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www.edureka.co/data-science
Predictive Analysis can help you Combat Employee Attrition!
Learn How
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At the end of the session, you will be able to understand:
✓ BI vs BA
✓ ...
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Business Intelligence Vs Business Analytics
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BI(What) --> Diagnostic analytics(Why) --> Predictive analytics(What wil...
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Types of Analytics?
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Next-Generation Analytics
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What is Predictive Analytics?
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Predictive analytics is the analysis of data by using statistical algori...
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Predictive Analytics Lifecycle
Source: blogs.sas.com
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Why Predictive Analytics?
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Only Analytics Is Not Enough!
Predictive analytics is a game-changer ...
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Forbes Says
Source: Forbes
The top objective for between two-thirds a...
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Major Domains Using Predictive Analytics
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Employee Attrition Prevention
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What Is Churn/Attrition ?
When your customers reduce their usage or c...
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Why HR needs Analytics
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Why HR needs Analytics
Predict attrition
especially amongst
high perf...
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Impact of Employee Turnover
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A CAP study found average costs to replace an employee are :
16% of a...
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In a recent article on employee retention, Josh Bersin of Bersin by D...
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Why Employee look for a change
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Identify :
• Which type of employees are churning
Evaluate :
• What i...
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Build Retention Framework
Build an attrition model
Build a profitabil...
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If HR Analyses the employee data beyond the wall, they can gain more ...
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What Is Measured Normally By HR
HR generally concentrate on the follo...
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What can be measured by predictive analysis
HR
Matrices
Recruitment
R...
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Turnover modeling :
• Predicting future turnover in business units in...
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1. Keeping a metric live even when it has no clear business reason fo...
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1. Keeping a metric live even when it has no clear business reason fo...
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1. Keeping a metric live even when it has no clear business reason fo...
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1. Keeping a metric live even when it has no clear business reason fo...
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1. Keeping a metric live even when it has no clear business reason fo...
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1. Keeping a metric live even when it has no clear business reason fo...
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Predictive Analytics Is A Game-Changer
Source: Forbes
Can precisely i...
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Many companies favor job candidates with stellar academic records fro...
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• Sprint has identified the factors that best foretell which employee...
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Problem statement:
An Indian MNC has a linear growth model. It wants ...
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Problem statement:
Campus hiring team is interested in how variables,...
www.edureka.co/advanced-predictive-modelling-in-r
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Develop
culture of
data-driven
decision-
making
Key To Success
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Transparency
of business
and
workforce
information
Develop
culture of...
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Transparency
of business
and
workforce
information
Develop
culture of...
Slide 43Slide 43Slide 43 www.edureka.co/data-science
Transparency
of business
and
workforce
information
Analytics as a
jou...
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...
Predictive analysis can help you combat Employee Attrition ! Learn how?
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Predictive analysis can help you combat Employee Attrition ! Learn how?

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

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

  1. 1. www.edureka.co/data-science Predictive Analysis can help you Combat Employee Attrition! Learn How
  2. 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. 3. Slide 3Slide 3Slide 3 www.edureka.co/data-science Business Intelligence Vs Business Analytics
  4. 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. 5. Slide 5Slide 5Slide 5 www.edureka.co/data-science Types of Analytics?
  6. 6. Slide 6Slide 6Slide 6 www.edureka.co/data-science Next-Generation Analytics
  7. 7. Slide 7Slide 7Slide 7 www.edureka.co/data-science What is Predictive Analytics?
  8. 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. 9. Slide 9Slide 9Slide 9 www.edureka.co/data-science Predictive Analytics Lifecycle Source: blogs.sas.com
  10. 10. Slide 10Slide 10Slide 10 www.edureka.co/data-science Why Predictive Analytics?
  11. 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. 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. 13. Slide 13Slide 13Slide 13 www.edureka.co/data-science Major Domains Using Predictive Analytics
  14. 14. Slide 14Slide 14Slide 14 www.edureka.co/data-science Employee Attrition Prevention
  15. 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. 16. Slide 16Slide 16Slide 16 www.edureka.co/data-science Why HR needs Analytics
  17. 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. 18. Slide 18Slide 18Slide 18 www.edureka.co/data-science Impact of Employee Turnover
  19. 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. 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. 21. Slide 21Slide 21Slide 21 www.edureka.co/data-science Why Employee look for a change
  22. 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. 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. 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. 25. Slide 25Slide 25Slide 25 www.edureka.co/data-science What Is Measured Normally By HR HR generally concentrate on the following factors :
  26. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
  39. 39. www.edureka.co/advanced-predictive-modelling-in-r
  40. 40. Slide 40Slide 40Slide 40 www.edureka.co/data-science Develop culture of data-driven decision- making Key To Success
  41. 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. 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. 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
  44. 44. Questions Slide 44 www.edureka.co/advanced-predictive-modelling-in-r
  45. 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

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