SlideShare ist ein Scribd-Unternehmen logo
1 von 46
www.edureka.co/data-science
Predictive Analysis can help you Combat Employee Attrition!
Learn How
Slide 2Slide 2Slide 2 www.edureka.co/data-science
At the end of the session, you will be able to understand:
✓ BI vs BA
✓ Types of Analytics
✓ Why Predictive Analytics?
✓ Domains where predictive analysis is creating magic
✓ Benefits Which you can gain with HR Analytics
✓ Real Time examples on HR Analytics
Agenda
Hands
on
Slide 3Slide 3Slide 3 www.edureka.co/data-science
Business Intelligence Vs Business Analytics
Slide 4Slide 4Slide 4 www.edureka.co/data-science
BI(What) --> Diagnostic analytics(Why) --> Predictive analytics(What will) --> Predictive analytics(Next best action)
is the path smarter organizations adopt and rightly so!
Before we go ahead, lets understand difference between BI and BA
WHAT is happening to your business = Business
Intelligence (For Visibility)
Data-warehousing, visualizations, Dashboards-->
Enabler of BI
WHY it is happening, WHAT WILL likely happen
in future = Business Analytics (For Investigation,
Prediction & Prescription)
Data analytics, Data sciences --> Enabler of
Business analytics
Business Intelligence Business Analytics
BI Vs BA
Slide 5Slide 5Slide 5 www.edureka.co/data-science
Types of Analytics?
Slide 6Slide 6Slide 6 www.edureka.co/data-science
Next-Generation Analytics
Slide 7Slide 7Slide 7 www.edureka.co/data-science
What is Predictive Analytics?
Slide 8Slide 8Slide 8 www.edureka.co/data-science
Predictive analytics is the analysis of data by using statistical algorithms and machine-learning
techniques to identify the likelihood of future outcomes based on historical data.
Predictive Analytics
Slide 9Slide 9Slide 9 www.edureka.co/data-science
Predictive Analytics Lifecycle
Source: blogs.sas.com
Slide 10Slide 10Slide 10 www.edureka.co/data-science
Why Predictive Analytics?
Slide 11Slide 11Slide 11 www.edureka.co/data-science
Only Analytics Is Not Enough!
Predictive analytics is a game-changer — it’s like “Moneyball” for… money.
Slide 12Slide 12Slide 12 www.edureka.co/data-science
Forbes Says
Source: Forbes
The top objective for between two-thirds and three-quarters of executives is to develop the ability
to model and predict behaviours to the point where individual decisions can be
made in real time, based on the analysis at hand.
Slide 13Slide 13Slide 13 www.edureka.co/data-science
Major Domains Using Predictive Analytics
Slide 14Slide 14Slide 14 www.edureka.co/data-science
Employee Attrition Prevention
Slide 15Slide 15Slide 15 www.edureka.co/data-science
What Is Churn/Attrition ?
When your customers reduce their usage or completely stop using your products or services
They are leaving your brand and might be shopping with your competitor
Slide 16Slide 16Slide 16 www.edureka.co/data-science
Why HR needs Analytics
Slide 17Slide 17Slide 17 www.edureka.co/data-science
Why HR needs Analytics
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)
Slide 18Slide 18Slide 18 www.edureka.co/data-science
Impact of Employee Turnover
Slide 19Slide 19Slide 19 www.edureka.co/data-science
A CAP study found average costs to replace an employee are :
16% of annual salary for low-paying jobs (earning under $30,000 a year).
For example, the cost to replace a $10/hour retail employee would be $3,328.
20% of annual salary for mid-range positions (earning $30,000 to $50,000 a year).
For example, the cost to replace a $40k manager would be $8,000.
Up to 213% of annual salary for highly educated executive positions.
For example, the cost to replace a $100k CEO is $213,000.
Hard to predict the true cost of employee turnover as there are many intangible, and often untracked, costs associated with employee turnover
Cost of Employee Turnover
Slide 20Slide 20Slide 20 www.edureka.co/data-science
In a recent article on employee retention, Josh Bersin of Bersin by Deloitte outlined factors a business
should consider in calculating the "real" cost of losing an employee.
These factors include:
The cost of hiring a new employee including the advertising, interviewing, screening, and hiring.
Cost of on-boarding a new person including training and management time.
Lost productivity... it may take a new employee 1-2 years to reach the productivity of an existing person.
Lost engagement... other employees who see high turnover tend to disengage and lose productivity.
Customer service and errors, for example new employees take longer and are often less adept at solving
problems.
Training cost. For example, over 2-3 years a business likely invests 10-20% of an employee's salary or more
in training
Cultural impact... Whenever someone leaves others take time to ask "why?"
Real Cost Of Losing An Employee?
Slide 21Slide 21Slide 21 www.edureka.co/data-science
Why Employee look for a change
Slide 22Slide 22Slide 22 www.edureka.co/data-science
Identify :
• Which type of employees are churning
Evaluate :
• What is the churn rate
Measure:
• What is the financial loss
Monitor :
• How is it trending
What we can do about it
Analyze the following traits :
Research :
• Salary is low
• Manager is not able to handle
• Check if the environment has become hostile
Segmentation :
• Divide you employees in categories like top
performers
• Monitor each segment trend
Predictive modeling :
• Which employees are like to churn
• Which employees are the most profitable
Proactive retention strategies:
• Use your insights to re-engage your employee
• Promise to sort the things
• Conduct regular surveys and feedback
Action Plan To Combat :
Use Analytical Tools & strategies to combat Attrition
Slide 23Slide 23Slide 23 www.edureka.co/data-science
Build Retention Framework
Build an attrition model
Build a profitability model
Build a cross model with above two models
Slide 24Slide 24Slide 24 www.edureka.co/data-science
If HR Analyses the employee data beyond the wall, they can gain more insights from it and hence can
stop turnover before it gets triggered
Smart HR Analytics can foresee the churn
Slide 25Slide 25Slide 25 www.edureka.co/data-science
What Is Measured Normally By HR
HR generally concentrate on the following factors :
Slide 26Slide 26Slide 26 www.edureka.co/data-science
What can be measured by predictive analysis
HR
Matrices
Recruitment
Retention
Performance &
Career
Management
TrainingComp &
Benefits
Workforce
Organization
effectiveness
Apart from the previous factors, an HR should pay attention to :
Slide 27Slide 27Slide 27 www.edureka.co/data-science
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.
2.Targeted retention :
• Find out high risk of churn in the future and focus retention activities on critical few people
3.Risk Management :
• Profiling of candidates with higher risk of leaving prematurely or those performing below
standard.
4.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
Critical Area For predictive analysis
Slide 28Slide 28Slide 28 www.edureka.co/data-science
1. Keeping a metric live even when it has no clear business reason for being
Common HR mistakes to avoid
Slide 29Slide 29Slide 29 www.edureka.co/data-science
1. Keeping a metric live even when it has no clear business reason for being
2. Relying on just a few metrics to evaluate employee performance, so smart employees can game the system
Common HR mistakes to avoid
Slide 30Slide 30Slide 30 www.edureka.co/data-science
1. Keeping a metric live even when it has no clear business reason for being
2. Relying on just a few metrics to evaluate employee performance, so smart employees can game the system
3. Insisting on 100% accurate data before an analysis is accepted—which amounts to never making a decision
Common HR mistakes to avoid
Slide 31Slide 31Slide 31 www.edureka.co/data-science
1. Keeping a metric live even when it has no clear business reason for being
2. Relying on just a few metrics to evaluate employee performance, so smart employees can game the system
3. Insisting on 100% accurate data before an analysis is accepted—which amounts to never making a decision
4. Assessing employees only on simple measures such as grades and test scores, which often fail to accurately
predict success
Common HR mistakes to avoid
Slide 32Slide 32Slide 32 www.edureka.co/data-science
1. Keeping a metric live even when it has no clear business reason for being
2. Relying on just a few metrics to evaluate employee performance, so smart employees can game the system
3. Insisting on 100% accurate data before an analysis is accepted—which amounts to never making a decision
4. Assessing employees only on simple measures such as grades and test scores, which often fail to accurately
predict success
5. Using analytics to hire lower-level people but not when assessing senior management
Common HR mistakes to avoid
Slide 33Slide 33Slide 33 www.edureka.co/data-science
1. Keeping a metric live even when it has no clear business reason for being
2. Relying on just a few metrics to evaluate employee performance, so smart employees can game the system
3. Insisting on 100% accurate data before an analysis is accepted—which amounts to never making a decision
4. Assessing employees only on simple measures such as grades and test scores, which often fail to accurately
predict success
5. Using analytics to hire lower-level people but not when assessing senior management
6. Analyzing HR efficiency metrics only, while failing to address the impact of talent management on business
performance
Common HR mistakes to avoid
Slide 34Slide 34Slide 34 www.edureka.co/data-science
Predictive Analytics Is A Game-Changer
Source: Forbes
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 operating income.
Slide 35Slide 35Slide 35 www.edureka.co/data-science
Many companies favor job candidates with stellar academic records from prestigious schools—but
AT&T and Google have established through quantitative analysis that a demonstrated ability to take
initiative is a far better predictor of high performance on the job.
Predictive Analytics Is A Game-Changer
Slide 36Slide 36Slide 36 www.edureka.co/data-science
• Sprint has identified the factors that best foretell which employees will leave after a relatively short
time
Predictive Analytics Is A Game-Changer
• In 3 weeks Oracle was able to predict which top performers were predicted to leave the organization
and why - this information is now driving global policy changes in retaining key performers and has
provided the approved business case to expand the scope to predicting high performer flight
Slide 37Slide 37Slide 37 www.edureka.co/data-science
Problem statement:
An Indian MNC has a linear growth model. It wants to identify relationship between % revenue growth and % headcount
growth. They have revenue and headcount details for past 10 years. Solution
Solution Approach:
•Identify the correlation coefficient based on the type of data and plot a scatter plot.
•Given that revenue growth is estimated at X% for the next year, we can predict headcount growth
Problem statement:
An HR manager identify 20 variables such as educational qualification, college, age, gender, nationality etc. that predicts
the hiring effectiveness. He wants to identify mutually exclusive variables which affect hiring effectiveness.
Solution Approach:
•Using factor analysis , mutually exclusive factors can be identified
Advanced And Predictive Analytics Application
Slide 38Slide 38Slide 38 www.edureka.co/data-science
Problem statement:
Campus hiring team is interested in how variables, such as entrance test score conducted by company, GPA (grade point
average) and prestige of the institution, effect selection . The response variable, selected/not selected, is a binary variable
Solution Approach:
•Selection data is collected for past 5 years for the above parameters indicated.
•Here dependent variable is selected/not selected( Selected =1, Not Selected= 0) and independent variables are Test
Score, GPA, Prestige of the institute.
•Using logistic regression a equation can be developed
Problem statement:
A company conducted a employee engagement survey using a questionnaire developed by internal HR team. The
questionnaire had 15 questions and responses were collected from 50 employees. As a HR manager, we want to identify
mutually exclusive factors.
Solution Approach:
•Using factor analysis , mutually exclusive factors can be identified
Advanced and Predictive Analytics application
www.edureka.co/advanced-predictive-modelling-in-r
Slide 40Slide 40Slide 40 www.edureka.co/data-science
Develop
culture of
data-driven
decision-
making
Key To Success
Slide 41Slide 41Slide 41 www.edureka.co/data-science
Transparency
of business
and
workforce
information
Develop
culture of
data-driven
decision-
making
Key To Success
Slide 42Slide 42Slide 42 www.edureka.co/data-science
Transparency
of business
and
workforce
information
Develop
culture of
data-driven
decision-
making
Empower line
leaders, not
just HR and
L&D
Key To Success
Slide 43Slide 43Slide 43 www.edureka.co/data-science
Transparency
of business
and
workforce
information
Analytics as a
journey, not
an end
Develop
culture of
data-driven
decision-
making
Empower line
leaders, not
just HR and
L&D
Key To Success
Questions
Slide 44 www.edureka.co/advanced-predictive-modelling-in-r
Slide 45
Your feedback is important to us, be it a compliment, a suggestion or a complaint. It helps us to make
the course better!
Please spare few minutes to take the survey after the webinar.
www.edureka.co/advanced-predictive-modelling-in-r
Survey
Predictive analysis can help you combat Employee Attrition ! Learn how?

Weitere ähnliche Inhalte

Was ist angesagt?

Employee Turnover And Retention In Banks
Employee Turnover And Retention In BanksEmployee Turnover And Retention In Banks
Employee Turnover And Retention In BanksAllen Godwin Amanna
 
Research paper on Employee turnover in organizations
Research paper on Employee turnover in organizationsResearch paper on Employee turnover in organizations
Research paper on Employee turnover in organizationsSummaya Sharif
 
Talent Analytics: A Systems Perspective
Talent Analytics:  A Systems PerspectiveTalent Analytics:  A Systems Perspective
Talent Analytics: A Systems PerspectiveSharad Verma
 
Analytics Driving Action - Building a Data-Driven HR Function
Analytics Driving Action - Building a Data-Driven HR FunctionAnalytics Driving Action - Building a Data-Driven HR Function
Analytics Driving Action - Building a Data-Driven HR FunctionJonathan Sidhu
 
Study report on_turnover_of_employees
Study report on_turnover_of_employeesStudy report on_turnover_of_employees
Study report on_turnover_of_employeesaldrinraghu123
 
Final Project
Final ProjectFinal Project
Final ProjectSHK
 
Final dissertation presentation
Final dissertation presentationFinal dissertation presentation
Final dissertation presentationDipti Belan
 
NHRD HR Analytics Presentation
NHRD HR Analytics PresentationNHRD HR Analytics Presentation
NHRD HR Analytics PresentationSupriya Thankappan
 
ROEI - Return on Employee Investment
ROEI - Return on Employee InvestmentROEI - Return on Employee Investment
ROEI - Return on Employee InvestmentSage HRMS
 
ROEI®: Return On Employee Investment® Increase Competitiveness Through Your ...
 ROEI®: Return On Employee Investment® Increase Competitiveness Through Your ... ROEI®: Return On Employee Investment® Increase Competitiveness Through Your ...
ROEI®: Return On Employee Investment® Increase Competitiveness Through Your ...Sage
 
EMPLOYEE RETENTION
EMPLOYEE RETENTION  EMPLOYEE RETENTION
EMPLOYEE RETENTION tigerjayadev
 
FermaLogis INc - Analysis on Employee attrition
FermaLogis INc - Analysis on Employee attritionFermaLogis INc - Analysis on Employee attrition
FermaLogis INc - Analysis on Employee attritionAshish Kumar Doke
 

Was ist angesagt? (18)

attritition rate
attritition rateattritition rate
attritition rate
 
Attrition
AttritionAttrition
Attrition
 
Employee Turnover And Retention In Banks
Employee Turnover And Retention In BanksEmployee Turnover And Retention In Banks
Employee Turnover And Retention In Banks
 
Research paper on Employee turnover in organizations
Research paper on Employee turnover in organizationsResearch paper on Employee turnover in organizations
Research paper on Employee turnover in organizations
 
Hr analytics
Hr analyticsHr analytics
Hr analytics
 
Talent Analytics: A Systems Perspective
Talent Analytics:  A Systems PerspectiveTalent Analytics:  A Systems Perspective
Talent Analytics: A Systems Perspective
 
Analytics Driving Action - Building a Data-Driven HR Function
Analytics Driving Action - Building a Data-Driven HR FunctionAnalytics Driving Action - Building a Data-Driven HR Function
Analytics Driving Action - Building a Data-Driven HR Function
 
Study report on_turnover_of_employees
Study report on_turnover_of_employeesStudy report on_turnover_of_employees
Study report on_turnover_of_employees
 
Final Project
Final ProjectFinal Project
Final Project
 
Final dissertation presentation
Final dissertation presentationFinal dissertation presentation
Final dissertation presentation
 
NHRD HR Analytics Presentation
NHRD HR Analytics PresentationNHRD HR Analytics Presentation
NHRD HR Analytics Presentation
 
ROEI - Return on Employee Investment
ROEI - Return on Employee InvestmentROEI - Return on Employee Investment
ROEI - Return on Employee Investment
 
ROEI®: Return On Employee Investment® Increase Competitiveness Through Your ...
 ROEI®: Return On Employee Investment® Increase Competitiveness Through Your ... ROEI®: Return On Employee Investment® Increase Competitiveness Through Your ...
ROEI®: Return On Employee Investment® Increase Competitiveness Through Your ...
 
Sage Return on Employee Investment
Sage Return on Employee InvestmentSage Return on Employee Investment
Sage Return on Employee Investment
 
Strategic workforce planning white paper
Strategic workforce planning white paperStrategic workforce planning white paper
Strategic workforce planning white paper
 
EMPLOYEE RETENTION
EMPLOYEE RETENTION  EMPLOYEE RETENTION
EMPLOYEE RETENTION
 
Presentation on Attrition
 Presentation on Attrition Presentation on Attrition
Presentation on Attrition
 
FermaLogis INc - Analysis on Employee attrition
FermaLogis INc - Analysis on Employee attritionFermaLogis INc - Analysis on Employee attrition
FermaLogis INc - Analysis on Employee attrition
 

Ähnlich wie Predictive analysis can help you combat Employee Attrition ! Learn how?

USING BIG AND LITTLE DATA TO RECRUIT THE RIGHT CANDIDATE FOR EVERY POSITION
USING BIG AND LITTLE DATA TO RECRUIT THE RIGHT CANDIDATE FOR EVERY POSITIONUSING BIG AND LITTLE DATA TO RECRUIT THE RIGHT CANDIDATE FOR EVERY POSITION
USING BIG AND LITTLE DATA TO RECRUIT THE RIGHT CANDIDATE FOR EVERY POSITIONDr. John Sullivan
 
The Future of HR: From Metrics to Analytics [Webcast]
The Future of HR: From Metrics to Analytics [Webcast]The Future of HR: From Metrics to Analytics [Webcast]
The Future of HR: From Metrics to Analytics [Webcast]LinkedIn Talent Solutions
 
IBM Attrition Presentation Deck.pdf
IBM Attrition Presentation Deck.pdfIBM Attrition Presentation Deck.pdf
IBM Attrition Presentation Deck.pdfChetanPant17
 
The Insider's Guide to Workforce Analytics
The Insider's Guide to Workforce AnalyticsThe Insider's Guide to Workforce Analytics
The Insider's Guide to Workforce AnalyticsVisier
 
10.09.14 glassdoor webinar_all_slides
10.09.14 glassdoor webinar_all_slides10.09.14 glassdoor webinar_all_slides
10.09.14 glassdoor webinar_all_slidesDr. John Sullivan
 
The Datafication of HR: Graduating from Metrics to Analytics
The Datafication of HR: Graduating from Metrics to AnalyticsThe Datafication of HR: Graduating from Metrics to Analytics
The Datafication of HR: Graduating from Metrics to AnalyticsVisier
 
Get a 3D view of your workforce
Get a 3D view of your workforceGet a 3D view of your workforce
Get a 3D view of your workforceAccess Group
 
Business analytics workshop presentation final
Business analytics workshop presentation   finalBusiness analytics workshop presentation   final
Business analytics workshop presentation finalBrian Beveridge
 
Workforce analytics, an introduction
Workforce analytics, an introductionWorkforce analytics, an introduction
Workforce analytics, an introductionAnalitiQs
 
Merging forensics w data analytics
Merging forensics w data analyticsMerging forensics w data analytics
Merging forensics w data analyticschris75308
 
Performance Appraisal in IT industry
Performance Appraisal in IT industryPerformance Appraisal in IT industry
Performance Appraisal in IT industrySudip Paudel
 
Frontline Optimization
Frontline OptimizationFrontline Optimization
Frontline OptimizationWorkday, Inc.
 
LinkedIn Talent Insights Launch event
LinkedIn Talent Insights Launch eventLinkedIn Talent Insights Launch event
LinkedIn Talent Insights Launch eventLinkedIn Europe
 
A Primer on HR Analytics
A Primer on HR AnalyticsA Primer on HR Analytics
A Primer on HR AnalyticsWorkforce Group
 
The Datafication of HR [WHITE PAPER]
The Datafication of HR [WHITE PAPER]The Datafication of HR [WHITE PAPER]
The Datafication of HR [WHITE PAPER]Sage HR
 
The What, The Why and the How of People Analytics November 2017
The What, The Why and the How of People Analytics November 2017The What, The Why and the How of People Analytics November 2017
The What, The Why and the How of People Analytics November 2017Dave Millner
 
Business Results from using Clear Direction Assessment Profile
Business Results from using Clear Direction Assessment ProfileBusiness Results from using Clear Direction Assessment Profile
Business Results from using Clear Direction Assessment Profileunificoaching
 

Ähnlich wie Predictive analysis can help you combat Employee Attrition ! Learn how? (20)

USING BIG AND LITTLE DATA TO RECRUIT THE RIGHT CANDIDATE FOR EVERY POSITION
USING BIG AND LITTLE DATA TO RECRUIT THE RIGHT CANDIDATE FOR EVERY POSITIONUSING BIG AND LITTLE DATA TO RECRUIT THE RIGHT CANDIDATE FOR EVERY POSITION
USING BIG AND LITTLE DATA TO RECRUIT THE RIGHT CANDIDATE FOR EVERY POSITION
 
The Future of HR: From Metrics to Analytics [Webcast]
The Future of HR: From Metrics to Analytics [Webcast]The Future of HR: From Metrics to Analytics [Webcast]
The Future of HR: From Metrics to Analytics [Webcast]
 
IBM Attrition Presentation Deck.pdf
IBM Attrition Presentation Deck.pdfIBM Attrition Presentation Deck.pdf
IBM Attrition Presentation Deck.pdf
 
The Insider's Guide to Workforce Analytics
The Insider's Guide to Workforce AnalyticsThe Insider's Guide to Workforce Analytics
The Insider's Guide to Workforce Analytics
 
10.09.14 glassdoor webinar_all_slides
10.09.14 glassdoor webinar_all_slides10.09.14 glassdoor webinar_all_slides
10.09.14 glassdoor webinar_all_slides
 
Hr analytics 2
Hr analytics 2Hr analytics 2
Hr analytics 2
 
The Datafication of HR: Graduating from Metrics to Analytics
The Datafication of HR: Graduating from Metrics to AnalyticsThe Datafication of HR: Graduating from Metrics to Analytics
The Datafication of HR: Graduating from Metrics to Analytics
 
Get a 3D view of your workforce
Get a 3D view of your workforceGet a 3D view of your workforce
Get a 3D view of your workforce
 
Business analytics workshop presentation final
Business analytics workshop presentation   finalBusiness analytics workshop presentation   final
Business analytics workshop presentation final
 
Workforce analytics, an introduction
Workforce analytics, an introductionWorkforce analytics, an introduction
Workforce analytics, an introduction
 
Merging forensics w data analytics
Merging forensics w data analyticsMerging forensics w data analytics
Merging forensics w data analytics
 
Performance Appraisal in IT industry
Performance Appraisal in IT industryPerformance Appraisal in IT industry
Performance Appraisal in IT industry
 
Frontline Optimization
Frontline OptimizationFrontline Optimization
Frontline Optimization
 
LinkedIn Talent Insights Launch event
LinkedIn Talent Insights Launch eventLinkedIn Talent Insights Launch event
LinkedIn Talent Insights Launch event
 
A Primer on HR Analytics
A Primer on HR AnalyticsA Primer on HR Analytics
A Primer on HR Analytics
 
The Datafication of HR [WHITE PAPER]
The Datafication of HR [WHITE PAPER]The Datafication of HR [WHITE PAPER]
The Datafication of HR [WHITE PAPER]
 
The What, The Why and the How of People Analytics November 2017
The What, The Why and the How of People Analytics November 2017The What, The Why and the How of People Analytics November 2017
The What, The Why and the How of People Analytics November 2017
 
One Page Talent Management
One Page Talent ManagementOne Page Talent Management
One Page Talent Management
 
Business Results from using Clear Direction Assessment Profile
Business Results from using Clear Direction Assessment ProfileBusiness Results from using Clear Direction Assessment Profile
Business Results from using Clear Direction Assessment Profile
 
Business analytics
Business analyticsBusiness analytics
Business analytics
 

Mehr von Edureka!

What to learn during the 21 days Lockdown | Edureka
What to learn during the 21 days Lockdown | EdurekaWhat to learn during the 21 days Lockdown | Edureka
What to learn during the 21 days Lockdown | EdurekaEdureka!
 
Top 10 Dying Programming Languages in 2020 | Edureka
Top 10 Dying Programming Languages in 2020 | EdurekaTop 10 Dying Programming Languages in 2020 | Edureka
Top 10 Dying Programming Languages in 2020 | EdurekaEdureka!
 
Top 5 Trending Business Intelligence Tools | Edureka
Top 5 Trending Business Intelligence Tools | EdurekaTop 5 Trending Business Intelligence Tools | Edureka
Top 5 Trending Business Intelligence Tools | EdurekaEdureka!
 
Tableau Tutorial for Data Science | Edureka
Tableau Tutorial for Data Science | EdurekaTableau Tutorial for Data Science | Edureka
Tableau Tutorial for Data Science | EdurekaEdureka!
 
Python Programming Tutorial | Edureka
Python Programming Tutorial | EdurekaPython Programming Tutorial | Edureka
Python Programming Tutorial | EdurekaEdureka!
 
Top 5 PMP Certifications | Edureka
Top 5 PMP Certifications | EdurekaTop 5 PMP Certifications | Edureka
Top 5 PMP Certifications | EdurekaEdureka!
 
Top Maven Interview Questions in 2020 | Edureka
Top Maven Interview Questions in 2020 | EdurekaTop Maven Interview Questions in 2020 | Edureka
Top Maven Interview Questions in 2020 | EdurekaEdureka!
 
Linux Mint Tutorial | Edureka
Linux Mint Tutorial | EdurekaLinux Mint Tutorial | Edureka
Linux Mint Tutorial | EdurekaEdureka!
 
How to Deploy Java Web App in AWS| Edureka
How to Deploy Java Web App in AWS| EdurekaHow to Deploy Java Web App in AWS| Edureka
How to Deploy Java Web App in AWS| EdurekaEdureka!
 
Importance of Digital Marketing | Edureka
Importance of Digital Marketing | EdurekaImportance of Digital Marketing | Edureka
Importance of Digital Marketing | EdurekaEdureka!
 
RPA in 2020 | Edureka
RPA in 2020 | EdurekaRPA in 2020 | Edureka
RPA in 2020 | EdurekaEdureka!
 
Email Notifications in Jenkins | Edureka
Email Notifications in Jenkins | EdurekaEmail Notifications in Jenkins | Edureka
Email Notifications in Jenkins | EdurekaEdureka!
 
EA Algorithm in Machine Learning | Edureka
EA Algorithm in Machine Learning | EdurekaEA Algorithm in Machine Learning | Edureka
EA Algorithm in Machine Learning | EdurekaEdureka!
 
Cognitive AI Tutorial | Edureka
Cognitive AI Tutorial | EdurekaCognitive AI Tutorial | Edureka
Cognitive AI Tutorial | EdurekaEdureka!
 
AWS Cloud Practitioner Tutorial | Edureka
AWS Cloud Practitioner Tutorial | EdurekaAWS Cloud Practitioner Tutorial | Edureka
AWS Cloud Practitioner Tutorial | EdurekaEdureka!
 
Blue Prism Top Interview Questions | Edureka
Blue Prism Top Interview Questions | EdurekaBlue Prism Top Interview Questions | Edureka
Blue Prism Top Interview Questions | EdurekaEdureka!
 
Big Data on AWS Tutorial | Edureka
Big Data on AWS Tutorial | Edureka Big Data on AWS Tutorial | Edureka
Big Data on AWS Tutorial | Edureka Edureka!
 
A star algorithm | A* Algorithm in Artificial Intelligence | Edureka
A star algorithm | A* Algorithm in Artificial Intelligence | EdurekaA star algorithm | A* Algorithm in Artificial Intelligence | Edureka
A star algorithm | A* Algorithm in Artificial Intelligence | EdurekaEdureka!
 
Kubernetes Installation on Ubuntu | Edureka
Kubernetes Installation on Ubuntu | EdurekaKubernetes Installation on Ubuntu | Edureka
Kubernetes Installation on Ubuntu | EdurekaEdureka!
 
Introduction to DevOps | Edureka
Introduction to DevOps | EdurekaIntroduction to DevOps | Edureka
Introduction to DevOps | EdurekaEdureka!
 

Mehr von Edureka! (20)

What to learn during the 21 days Lockdown | Edureka
What to learn during the 21 days Lockdown | EdurekaWhat to learn during the 21 days Lockdown | Edureka
What to learn during the 21 days Lockdown | Edureka
 
Top 10 Dying Programming Languages in 2020 | Edureka
Top 10 Dying Programming Languages in 2020 | EdurekaTop 10 Dying Programming Languages in 2020 | Edureka
Top 10 Dying Programming Languages in 2020 | Edureka
 
Top 5 Trending Business Intelligence Tools | Edureka
Top 5 Trending Business Intelligence Tools | EdurekaTop 5 Trending Business Intelligence Tools | Edureka
Top 5 Trending Business Intelligence Tools | Edureka
 
Tableau Tutorial for Data Science | Edureka
Tableau Tutorial for Data Science | EdurekaTableau Tutorial for Data Science | Edureka
Tableau Tutorial for Data Science | Edureka
 
Python Programming Tutorial | Edureka
Python Programming Tutorial | EdurekaPython Programming Tutorial | Edureka
Python Programming Tutorial | Edureka
 
Top 5 PMP Certifications | Edureka
Top 5 PMP Certifications | EdurekaTop 5 PMP Certifications | Edureka
Top 5 PMP Certifications | Edureka
 
Top Maven Interview Questions in 2020 | Edureka
Top Maven Interview Questions in 2020 | EdurekaTop Maven Interview Questions in 2020 | Edureka
Top Maven Interview Questions in 2020 | Edureka
 
Linux Mint Tutorial | Edureka
Linux Mint Tutorial | EdurekaLinux Mint Tutorial | Edureka
Linux Mint Tutorial | Edureka
 
How to Deploy Java Web App in AWS| Edureka
How to Deploy Java Web App in AWS| EdurekaHow to Deploy Java Web App in AWS| Edureka
How to Deploy Java Web App in AWS| Edureka
 
Importance of Digital Marketing | Edureka
Importance of Digital Marketing | EdurekaImportance of Digital Marketing | Edureka
Importance of Digital Marketing | Edureka
 
RPA in 2020 | Edureka
RPA in 2020 | EdurekaRPA in 2020 | Edureka
RPA in 2020 | Edureka
 
Email Notifications in Jenkins | Edureka
Email Notifications in Jenkins | EdurekaEmail Notifications in Jenkins | Edureka
Email Notifications in Jenkins | Edureka
 
EA Algorithm in Machine Learning | Edureka
EA Algorithm in Machine Learning | EdurekaEA Algorithm in Machine Learning | Edureka
EA Algorithm in Machine Learning | Edureka
 
Cognitive AI Tutorial | Edureka
Cognitive AI Tutorial | EdurekaCognitive AI Tutorial | Edureka
Cognitive AI Tutorial | Edureka
 
AWS Cloud Practitioner Tutorial | Edureka
AWS Cloud Practitioner Tutorial | EdurekaAWS Cloud Practitioner Tutorial | Edureka
AWS Cloud Practitioner Tutorial | Edureka
 
Blue Prism Top Interview Questions | Edureka
Blue Prism Top Interview Questions | EdurekaBlue Prism Top Interview Questions | Edureka
Blue Prism Top Interview Questions | Edureka
 
Big Data on AWS Tutorial | Edureka
Big Data on AWS Tutorial | Edureka Big Data on AWS Tutorial | Edureka
Big Data on AWS Tutorial | Edureka
 
A star algorithm | A* Algorithm in Artificial Intelligence | Edureka
A star algorithm | A* Algorithm in Artificial Intelligence | EdurekaA star algorithm | A* Algorithm in Artificial Intelligence | Edureka
A star algorithm | A* Algorithm in Artificial Intelligence | Edureka
 
Kubernetes Installation on Ubuntu | Edureka
Kubernetes Installation on Ubuntu | EdurekaKubernetes Installation on Ubuntu | Edureka
Kubernetes Installation on Ubuntu | Edureka
 
Introduction to DevOps | Edureka
Introduction to DevOps | EdurekaIntroduction to DevOps | Edureka
Introduction to DevOps | Edureka
 

Kürzlich hochgeladen

08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUK Journal
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Drew Madelung
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityPrincipled Technologies
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 
Evaluating the top large language models.pdf
Evaluating the top large language models.pdfEvaluating the top large language models.pdf
Evaluating the top large language models.pdfChristopherTHyatt
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...Martijn de Jong
 

Kürzlich hochgeladen (20)

08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
Evaluating the top large language models.pdf
Evaluating the top large language models.pdfEvaluating the top large language models.pdf
Evaluating the top large language models.pdf
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 

Predictive analysis can help you combat Employee Attrition ! Learn how?

  • 1. www.edureka.co/data-science Predictive Analysis can help you Combat Employee Attrition! Learn How
  • 2. Slide 2Slide 2Slide 2 www.edureka.co/data-science At the end of the session, you will be able to understand: ✓ BI vs BA ✓ Types of Analytics ✓ Why Predictive Analytics? ✓ Domains where predictive analysis is creating magic ✓ Benefits Which you can gain with HR Analytics ✓ Real Time examples on HR Analytics Agenda Hands on
  • 3. Slide 3Slide 3Slide 3 www.edureka.co/data-science Business Intelligence Vs Business Analytics
  • 4. Slide 4Slide 4Slide 4 www.edureka.co/data-science BI(What) --> Diagnostic analytics(Why) --> Predictive analytics(What will) --> Predictive analytics(Next best action) is the path smarter organizations adopt and rightly so! Before we go ahead, lets understand difference between BI and BA WHAT is happening to your business = Business Intelligence (For Visibility) Data-warehousing, visualizations, Dashboards--> Enabler of BI WHY it is happening, WHAT WILL likely happen in future = Business Analytics (For Investigation, Prediction & Prescription) Data analytics, Data sciences --> Enabler of Business analytics Business Intelligence Business Analytics BI Vs BA
  • 5. Slide 5Slide 5Slide 5 www.edureka.co/data-science Types of Analytics?
  • 6. Slide 6Slide 6Slide 6 www.edureka.co/data-science Next-Generation Analytics
  • 7. Slide 7Slide 7Slide 7 www.edureka.co/data-science What is Predictive Analytics?
  • 8. Slide 8Slide 8Slide 8 www.edureka.co/data-science Predictive analytics is the analysis of data by using statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. Predictive Analytics
  • 9. Slide 9Slide 9Slide 9 www.edureka.co/data-science Predictive Analytics Lifecycle Source: blogs.sas.com
  • 10. Slide 10Slide 10Slide 10 www.edureka.co/data-science Why Predictive Analytics?
  • 11. Slide 11Slide 11Slide 11 www.edureka.co/data-science Only Analytics Is Not Enough! Predictive analytics is a game-changer — it’s like “Moneyball” for… money.
  • 12. Slide 12Slide 12Slide 12 www.edureka.co/data-science Forbes Says Source: Forbes The top objective for between two-thirds and three-quarters of executives is to develop the ability to model and predict behaviours to the point where individual decisions can be made in real time, based on the analysis at hand.
  • 13. Slide 13Slide 13Slide 13 www.edureka.co/data-science Major Domains Using Predictive Analytics
  • 14. Slide 14Slide 14Slide 14 www.edureka.co/data-science Employee Attrition Prevention
  • 15. Slide 15Slide 15Slide 15 www.edureka.co/data-science What Is Churn/Attrition ? When your customers reduce their usage or completely stop using your products or services They are leaving your brand and might be shopping with your competitor
  • 16. Slide 16Slide 16Slide 16 www.edureka.co/data-science Why HR needs Analytics
  • 17. Slide 17Slide 17Slide 17 www.edureka.co/data-science Why HR needs Analytics 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)
  • 18. Slide 18Slide 18Slide 18 www.edureka.co/data-science Impact of Employee Turnover
  • 19. Slide 19Slide 19Slide 19 www.edureka.co/data-science A CAP study found average costs to replace an employee are : 16% of annual salary for low-paying jobs (earning under $30,000 a year). For example, the cost to replace a $10/hour retail employee would be $3,328. 20% of annual salary for mid-range positions (earning $30,000 to $50,000 a year). For example, the cost to replace a $40k manager would be $8,000. Up to 213% of annual salary for highly educated executive positions. For example, the cost to replace a $100k CEO is $213,000. Hard to predict the true cost of employee turnover as there are many intangible, and often untracked, costs associated with employee turnover Cost of Employee Turnover
  • 20. Slide 20Slide 20Slide 20 www.edureka.co/data-science In a recent article on employee retention, Josh Bersin of Bersin by Deloitte outlined factors a business should consider in calculating the "real" cost of losing an employee. These factors include: The cost of hiring a new employee including the advertising, interviewing, screening, and hiring. Cost of on-boarding a new person including training and management time. Lost productivity... it may take a new employee 1-2 years to reach the productivity of an existing person. Lost engagement... other employees who see high turnover tend to disengage and lose productivity. Customer service and errors, for example new employees take longer and are often less adept at solving problems. Training cost. For example, over 2-3 years a business likely invests 10-20% of an employee's salary or more in training Cultural impact... Whenever someone leaves others take time to ask "why?" Real Cost Of Losing An Employee?
  • 21. Slide 21Slide 21Slide 21 www.edureka.co/data-science Why Employee look for a change
  • 22. Slide 22Slide 22Slide 22 www.edureka.co/data-science Identify : • Which type of employees are churning Evaluate : • What is the churn rate Measure: • What is the financial loss Monitor : • How is it trending What we can do about it Analyze the following traits : Research : • Salary is low • Manager is not able to handle • Check if the environment has become hostile Segmentation : • Divide you employees in categories like top performers • Monitor each segment trend Predictive modeling : • Which employees are like to churn • Which employees are the most profitable Proactive retention strategies: • Use your insights to re-engage your employee • Promise to sort the things • Conduct regular surveys and feedback Action Plan To Combat : Use Analytical Tools & strategies to combat Attrition
  • 23. Slide 23Slide 23Slide 23 www.edureka.co/data-science Build Retention Framework Build an attrition model Build a profitability model Build a cross model with above two models
  • 24. Slide 24Slide 24Slide 24 www.edureka.co/data-science If HR Analyses the employee data beyond the wall, they can gain more insights from it and hence can stop turnover before it gets triggered Smart HR Analytics can foresee the churn
  • 25. Slide 25Slide 25Slide 25 www.edureka.co/data-science What Is Measured Normally By HR HR generally concentrate on the following factors :
  • 26. Slide 26Slide 26Slide 26 www.edureka.co/data-science What can be measured by predictive analysis HR Matrices Recruitment Retention Performance & Career Management TrainingComp & Benefits Workforce Organization effectiveness Apart from the previous factors, an HR should pay attention to :
  • 27. Slide 27Slide 27Slide 27 www.edureka.co/data-science 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. 2.Targeted retention : • Find out high risk of churn in the future and focus retention activities on critical few people 3.Risk Management : • Profiling of candidates with higher risk of leaving prematurely or those performing below standard. 4.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 Critical Area For predictive analysis
  • 28. Slide 28Slide 28Slide 28 www.edureka.co/data-science 1. Keeping a metric live even when it has no clear business reason for being Common HR mistakes to avoid
  • 29. Slide 29Slide 29Slide 29 www.edureka.co/data-science 1. Keeping a metric live even when it has no clear business reason for being 2. Relying on just a few metrics to evaluate employee performance, so smart employees can game the system Common HR mistakes to avoid
  • 30. Slide 30Slide 30Slide 30 www.edureka.co/data-science 1. Keeping a metric live even when it has no clear business reason for being 2. Relying on just a few metrics to evaluate employee performance, so smart employees can game the system 3. Insisting on 100% accurate data before an analysis is accepted—which amounts to never making a decision Common HR mistakes to avoid
  • 31. Slide 31Slide 31Slide 31 www.edureka.co/data-science 1. Keeping a metric live even when it has no clear business reason for being 2. Relying on just a few metrics to evaluate employee performance, so smart employees can game the system 3. Insisting on 100% accurate data before an analysis is accepted—which amounts to never making a decision 4. Assessing employees only on simple measures such as grades and test scores, which often fail to accurately predict success Common HR mistakes to avoid
  • 32. Slide 32Slide 32Slide 32 www.edureka.co/data-science 1. Keeping a metric live even when it has no clear business reason for being 2. Relying on just a few metrics to evaluate employee performance, so smart employees can game the system 3. Insisting on 100% accurate data before an analysis is accepted—which amounts to never making a decision 4. Assessing employees only on simple measures such as grades and test scores, which often fail to accurately predict success 5. Using analytics to hire lower-level people but not when assessing senior management Common HR mistakes to avoid
  • 33. Slide 33Slide 33Slide 33 www.edureka.co/data-science 1. Keeping a metric live even when it has no clear business reason for being 2. Relying on just a few metrics to evaluate employee performance, so smart employees can game the system 3. Insisting on 100% accurate data before an analysis is accepted—which amounts to never making a decision 4. Assessing employees only on simple measures such as grades and test scores, which often fail to accurately predict success 5. Using analytics to hire lower-level people but not when assessing senior management 6. Analyzing HR efficiency metrics only, while failing to address the impact of talent management on business performance Common HR mistakes to avoid
  • 34. Slide 34Slide 34Slide 34 www.edureka.co/data-science Predictive Analytics Is A Game-Changer Source: Forbes 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 operating income.
  • 35. Slide 35Slide 35Slide 35 www.edureka.co/data-science Many companies favor job candidates with stellar academic records from prestigious schools—but AT&T and Google have established through quantitative analysis that a demonstrated ability to take initiative is a far better predictor of high performance on the job. Predictive Analytics Is A Game-Changer
  • 36. Slide 36Slide 36Slide 36 www.edureka.co/data-science • Sprint has identified the factors that best foretell which employees will leave after a relatively short time Predictive Analytics Is A Game-Changer • In 3 weeks Oracle was able to predict which top performers were predicted to leave the organization and why - this information is now driving global policy changes in retaining key performers and has provided the approved business case to expand the scope to predicting high performer flight
  • 37. Slide 37Slide 37Slide 37 www.edureka.co/data-science Problem statement: An Indian MNC has a linear growth model. It wants to identify relationship between % revenue growth and % headcount growth. They have revenue and headcount details for past 10 years. Solution Solution Approach: •Identify the correlation coefficient based on the type of data and plot a scatter plot. •Given that revenue growth is estimated at X% for the next year, we can predict headcount growth Problem statement: An HR manager identify 20 variables such as educational qualification, college, age, gender, nationality etc. that predicts the hiring effectiveness. He wants to identify mutually exclusive variables which affect hiring effectiveness. Solution Approach: •Using factor analysis , mutually exclusive factors can be identified Advanced And Predictive Analytics Application
  • 38. Slide 38Slide 38Slide 38 www.edureka.co/data-science Problem statement: Campus hiring team is interested in how variables, such as entrance test score conducted by company, GPA (grade point average) and prestige of the institution, effect selection . The response variable, selected/not selected, is a binary variable Solution Approach: •Selection data is collected for past 5 years for the above parameters indicated. •Here dependent variable is selected/not selected( Selected =1, Not Selected= 0) and independent variables are Test Score, GPA, Prestige of the institute. •Using logistic regression a equation can be developed Problem statement: A company conducted a employee engagement survey using a questionnaire developed by internal HR team. The questionnaire had 15 questions and responses were collected from 50 employees. As a HR manager, we want to identify mutually exclusive factors. Solution Approach: •Using factor analysis , mutually exclusive factors can be identified Advanced and Predictive Analytics application
  • 40. Slide 40Slide 40Slide 40 www.edureka.co/data-science Develop culture of data-driven decision- making Key To Success
  • 41. Slide 41Slide 41Slide 41 www.edureka.co/data-science Transparency of business and workforce information Develop culture of data-driven decision- making Key To Success
  • 42. Slide 42Slide 42Slide 42 www.edureka.co/data-science Transparency of business and workforce information Develop culture of data-driven decision- making Empower line leaders, not just HR and L&D Key To Success
  • 43. Slide 43Slide 43Slide 43 www.edureka.co/data-science Transparency of business and workforce information Analytics as a journey, not an end Develop culture of data-driven decision- making Empower line leaders, not just HR and L&D Key To Success
  • 45. Slide 45 Your feedback is important to us, be it a compliment, a suggestion or a complaint. It helps us to make the course better! Please spare few minutes to take the survey after the webinar. www.edureka.co/advanced-predictive-modelling-in-r Survey