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

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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)

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What should/could be measured?

Recruitment

Organizationeffectiveness

HRMatrices

Workforce

Comp &Benefits

Retention

Performance &Career

Management

Training

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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

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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

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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 .

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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!