Trendwise Analytics provides three levels of HR analytics and reporting capabilities to organizations. Level 1 involves descriptive analysis and dashboards to report basic metrics and percentages. Level 2 derives operational metrics like turnover and promotion ratios to track HR function efficiency. Level 3 utilizes predictive modeling like attrition forecasting, segmentation, and compensation analysis to inform decisions regarding retention, hiring, and compensation.

1. Trendwise Analytics
HR Analytics & Reporting
Trendwise Analytics

2. 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

3. 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

5. 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)

6. 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-2
Operational
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-3
Predictive
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-2
HR 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 basis
Turnover 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 year
Numerator: Distribution of above employees across
Improved performance rating in current year
Same performance rating in current year
Leavers 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
4.3%
2.5%
3.5%
4.5%
4.1% 4.3% 4.4% 4.6% 4.8% 4.9%5.1%
0.0%
2.0%
4.0%
6.0%
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/college(
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/College(
10%)
Other than tier-1
college(5%)
 FTE Segment with
highest Attrition %
FTE Segment with least
Attrition %
illustration

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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/college(
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/College(
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-1
Divide overall compensation into four major components;
Company, Employee, Market and general followed by identification of
top drivers in each quadrant
Stage-2
Study historical data to find the relation between compensation and
attributes in each quadrant , using SAS
Stage-3
Use predictive analysis in SAS(multiple linear regression) to
quantify the relation between compensation and attributes
Stage-4
Using above models, build a fair compensation prediction tool
that covers all the relevant attributes from each quadrant
Stage-5
Use the results obtained from predictive analysis to estimate the
optimal compensation for a given employee
Approach
List 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

15. 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 Compensation
Do 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

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