to come of age. While Hadoop, Cloud Computing, BigData analytics for the technological basis for us, our domain focus is on predictive aspect of analytics which would create insights for our customers like never before.
Overview
• To be one of the most valuable companies in the area of advanced analytics with a strong global presence with a wide client base for our products and solutions.
Vision
• To develop analytics tools and solutions for handling big, unstructured data for creating business insights. The offerings would be targeted to specific business areas and industry streams. Also to provide support and services to our customers on our products and solutions.
Mission
Trendwise Analytics
Services and Technology
• CRM Analytics• HR Analytics• Big Data Analysis (leveraging Hadoop)• Social Media Analytics• Verbatim Analysis/Text analyzer• Advanced Analytics and Predictive modeling• Mobility and Mobile Analytics
Services
• SAS• R• Tableau• Jasper soft• Mysql PHP• Hadoop
Technology and Tools
Trendwise Analytics
Background and objectives
Need of HR analytics & reporting Many 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 recruit whom to hire how to onboard and train employees how they keep employees informed and engaged through their tenure with the
organization Hence regular tracking and prediction of crucial HR metrics is indispensable
Objectives Predict attrition especially amongst high performers. Forecast the right fitment for aspiring employee Predict how compensation values will pan out. Establish linkages between Employee engagement score and C-Sat scores(Work in
progress)
Trendwise Analytics
Trendwise Analytics – HR analytics capabilities
• Reporting of basic metrics, their frequencies & percentages by various cuts followed by key highlights. These can be monthly, quarterly, half yearly tracking reports• Tool: SAS/REPORT• Techniques: frequencies , means, percentages etc.,
Level-1 Descriptive
analysis
• Derivation of some HR operational metrics which will help us in tracking the efficiency of HR functions• Tool: SAS• Techniques: means, variance, control limits, ratios,
percentages etc.,
Level-2Operational
metrics
• Predictive analysis based on historical HR data. Attrition forecasting, performance management, compensation analysis, survey analytics, new hire strategies etc.,• Tool: SAS BASE, SAS E-miner, Excel• Techniques: Regression analysis, Time series analysis,
cluster analysis, CHAID etc.,
Level-3Predictive analysis
Three levels of HR analytics and reporting
Trendwise Analytics
HR Reporting and Analytics: Level-1
HR 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 &
Completion
Performance tracking reports , Absences ,Event Grievances / Disciplinary
Actions Employee Appraisal / Review / Accomplishments
Requisition tracking, Vacancy / skills matching / competencies
Payroll related reports, Injury illness, Time and labor
All 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
Trendwise Analytics
How to use Level-1 analysis?
Reports
2010 Q1
2010 Q2
2010 Q3
2010 Q4
2011 Q1
2011 Q2
2011 Q3
Involuntary Turnover Voluntary Turnover
Better Compensation
Higher Education
Unsatisfactory performance
Company HR policies
Shifting location
Retirement
Voluntary Turnover Involuntary Turnover
Insights
Action points
Turnover rates are above acceptable levels in last two quarters
Compensation and location shift are two main reasons
Revise compensation strategies, time to concentrate on incentives
and employee retention strategies
Trendwise Analytics
HR Reporting and Analytics: Level-2HR metrics and ratios–HR 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 year
Promotion 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 productivity
Joiners Rate: The ratio of new and replacement hires as the percentage of total employment
Metric Insights Action points
Focus on new hire and employee retention strategies
How to use Level-2 analysis?
Trendwise Analytics
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 level
Attrition 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 characteristics
Compensation 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
Trendwise Analytics
HR Reporting and Analytics: Level-3 Attrition forecasting
Predicting/forecasting near future attrition numbers by identifying patterns in historical attrition data
0.0%
2.0%
4.0%
6.0%
4.3%
2.5%
3.5%
4.5%4.1% 4.3% 4.4% 4.6% 4.8% 4.9% 5.1%
Attrition%
illustration
Trendwise Analytics
HR Reporting and Analytics: Level-3 Attrition segmentation
Identifying segments with high/low attrition rates and employee characteristics in each segment
Over all Head count (Attrition
15%)
Age <28(Attrition20
%)
Tenure with the company <1.5
years(30%)
Tier-1 University/colleg
e(35%)
Other than tier-1(28%)
Tenure with company 1.5-3
years(20%)
Tenure with company >3 years(10%)
Age >28(Attrition9%)
Tenure with company < 3 years(14%)
Tenure with company >3 years(6%)
Tier-1 University/Colle
ge(10%)
Other than tier-1 college(5%)
FTE Segment with highest Attrition %
FTE Segment with least Attrition %
illustration
Trendwise Analytics
HR Reporting and Analytics: Level-3 Top performer segmentation
Identifying High /Low performing employee segments and their characteristics (subjected to availability of necessary performance measures)
Over all FTE population (20% high performers)
Age <28(30% high performers)
Tenure with the company >3 years(40%)
Tier-1 University/colleg
e(55%)
Other than tier-1(25%)
Tenure with company 1.5-3
years(30%)
Tenure with company < 1.5
years(20%)
Age >28(18% high performers)
Tenure with company > 3 years(22%)
Tenure with company < 3 years(14%)
Tier-1 University/Colle
ge(18%)
Other than tier-1 college(10%)
FTE Segment with High % of top performers
FTE Segment with least % top performers
illustration
Trendwise Analytics
HR Reporting and Analytics: Level-3 Fair compensation tool
Project Stage
Description
Stage-1Divide overall compensation into four major components; Company, Employee, Market and general followed by identification of top drivers in each quadrant
Stage-2Study historical data to find the relation between compensation and attributes in each quadrant , using SAS
Stage-3Use predictive analysis in SAS(multiple linear regression) to quantify the relation between compensation and attributes
Stage-4Using above models, build a fair compensation prediction tool that covers all the relevant attributes from each quadrant
Stage-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
Trendwise Analytics
HR Reporting and Analytics: Level-3 Fair compensation tool & algorithm
gap between Maximum and
minimum salary
Company 30%
Employee 35%
Market 25%
Others 10%
Divided into four quadrants based on weights
Budget
Urgency
Impact
Identifying top attributes in each quadrants
Company component in final compensation
Assign weights to each of these components based on statistical analysis of historical data
Final CompensationDo the same excessive for four quadrants
Trendwise Analytics
HR Reporting and Analytics: Level-3 Voice of employee survey analysis & drivers of satisfaction
Reporting: 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 data
E.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
regression
Driver Analysis (part-2): Merging of survey responders data with employee profile
and performance data. Identification of main drivers of satisfaction from non
surveyed variables
E.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 satisfaction
Analysis of verbatim comments:
Descriptive analysis of positive , negative and neutral comments
Identification of frequently mentioned topics and their positive negative frequencies
Trendwise Analytics
Appendix
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Predictive Analysis using SAS- Examples with dummy data
Attrition forecasting using SAS
Attrition segmentation using SAS
Forecasting
Segmentation
Trendwise Analytics
THANK YOU