Specialist Diploma in Business Analytics Specialist ... · Project Submitted by STUDENT NAME: Don Teo Hong Gee ADMIN NUMBER: 1980806B GROUP: 4. 2 ... groups “Attrition” vs “No-Attrition”

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Specialist Diploma in Business Analytics

Specialist Diploma in Financial Analytics

AY2019/2020 Apr Semester

Business Intelligence Fundamentals

(CBA1C08)

Project

Submitted by

STUDENT NAME: Don Teo Hong Gee

ADMIN NUMBER: 1980806B

GROUP: 4

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=================================================================================

INTRODUCTION =================================================================================

I chose a Sample dataset on “HR Employee Attrition and Performance” listed on Kaggle, for this project (Extracted from Watson Analytics blog - https://www.ibm.com/communities/analytics/watson-analytics-blog/hr-employee-attrition/).

From the description, I was able to observe the dataset contain HR related information on Past and

Current Employees such as:

As the Title of the dataset stated and its related information; it is a study on Employee Attrition and

the Attrition Rate for this company is at 16.12% (237 out of Total 1,470 staffs), which in general as a best

practise Attrition benchmark; it is considered High and the associated Cost of Attrition cannot be

under-estimated! (refer to section on Analysis & Dashboards)

I will attest to such study as retaining Capable Productive Employees is one Critical Factor to success

in any organization. Organisation have to aim at maximizing the tenure of Capable Productive

Employee for a mutually beneficial commitment towards fulfilling the Organization Objectives;

thereby also address Value Leakages in terms of Talents/Knowledge Drain, Cost Involved in Employee

Attrition such as;

• Cost of Hiring a new employee including the Advertising, Interviewing, Screening, and Hiring,

• On-Boarding Time and Cost,

• Training Invested & Re-Training New Staff,

• Lost Productivity—it takes a New Employee 1 to 2 years to reach the Productivity of an existing

person,

• Lost Engagement—other employees who see high turnover tend to disengage and lose

productivity etc.

Side Note: Cost of employee turnover is often untracked! or acted upon!

On Average every 1% reduction in Attrition is $3m replacement cost!!

The purpose of this Analytic Project is to understand what factors this Organisation should focus on

in order to minimise Attrition; to know what changes should be made to the workplace (i.e. Employee

Engagement, HR Policies – Recruitment, Onboarding etc, Talent Management System etc), in order to get most of their

Capable Productive Employees to stay. Also, to know which of these variables is most important and

· Age · DistanceFromHome · EnvironmentSatisfaction · JobRole · NumCompaniesWorked · RelationshipSatisfaction · WorkLifeBalance

· Attrition · Education · Gender · JobSatisfaction · Over18 · StandardHours · YearsAtCompany

· BusinessTravel · EducationField · HourlyRate · MaritalStatus · OverTime · StockOptionLevel · YearsInCurrentRole

· DailyRate · EmployeeCount · JobInvolvement · MonthlyIncome · PercentSalaryHike · TotalWorkingYears · YearsSinceLastPromotion

· Department · EmployeeNumber · JobLevel · MonthlyRate · PerformanceRating · TrainingTimesLastYear · YearsWithCurrManager

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needs to be addressed right away. This Intervention Measures should eventually lead to minimising

related Value Leakages in Human Capital!

Analysis Approach:

With the above stated Analytics Project Purpose, we identify relevant “Column Variables” in the

dataset that we can regroup or remove (the unnecessary ones or set them aside for the time being) for more

comprehensive and effective analysis. This will follow by Data Profiling, Cleaning, Exploration and

Dashboards Analysis.

To begin, we try to understand the various Factors (X: Independent Variables); and comparing the two

groups “Attrition” vs “No-Attrition” (so that we can determine which are the factors could be

influencing Attrition) as follows:

Y = f(x1, x2, …. xn)

Basically, I regroup all the Variables into 10 Main Categories for more comprehensive and

effectiveness of Analysis:

1. Monetary (Monthly Income etc)

2. OT Recognition (Overtime vs Last Promotion)

3. Travel (DistfmHome, BizTravel Frequency)

4. Education Background

5. Training (#Trg Skill Upgrading)

6. Criticality of Role (Job Involvement)

7. Employee Satisfaction (Job Satisfaction, Envrt Satisfaction)

8. Tenure of Service (Yrs at Company)

9. Job Role Type, Dept

10. Other (Age, Martial Status, Gender etc)

(Y) DEPENDENT VARIABLE WHICH WE ARE EXAMINING:

Attrition (Y):

A categorical variable, Attrition: left company (“Yes”), No-Attrition: not left company (“No”)

(x) VARIABLES SERVE AS REFERENCE INDEXES:

EmployeeCount:

An integer as a Counter-Index for data analysis to reference to.

EmployeeNumber:

A numeric indicator as an index for data analysis to reference to.

(x) VARIABLES RELATED TO RECOGNITION - MONETARY COMPENSATION:

Monthly Income: An integer value indicated the Monthly Income (Salary) of employee

MonthlyRate: An integer value indicated the Monthly Rate of employee

DailyRate: An integer value indicated the Daily Rate of employee

HourlyRate: An integer value indicated the Hourly Rate of employee

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Analysis on Monetary Factor on Attrition vs No-Attrition

Remove “MonthlyRate”, “DailyRate”, and “HourlyRate” at this stage, as with “Monthly

Income” data is sufficient for us to study the monetary influence.

PercentSalaryHike:

The % of increment ranging from 11% to 25%

The % of Salary Hike is determined by Performance, so we have little to do with it, and also we

only want to keep Productive Contributing Employee.

No Analysis needed at this stage.

StockOptionLevel:

A numeric indicator stating the Stock Option Level of the employee ranging from 0 to 3

No Indication on the what’s “0” to”3”, and Stock Option will be more enticing for Employees

of Senior Management, will be good analysis for 2nd phase when drilling into whether the

Attrition is significant at Senior Level Employees.

No Analysis needed at this stage.

(x) VARIABLES RELATED TO RECOGNITION – vs EFFORT:

OverTime:

A categorical variable; 2 Levels (“Yes” or “No”)

YearsSinceLastPromotion:

An integer value indicating the years Since Last Promotion; ranging from 0 to 15

Analysis on whether Effort Commitment to work hard is recognise; Overtime vs Promotion on

Attrition vs No-Attrition

(x) VARIABLES RELATED TO CUMMUTING, AWAY-FROM-HOME:

DistanceFromHome:

An integer to classify employee travel distance from home to workplace; ranging from 1 to 29 (km)

BusinessTravel:

A categorical variable indicating employee’s BusinessTravel Status “Non-Travel” or “Travel_Rarely” or

“Travel_Frequently”

Analysis on Travelling Hassles impact on Attrition vs No-Attrition; for “Distance from Home”,

“Business Travel”

WorkLifeBalance:

A numeric indicator for Wok-Life Balance level ranging from 1 to 4 (1 'Bad' 2 'Good' 3 'Better' 4 'Best')

No Analysis needed at this stage, as Work-Life-Balance can be contributed by many other

primary factors such as Distance-From-Home, Business Traveling etc. Work-Life-Balance will

also have been covered under Job Satisfaction via Employee Survey.

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(x) VARIABLES RELATED TO ACADEMIC QUALIFICATION:

Education:

A categorical indicator on employee’s education ranging from 1 to 5 (1 'Below College' 2 'College' 3

'Bachelor' 4 'Master' 5 Doctorate)

EducationField:

A categorical variable on employee’s field of education ranging from 1 to 6 (1 “Human Resource”, 2

“Life Sciences”, 3 “Marketing”, 4 “Medical”, 5 “Other”, 6 “Technical Degree”)

Analysis on Education Factor and Education Field on Attrition vs No-Attrition

(x) VARIABLES RELATED TO CAREER GROWTH IN TERMS OF SKILL UPGRADING:

TrainingTimesLastYear:

A numeric indicator on number of times an employee attended training last year ranging from 0 to 6

Analysis of Training (Upgrading of Skillsets) influence on Attrition vs No-Attrition.

(x) VARIABLES RELATED TO CRTICALITY OF JOB ROLE POSITION:

JobInvolvement:

A numeric indicator on job involvement level ranging from 1 to 4 (1 'Low' 2 'Medium' 3 'High' 4 'Very

High')

Analysis of Job Involvement on Attrition vs No-Attrition

Job Involvement here is interpreted as Criticality of Role under the pretext that if an employee

is highly involved his/her role needs backup candidate to ensure continuity.

Those High Job Involvement employees are the ones that needed to be monitored more closely,

and backup candidates be identified with succession plan to be put in place. This will be used

at 2nd phase for identifying which are the specific employee that are Highly Involved, which

means the Criticality of Roles they have in their department.

(x) VARIABLES RELATED TO GENERAL EMPLOYEE SATISFACTION CONSIDERATION:

JobSatisfaction:

A numeric indicator filled in by the employee via employee survey ranging from 1 to 4 (1 'Low' 2

'Medium' 3 'High' 4 'Very High')

Analysis of Job Satisfaction influence on Attrition vs No-Attrition

EnvironmentSatisfaction:

A numeric indicator filled out by the employee ranging from 1 to 4 (1 'Low' 2 'Medium' 3 'High' 4 'Very

High')

Analysis of Environment Satisfaction influence on Attrition vs No-Attrition

RelationshipSatisfaction:

A numeric indicator filled in by the employee’s manager ranging from 0 to 4 (1 'Low' 2 'Medium' 3

'High' 4 'Very High')

Relationship Satisfaction will have been covered under Job Satisfaction via Employee Survey.

No Analysis is needed in this case.

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(x) VARIABLES RELATED TO TENURE OF SERVICE IN DIFFERENT ASPECT:

YearsAtCompany:

An integer value indicated the years of service in company

Analysis on Years-at-Company on Attrition vs No-Attrition

YearsWithCurrManager:

An integer value indicated the years With Current Manager

This will be covered under “YearsSinceLastPromotion” Analysis to check the influence, and also

even if Attrition happen at high number of years with current manager, it will indicate the

performance is not of high rating.

No Analysis is needed at this stage.

YearsInCurrentRole:

An integer value indicated the years In Current Role, ranging from “0” to “18”.

Same explanation as above “YearsWithCurrManager”


(x) VARIABLES RELATED TO JOB SCOPE, POSITIONAL DESIGNATION:

JobRole:

A numeric indicator; 9 levels “healthcare Representative”, “Human resource”, “Laboratory technician’,

“Manager”, “Manufacturing Director”, “Research Director”, “Research Scientist”, “Sales Executive”,

“Sales Representative”

Analysis of Job Role on Attrition vs No-Attrition; to check out which are the Job Roles had high

Attrition.

This is more to identify where the Attrition is occurring, not a Factor that may influence

Attrition.

JobLevel:

A numeric indicator filled in by the HR ranging from 1 to 5

Indicator “1” to “5” has no clear definition, and basically from Job Role Analysis is sufficient to

unearth preliminary where Attrition is occurring, may be needed at later stage to drill deeper.


Department:

A categorical variable assessing the department in which employee is working 3 levels: “Human

Resource”, “Research & Development”, “Sales”

Analysis on Attrition severity among the Department

(x) VARIABLES RELATED TO OTHER GENERAL VARIABLES:

Age:

An integer that indicates the age of the employee when data is collected

Analysis on Age for Attrition Group; to evaluate which Age Group had most Attrition.

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This is more to identify where the Attrition is occurring, not a Factor that may influence

Attrition.

TotalWorkingYears:

A numeric indicator filled in by the HR ranging from 0 to 40

No Analysis needed for this, as has been analysed under “Age”

Over18:

A categorical variable (yes or no)

No Analysis needed for this, as has been analysed under “Age”

Gender:

A categorical variable indicating employee’s gender “Male” or “Female”

Analysis of Gender on Attrition vs No-Attrition to determine which gender had more Attrition.

This will not be a factor that influence Attrition but more identifying the Attrition Personas.

MaritalStatus:

A categorical variable indicating employee’s Marital Status; 3 levels “Divorced”, “Married”, “Single”

Analysis of Marital Status on Attrition vs No-Attrition to determine which Marital Status Type

had more Attrition.

This will not be a factor that influence Attrition but more identifying the Attrition Personas.

NumCompaniesWorked:

A numeric indicator on Number of Companies an employee had previously worked ranging from 1 to 9

The number of Companies the employee previously worked will help during recruitment Phase,

and not a Factor that may influence Attrition.

No Analysis is needed for now.

PerformanceRating:

A numeric indicator filled in by the employee’s manager via Performance Appraisal ranging from 0 to

4 (1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding')

This serve as an indication or allow us to classify employees into Productive Contributing

Employees. We want to keep Productive Contributing Employees (nonetheless the below

performance employees will go into other coaching or counselling program depending it’s a

skill problem or attitude problem).

No Analysis needed in regard to identifying Factors that may influence Attrition.

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=================================================================================

DATA DISCOVERY: DATA PROFILING, CLEANING, EXPLORATION ================================================================================= (E.G. Null, Missing Values, Invalid Values), Document Your Method of Resolution (If Any such occurred, will

execute removal or imputation). Explain Any Relationship Between the Different Datasets (If Applicable)

Include A Screenshot of The Finalized Data Model for Your Dashboard (A single dataset is being examined here).

Overview of Datasets and Data Description

Source of Datasets: https://www.kaggle.com/patelprashant/employee-attrition

The data on Employee Attrition comprises 1470 observations of 35 features, i.e. there are 1,470 rows

and 35 columns. Of all the 35 columns, 9 are discrete, 26 are continuous.

There are 0 Missing Values/Null out of 51,450 data points.

Structure of the Dataset

Basic StatisticsName Value

Rows 1,470

Columns 35

Discrete columns 9

Continuous columns 26

All missing columns 0

Missing observations 0

Total observations 51,450

https://www.kaggle.com/patelprashant/employee-attrition

https://www.kaggle.com/patelprashant/employee-attrition

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✓ Check for Header properly loaded. All header properly loaded.

✓ Check for Correct Data Type loaded for each Column. There are some Data Type not correctly

assigned by Power BI, they are as follows:

o Column “Education” is discreet and factor variables but in numeric form. Would need

to convert it into text form.

Convert to 1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 Doctorate,

under a New Column “Education Copy”

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o Likewise, we do the same for Column “JobInvolvement” to convert it into text form

and convert to 1 'Low' 2 'Medium' 3 'High' 4 'Very High', under New Column “JobInvolvement Copy”

o Do the same for Column “JobSatisfaction” to convert it into text form and convert to

1 'Low' 2 'Medium' 3 'High' 4 'Very High', under New Column “JobSatisfaction Copy”

o Do the same for Column “WorkLifeBalance” to convert it into text form and convert

to 1 'Bad' 2 'Good' 3 'Better' 4 'Best', under New Column “WorkLifeBalanceCopy”

o Do the same for Column “EnvironmentSatisfaction” to convert it into text form and

convert to 1 'Low' 2 'Medium' 3 'High' 4 'Very High', under New Column “EnvironmentSatisfaction Copy”

Conduct Visual Inspection on the Dataset Summary Statistic => “Get Data” to “Blank Query”, function

“= Table.Profile(#"WA_Fn-UseC_-HR-Employee-Attriti")” to display i.e. “Min”, “Max”, “Average”,

“Standard Deviation” etc

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Several sample observations on how I read the above Summary & Data Overview:

• We see that ages of the employees are between 18 yrs to 60 yrs, the average age of the

employees is approximately 37 yrs, where the median is 36 yrs, i.e. most of the employees are

young.

• Positive cases (Attrition = yes) is around 237 and negative cases around 1233 in our dataset

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• The distance from home to their office is between 1 to 29 units, the average being close to 9

units.

• The employee count we see is singular in all cases, mean=median=min=max=1st quartile=3rd

quartile. So we can infer from here itself it has no effect on employee attrition. Serve as an

index reference.

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=================================================================================

ANALYSIS & DASHBOARDS

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BUSINESS CASE: Attrition Rate & Its Direct Impact

Actionable Insights:

To investigate Factors influencing Attrition Rate.

Our variable of interest is Attrition, so we would like to analyse each of the independent variables with

respect to Attrition (%).

(x) VARIABLES RELATED TO RECOGNITION - MONETARY COMPENSATION:

Attrition vs Monthly Income

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The Employees in Attrition Group has Lower Monthly Income than No-Attrition Group. From Boxplot,

we see the Median for Attrition Group is $3.41k (75% of them in $1.01k to $6.07K) while No-Attrition

is $5.48k (75% of them in $1.05K to $9.84K)

No-Attrition Group has Higher Monthly Income than the Attrition Group; reflected clearly in first 10-

years period (see all the red dots below those green dots).


To review Monthly Income Employees working less than 10 years; especially for the first 5-

years; against Industry Median for each Role-Appointment.

Add to “Factors has Potential Influence on Employee Attrition”

(x) VARIABLES RELATED TO RECOGNITION – vs EFFORT:

Attrition vs Overtime vs Years-Since-Last-Promotion

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For the No-Attrition Group; those work OT had Promotion faster than those No OT.

For Attrition Group, those work OT had Promotion slower than those No OT. This maybe an indication

those working hard are not recognised.


"Working Overtime" need review, against Promotion as Recognition; in alignment with

genuine productive overtime with verification from immediate superior or OT request form

approval


(x) VARIABLES RELATED TO CUMMUTING, AWAY-FROM-HOME:

Attrition vs Distance from Home

The Employees in Attrition Group has Longer Distance between Home and Work Location than No-

Attrition.

From Boxplot, we see the Median for Attrition Group is 10.29km (IQR 9.78km to 10.87km while No-

Attrition is 8.93km (IQR 8.63km to 9.09km).


To identify those "Longer Distance between Home and Work Location" and review, maybe

those that stay more than 9km need provide Company Transport at Fixed Pick-Up points or

provide some subsidy for this Group on Commuting such as Corporate Purchase of MRT Card

for better Discount etc.


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Attrition vs Business Travel

Non-Traveller has Least Attrition compared to those who need Travelling.

The Employees in Attrition Group has Travelled more Frequently than No-Attrition Group. Attrition

Group "Travel_Frequently" is 29.11% while No-Attrition is 16.87%,


"Travel_Frequently" need review, maybe those that "Travel_Frequently" need to compensate

Travel-Off Days or reschedule more evenly among the employees.


(x) VARIABLES RELATED TO ACADEMIC QUALIFICATION:

Attrition vs Education by Field

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The Highest Attrition are the ones with Bachelor Degree and Master Degree, in the Fields of Life

Science and Medical.


To focus on more Intensive Employee Engagement Activities / Intervention Program for

Bachelor Degree and Master Degree, in the Fields of Life Science and Medical Group.

Add to “Profiling PERSONAS of Employees in Attrition Group “

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(x) VARIABLES RELATED TO CAREER GROWTH IN TERMS OF SKILL UPGRADING:

Attrition vs Training-Times-Last-Year

Both Attrition and No-Attrition Group has Training 2 Times or more Last Year.

Notes: There is no indication that Number of Times attended Training influence Attrition.


Add to “No-Influence Factors on Attrition”

(x) VARIABLES RELATED TO CRTICALITY OF JOB ROLE POSITION:

Attrition vs Job Involvement

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The Employees with "High Involvement" (level 3) have Higher Attrition at 8.5%.


These “High Involvement” are “Critical Roles” which will need be readied with Backup

Candidates with Succession Plan arranged.

Add to “Profiling PERSONAS of Employees in Attrition Group“

(x) VARIABLES RELATED TO GENERAL EMPLOYEE SATISFACTION:

Attrition vs Job Satisfaction

52.79% (25.1%+27.69% level 3 & 4 Satisfaction) of Current No-Attrition Group has High, Very High Job

Satisfaction. We also have more than 50% of Attrition with High & Very High Job Satisfaction.

Notes: "Job Satisfaction" need Not review, as no much different between the 2 Groups.



Attrition vs Environment Satisfaction

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52.86% (26.6%+26.26% level 3 & 4 Satisfaction) of Current No-Attrition Group has High, Very High

Environment Satisfaction. We also have more than 50% of Attrition with High & Very High

Environment Satisfaction.

Notes: " Environment Satisfaction" need Not review, as no much different between the 2 Groups.



(x) VARIABLES RELATED TO TENURE OF SERVICE:

Attrition vs Years-At-Company

The Attrition occurred Highest during the first 10-years period (see all the red dots below those green

dots). From 15 years onwards the Attrition clearly decline and remains consistent.



Employees working less than 15 years; especially for the first 5-years.

Add to “Profiling PERSONAS of Employees in Attrition Group“

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(x) VARIABLES RELATED TO JOB SCOPE, POSITIONAL DESIGNATION:

Attrition vs Job Role

Attrition of Laboratory Technician is Highest i.e. ~27%, Lowest being of a Research Director. We can

rank Attrition % by job roles according to their Attrition.

Laboratory Technician > Sales Executive > Research Scientist > Sales Representative > Human

Resources > Manufacturing Director > Health Representative > Manager > Research Director.



Laboratory Technician, Sales Executive, Research Scientist, and Sales Representative (which

makes up more than 80% of Total Attrition – Pareto Analysis).


Attrition vs Department

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Attrition is Highest in the Sales Department and Lowest in the Research and Development

Department. We can already infer this from the Attrition in Job Roles. There we have already seen

that Sales Executive and Sales Representative in the Top 4 Roles which makes up more than 80% of

Total Attrition.


To review the Root Causes, could it be Sales Pressure or not meeting Sales Target, as then

Management can review the “Sales Training Program” for Salespersons (to improve Skillsets)

and/or “Managing Stress Therapy” (to improve handling Pressures etc) in the Sales

Department.


(x) VARIABLES RELATED TO OTHER GENERAL VARIABLES:

Attrition vs Age

It is very evident from the above graphical representation that Attrition is Higher for Younger Age

Group (e.g. 18 -34 years of age, makes up more than 50% of Attrition). Then from 35 to 60 years old,

Attrition is more stable hovering around 9.97% to 12.57%.


To focus on more Intensive Employee Engagement Activities / Intervention Program Activities

for Younger Age Group.


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Attrition vs Gender

Attrition for Male is Higher than Female, by almost 27%.

Note: This will not be a factor that may influence Attrition (decision to leave).



Attrition vs Marital-Status

Employees in Attrition Group who are Single, is Highest; at 50.63% of Total Attrition, which make sense

as generally Single has lesser Financial Commitment.

The Lowest is those who are Divorced; single parent usually is the Sole Financial Provider.

Note: This will not be a factor that may influence Attrition (decision to leave).


Company can encourage by organising more match-making activities to get the Singles to get

married, and/or provide more incentive for Married Employees.


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Overview of Employee Attrition Analytics Dashboard

The overall layout of my Analytics Dashboard with design considerations (e.g. colour schemes, style

etc), is based on Audience-Centric Infographic Design Best Practise. 5 of the Best Practises I adhere to:

1. Start with an Audience in mind.

Being Audience-Centric is key to my design, which in this case targeted for Human Capital

Professionals (i.e. GCHRO) and others who are interested in Human Capital Value Leakages

(i.e. Cost of Attrition):

a. Keeping in mind the issues (business questions) involved

b. Drilling for Root Causes Insights and thus possible solutions they need

2. Focus on Flow.

“Flow” is an important concept in infographic design. I want them to flow, both cognitively

and visually, from the top down. All my key Charts are placed at the Top, beginning with

Top Left “KPI Card”, with 2 others Charts on Top 2 Influencing Factors on Attrition namely:

Monthly Income and Promotion analysis with Years at Company. Refer to: Overview of

Employee Attrition Analytics Dashboard

3. Prioritize Typography (Font).

I have chosen easy readable Fonts, intermediating with different sizes i.e. Larger Fonts for

Key Info; this helps create a Visual Mental Pictorials that blend with the Charts. Examples

as below:

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4. Simple Colour Schemes rule.

I have chosen Contrasting Colours (Light Green also used to indicate for “No” vs Light Orange to

indicate “Yes”) to differentiate the Chart Types. It is Consistently applied for all the

Visualisations.

5. Minimalism reigns supreme.

All Visualisations has been kept to 2-4 Charts max. This is to ensure better results as

readers won’t be overwhelmed with cluttered information (only one exception is the

Summary Dashboard)

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=================================================================================

CONCLUSION

=================================================================================

Several Findings from the above Analysis:

Profiling PERSONAS of Employees in Attrition Group:

are Single (50.63% of Total Attrition), Male (63.29% of Total Attrition), working less than 10 years;

(especially for the first 5-years), has "High Involvement" (High Criticality of Role), from Sales

Department, in this Job Roles “Laboratory Technician, Sales Executive, Research Scientist,

and Sales Representative”, has Qualification Level “Bachelor Degree and Master Degree, in

the Fields of Life Science and Medical”, in the “Younger Age Group” (18 -34 years of age).

Employees in Attrition Group consisted of such Profile; this is where Attrition

occurred in the Organisation. Knowing this, they can develop more targeted

Employee Engagement Activities, more frequently and intensely. [help improve

the effectiveness of Employee Engagement Effort]

No-Influence Factors on Attrition:

Number of Times attended Training, Environment Satisfaction, Job Satisfaction

as explained in each of their respective analysis above as having No-Influence.

Factors has Potential Influence on Employee Attrition (rank in terms of Ease of

Intervention):

1. Monthly Income (Salary); need review, for those working less than 10 years (esp for

the first 5-years); against Industry Median for each Role-Appointment.

2. Overtime; working OT (genuine verified by Immediate Superior) as an indication of putting effort

and not recognised (in terms of Promotion)

3. Business Travel; need review, for those Travelling too frequently

4. Distance-From-Home; need review, especially for those stay more than 9km

5. More demanding (sales quota) pressure in Sales Dept

This is where the Organisation can develop more targeted Attrition

Intervention Program to adjust Income (Salary), Overtime, Business Travel

Frequency, Subsidy on Transport for Distance-from-Home, alleviate Sales

Work Pressure with Sales Training/Therapy etc

Recommended Next-Course-of-Actions for 2nd Phase:

Based on the 1st Round Analysis done in above sections we can ask several deeper questions to

strengthen our understanding:

1. Why are Young People leaving the company?

2. Why is attrition so high in Bachelor and Master Degree, in Life Science and Medical Fields?

3. Why is attrition so high in Laboratory Technician, Sales Executive, Research Scientist, and Sales

Rep Job Role?

4. Why is attrition high with those who Travel compare to those with No-Travel?

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5. Why is attrition high happening for those worked less than 10-yrs and income basically is lower

compare to those who stayed?

6. What is the characteristics of employees who are doing overtime?

7. What if we increase the Monthly Income by 1%, what will the Attrition Rate be?

8. Who are those Voluntarily Resigned and those being fired? Is it “push” or “pull” factors?

For answering such questions, we will need use Power BI tool to analyse multiple factors at once. This

tool allows us to interact with our data and give deeper level understanding on causes of attrition.

Further analysis can also be conducted using “What-If” function to deep-dive on simulating different

scenarios slicer made to the Variables-Factors, example: After selecting “Age” range for young employees (18-35), we can see:

• 21.95% of attrition among young employees

• Monthly income less than the median income (4K)

• Most young employees who are leaving are Sales Representative, Lab tech, Sales Exec

• 33% of them have bad work life balance

Establishing Flight Risk Indicator: From Factors identified, we can then proceed to predict Current

Cohort of Employee’s likelihood to quit (i.e. Flight Risk). We can assign Weightage to each Factor and

compute the Flight Risk (refer example below):

For DECISION MAKING: Flight Risk has to be measured against “Potential”, "Criticality of Role“,

"Successor Readiness“ etc. Risk of 75% and above, will trigger Intervention Plan

Staff A 1 2 3 4

Factors affecting Attrition Low Risk Medium Risk High Risk Critical Risk

Monthly Income (Salary) 1

Overtime 2

Business Travel 2

Distance-from-Home 2

Sales Work Pressure 3

Employee Sentiment Index 2

Flight Risk 50% Higher Index mean Higher Probability to Leave

Staff B 1 2 3 4

Factors affecting Attrition Low Risk Medium Risk High Risk Critical Risk

Monthly Income (Salary) 4

Overtime

Business Travel 4

Distance-from-Home 3

Sales Work Pressure 3

Employee Sentiment Index 3

Flight Risk 71% Higher Index mean Higher Probability to Leave

The Factors to be taken into consideration for Flight Risk is not Exhaustive

and should be reviewed regularly based on Analysis as showed earlier, align

to Business Logic

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Next, an organization must ensure it’s able to show the dollar impact. Leaders want to see the impact

of a Flight Risk Model. The statistics may be interesting but understanding not only the Cost of Attrition

and also the downstream impact of Attrition (how much is turnover directly impacting sales, customer

satisfaction, etc.?) will drive action from the Flight Risk Model. Remember on Average every 1% reduction

in Attrition is $3m replacement cost!!

Based on the Flight Risk finding, several Intervention Actions can be taken:

A great first step is conducting stay interviews within groups with High Flight Risk. “Stay interviews”

are a retention tool in which a manager conducts a one-on-one interview with employees in these

high-risk areas to uncover what he or she (the employee) does and doesn’t like that may lead to

turnover. Additional Follow-Up Actions in High Flight Risk Areas include the following:

• Online Training. Conduct training for leaders focused on factors that have been linked to Flight

Risk. For example, if teamwork and career development were found to impact turnover,

online training modules can provide learning and action lessons to drive behavioural change.

• 360 Leader Assessments. For leaders of areas of high risk, launch 360 assessments that focus

on competencies and skills to mitigate turnover and provide a better experience to their

employees.

• Pulse Surveys. Use these to gauge employee experiences linked to turnover to drive action

planning as well as to check in on the progress of action plans that are currently in place.

• Hiring Process. Evaluate the hiring process to examine if new hires are good fits for the

organization. Make sure to have the proper structure and rigor in the process (e.g.,

behavioural-based interviews, role plays, fit assessments, cognitive ability assessments). In

other words, stop turnover before it starts.

• Onboarding and Quality of Hire Surveys. Measure and connect the new hire experience to

early turnover and flight risk. Turnover is typically focused within the first year, so improving

the initial experience with the organization and identifying hires who need additional

attention is key.

Documents

Specialist Diploma in Business Analytics Specialist ... · Project Submitted by STUDENT NAME: Don Teo Hong Gee ADMIN NUMBER: 1980806B GROUP: 4. 2 ... groups “Attrition” vs “No-Attrition”