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

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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

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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)

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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, CHAID etc.,

Level-3Predictive analysis

Three levels of HR analytics and reporting

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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

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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

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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?

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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 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

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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

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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

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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

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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

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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

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Appendix

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Predictive Analysis using SAS- Examples with dummy data

Attrition forecasting using SAS

Attrition segmentation using SAS

Forecasting

Segmentation

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