hr analytics
Post on 17-Oct-2014
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HR AnalyticsShubham Singhal
80303120053
PGDM
NMIMS, Hyderabad
2 Primer
The core of HR Analytics is the "metric“
Metrics can be said as data that conveys meaning in a given context
Metric is to be distinguished from numbers
Example:
- Employee turnover is 13.5% p.a. Data- There is a 4 percent point rise in attrition rate on a year to year basis
Metric
- Inappropriate Leadership styles of select managers resulted in higher attrition of 3% on a comparable basis
Analytic
3 Primer – Contd.
Checklist, Dashboard, HRIS
- All of these are tools to collate and display information
Hypothesis: u0 & u1
Variables: Dependent and Independent
Statistical Models
- E.g. Regression, ANOVA
4 HR Analytics
Analytics is not so much about numbers, as it is to do with logic and reasoning Analytics is different from Analysis, which is the equivalent of number
crunching. Analytics uses analysis but then builds on it to understand the 'why' behind the figures and/or to predict decisions. Analytics is the methodology of logical analysis
Analytics requires the use of carefully constructed metrics
HR Analytics is data based; it uses past data to predict the future
It is not about the quantity of data churned; it is about the logic used to link metrics to results
5 Core concepts and terminologies
Analytics
Decision
=BusinessIntelligence
6
Business intelligence (BI) is a set of theories,methodologies, processes, architectures, andtechnologies that transform raw data into meaningfuland useful information for business purposes.
Business analytics (BA) refers to the skills,technologies, applications and practices for continuousiterative exploration and investigation of past businessperformance to gain insight and drive businessplanning.
Core concepts and terminologies
7Past to future
Tera bytes of data of information being
generated every single day which is being used to answer, fairly accurately,
what will probably occur in the future
Analytics is shifting emphasis from trend
analysis based purely on internal data to presenting
scenarios of the future
HR’s Evolution
9 BackgroundNeed 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
10
Why HR Analytics?“What getsmeasured, getsmanaged; Whatgets managed,gets executed”
- Peter Drucker
“ To clearlydemonstrate theinteraction ofbusiness objectivesand workforcestrategies todetermine a fullpicture of likelyoutcomes”
HR Dashboards - SAP
Measure &Manage
Linkage ofBusiness
Objectivesand PeopleStrategies
Return onInvestment
PerformanceImprovement
“The businessdemands on HR areincreasingly goingto be on analysis
just because peopleare so expensive“
- David Foster
“Global organizationswith workforce
analytics andworkforce planning
outperform all otherorganizations by 30%
more sales peremployee.”
- CedarCrestone
11 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)
12
What should/could be measured?
Recruitment
Organizationeffectiveness
HRMatrices
Workforce
Comp &Benefits
Retention
Performance &Career
Management
Training
13
Critical areas for HR Predictive analytics
1. Turnover modeling. Predicting future turnover in business units in specificfunctions, geographies by looking at factors such as commute time, time since lastrole change, and performance over time.
2. Targeted retention. Find out high risk of churn in the future and focus retentionactivities on critical few people
3. Risk Management. Profiling of candidates with higher risk of leaving prematurelyor those performing below standard.
4. Talent Forecasting. To predict which new hires, based on their profile, are likely tobe high fliers and then moving them in to fast track programs
14
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 etc.
Level-3Predictive analysis
Three levels of HR analytics and reporting
15Stages of Analytics
Predictive AnalyticsWhat can happen?
Analysis & MonitoringWhy did it happen? What is
happening now?
ReportingWhat happened?
Complexity
16Types of Analytical Models
PREDICTS
PREDICTSPREDICTIVE ANALYTICS
Current
Predictive Analytics Data
PREDICTS
Future
INFERENTIAL ANALYTICS
Analysis & MonitoringPast Data
Reporting
REPORT
DrawingConclusions or
Inferences
DESCRIPTIVE ANALYTICS
Representation ofData and
Summarizing
17 Critical areas for HR Predictive analytics
Turnover modeling. Predicting future turnover in business units in specific functions, geographies by looking at factors such as commute time, time since last role change, and performance over time. One can accelerate hiring efforts accordingly, reducing lead time time and panic hiring, which can lead to lower cost, higher quality hiring.
Recruitment advertising /HR Branding effectiveness: HR Branding efforts based on Response modeling for advertising jobs.
18 HR – Predictive analytics
Targeted retention. Find out high risk of churn in the future and focus retention activities on critical few people
Risk Management: profiling of candidates with higher risk of leaving prematurely or those performing below standard.
Talent Forecasting. To predict which new hires, based on their profile, are likely to be high fliers and then moving them in to fast track programs
19 Tools & Software Used
Typical tools / software:
• Microsoft Excel (max used)
• BI reporting tools
• ERP reporting tools, dashboards
• Statistical software like SAS, SPSS etc.
20 Social media impact
Predicting the future sounds mystical
Predictive ANALYTIC is touching every human on Earth who accesses internet
Day to day existence is now being exploited by social media and then the analytics
Executives; Corporate StrategyCraft and guide long term
workforce plan based on given information
Finance; Controlling; BudgetingGive input regarding financial figures and receives insights for midterm financial planning regarding the workforce
€$¥HR
BP
HR Business PartnerConsult with Business Units
based on workforce intelligence and drives action plans as final
deliverable from the process
HR HR HR
HR Administration; HR FunctionsRecruiting, Staffing, Talent Management and other HR functions support fulfillment of workforce action plans
HCM Analytics consumers by roleStakeholders across the organization
√x
Middle Managers; Line ManagersExecute on strategic plans and manage organizational performance to assure strategic objectives arereached timely and efficiently
MG
R
EmployeeNeeds contextual HR data to better perform
HR AnalystNeeds ad-hoc capabilities to do sophisticated analysis and planning
22
Real world case studies
Starbucks, Limited Brands, and Best Buy—can precisely identify the value of a 0.1%increase in employee engagement among employees at a particular store. At
Best Buy, for example, that value is more than $100,000 in the store’s annual operatingincome.
Many companies favor job candidates with stellar academic records from prestigiousschools—but AT&T and Google have established through quantitative analysis that ademonstrated ability to take initiative is a far better predictor of high performance onthe job.
Employee attrition can be less of a problem when managers see it coming. Sprint hasidentified the factors that best foretell which employees will leave after a relativelyshort time.
In 3 weeks Oracle was able to predict which top performers were predicted to leavethe organization and why - this information is now driving global policy changes in
retaining key performers and has provided the approved business case to expand thescope to predicting high performer flight .
23
Dow Chemical has evolved its workforce planning over the past decade, mininghistorical data on its 40,000 employees to forecasts promotion rates, internal transfers,and overall labor availability.
Dow uses a custom modeling tool to segment the workforce and calculates future headcount by segment and level for each business unit. These detailed predictions areaggregated to yield a workforce projection for the entire company.
Dow can engage in “what if” scenario planning, altering assumptions on internalvariables such as staff promotions or external variables such as political and legalconsiderations.
Real world case studies
Thanks!