Diving into the space of HR automation and understanding the role of Analytics and Bots in prioritizing and streamlining HR functions with efficiency to the uplift and upkeep the Business Profitability as a whole.

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Data Analytics – HR
2020

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3.  Introduction
 Breaking AI into HR Context
 Overview of Data Analysis
 Use case of AI – ML, NLP, Image Processing and Sentiment Analysis
 Flow of data from unstructured sources
 How doe we convert Unsupervised data to Supervised Information
 Case study from Real time implementation of Analytics
 How BOTS and Analytics is the new norm for 2020 +
 How do we use AI - Libraries in HR context
 Questions and Answers
What will we cover
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Let me Start with a Short Story …..

5. Classification Problems
• “Will my candidate join - The candidate was excited”
• “I thought the JD was correct but there seem to be a problem with the real work
against the JD “
• “Do I have a Heat Check method to gauze the joining probability”
• “Are my employees Engaged?”
• “How can I increase my Training Participation”
• “Can I know my capacity numbers before hand”
• “Are my assumptions and Hypothesis correct and mapped with exact results”
• “Is My succession Planning method adding to the business cause of Cost and
Returns “
• “Can I wear the HAT of CXO”
Do I have the right model
• Effective Model to Describe Data flow
• Effective Model to Predict Performance.
• Effective Model to Predict Trends
Types of Problems we face in our daily life…..
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6. HR is Beyond Statistics ……………
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Should I have Mastered Statistics rather than
being in HR?
The Subject of HR is Very Different in different
industry Context
Why can’t management distinguish
Administration and the subject of HR

7. Let’s See how can we break Analytics into Simple Steps
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8. Stages of Analytics
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Graphs , Visualizations
helps us understand the
various trends in the HR.
Performance Trend ,
Leaves Pattern , Attrition
Trends
Important as it gives a
deep dive and provide
us with insights or
reasons for various
trends.
If the present conditions
carries on then what
might happen or what it
could lead to if the current
process follows on. Recommends
possible course
of actions
while guiding
to a solution.

9. Let’s Begin with the concept of HR Analytics..
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10. Some technicalities of Analytics …..
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11. Assumptions :
1. Linear Relationship
2. Multivariate Normality
3. No Multi- Collinearity
4. Homoscedasticity
• Y the dependent variable.
• X1 , X2 , X3 are the various independent variables on which the value of y depends.
• M1, M2, M3 the coefficients of dependent variables.
• b the y intercept or the error term.
• We use a sample data what we called the training data set. Try to predict or find the values of the above
formula.
• Accuracy is checked by the “r squared” value. If r2>0.75 we can say it model is good. The higher
the r square the more accurate is my model for predicting Y.
• Significance of X variables can be checked by the p value . If the p value <= 0.05 the variables are
considered very significant for the model.
• Now, once we get the values on the above formula and the accuracy is to our liking we can use the
model (above formula ) to predict Y for the population.
Simplifying HR through Statistics
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12. Usage of algorithm like :
• Logistics Regression , Naïve Bayes , Decision tree , Random Forest
which ever model shows a good accuracy can be used to predict
classification. Could be used to predict attrition etc.
• The accuracy of the model for classification models is checked by the Confusion Matrix.
Simplifying HR through Statistics
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Accuracy : TP+TN / (TP+FP+FN+TN)

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Use case context of data flow by
understanding AI from an HR Angle

14. Integrated View - HRMS, Micro, BOTs & Analytics
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» Reduce Recruitment TAT
through ML and NLP
capability in JD creation,
Sourcing, Assessment and
Engagement
» Create digital directories of
candidates and employees
» Implement automated
‘Web Check-in’ process for
Onboarding
» Link employee to a ‘Drag-
n-Drop’ Org chart
» Track assets assigned to
employee
» Define and initiate Early
15-30-45-60-90 days
feedback
» Digitally create Employee
Road Map - 365 days and
beyond
» Create Confirmation
Assessment Profile
» Employees’ View of
documents, letter, policies
and forms
» Managers’ view of employee
records
» Geo Tagging and Fencing
for Attendance
Management
» Capability to launch
Interactive bots
» Enable employee to access
from both web and mobile
» Conduct Employee Stay
Interview Process
» Conduct 360 and Skip to
understand supervisor
effectiveness
» Conduct Behavior
Analysis through digital
engagement (incl. BOTs)
» Identify Training Needs
and create Career
Progression road map
» Conduct Assessment
Center
LMS
» Assign learning Modules
» Publish training calendar
& Assessment centers
» Integrate Digital Library
with open content through
API
» Design Instructor Led
Workshops
» Launch Survey, Quizzes
and Feedback
» Dynamic reporting
» Conduct Webcast and
Webinars
Exit Management
» E-Exit BOT to provide
discussion points to HR
» Exit Bot for Paperless
Employee Exit
» Online Departmental
Clearance
Integrated HRMS Platform for end-to-end
employee Life Cycle Management
Employee
Directory
(ESS)
Recruiting
& Digital
Onboarding
Attendance
& Leave
Mgmt.
Employee
Mgmt. Feedback &
Retention
Performance
Evaluation
Learning
Management
System
Employee
Exit

15. How AI and ML will be the part of the Entire Life Cycle of
Employee to Simplify
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Pre Joining
Data set 1
AI based
Sourcing
ML based CV
Analysis
Competency
Based Profiling
Gap
Mitigation
against JD
Profiling
Profiling
Intimation to
all
Department
Joined
Data Set 2
Transition –
Pre Joning
Induction
30 Days
Data Set 3
Early Stage
Stay Interview
LMS
Governance
Knowledge
Transfer
60 Days
Data Set 4
Early Stage
Stay Interview
LMS
SKIP
Knowledge
Transfer
Performance
90 Days
Data Set 5
Early Stage
Stay Interview
LMS
SKIP
Knowledge
Transfer
Performance
Profiling
Gap Mitigation
180 Days
Data Set 6
Early Stage
Stay Interview
LMS
SKIP
Knowledge
Transfer
Performance
Profiling
Gap Mitigation
Building Road
Map
Conversion of Unsupervised data to Supervised Data in ML FOR Predictive profile
PREDICTABILITYPROFILE

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Lets look at the real context
Step 1 Step 2

17. Step 1 for Analytics - How AI and ML will work?
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TA posts a position on
Career/Opening
Portal
TA Receives
Job Application/
Resume
TA Analyze the
CV and compare
with the required
JD Documents
TA finds the
suitability
Thank you for
your interest
No
Candidate receives
an email for first
round of Interview/
screening
Yes
Interview
gets
Scheduled
An info email goes
to Interview
panelist
Interview took
place and panelist
published the
scores/feedback
Selection
Status
Negative/Disqualified
BOT
Process
Positive / Qualified
QR Code gets generated
and information goes to
SPRINGBOARDManual
+
Web Based
input
TA Team · BGVC Check
· Profile Check
· JD ANalysis
Supervised Data Set
Machine Learning
Game Changer – NLP and
ML and Indexing

18. Step 2 - Flow of Employee data -1 – 180
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Use case– Descriptive and Predictive HR Data and the Game
Changers
https://www.stratemis.com/analytics-as-a-business

20. Recruitment Applicant Tracking – Descriptive Dashboard
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Lets Build the JD through ML and NLP
How do we use NLP Libraries Support Prescriptive Analytics
https://hris-demo.stratemis.com/ess

21. Probability of his joining the company / organization
Bases on the positive polarity scores
Recruitment Applicant Tracking– Predictive Dashboard

22. • 5 Engagement Positive Polarity Scores of the candidates
• Positive Polarity would give us a more promising figure to know “How Positive he is in
joining.”
• Polarity out of 20 conversational points .
• We have used Sentiment Analysis on his responses to calculate the positive polarity.
Recruitment Applicant Tracking– Mechanics

23. 30 % weightage
from Engagement 1
30 % weightage from
Engagement 2
20 % weightage from
Engagement 3
10 % weightage
from Engagement 4
10 % weightage from
Engagement 5

24. Probability of his joining the company / organization
Bases on the positive polarity scores

25. Head Count and Attrition Report – Descriptive
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Head Count and Attrition Report – Predictive
https://hris-demo.stratemis.com/ess

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Use case HR – How do we reach to a Hypothesis To Predict
Attrition
https://www.stratemis.com/analytics-as-a-business

28. Attributes to Attrition or Performance - Influencers
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Attributes 1 3 5
One on One assessment - Concerns-Redeployment/
Night shift/ Family problem/ Outstn FTE/ Training
Issues / Marriage / Salary/ Higher education/
Others (If others, substantiate the reason on
comments section)
Any of the mentioned concerns raised repeatedly/ still
to be addressed
Raises concern but concern addressed No concerns raised/ no sign
Performance
Visible dip in performance along with concerns on other
stated attributes.
Overall consistent performance, however with a
few sporadic instances of low quality.
Highly motivated and good performer.
Leave
Long leave/ Dubious leave/ Unplanned / Unscheduled
leave
Leave extended (Short or long), not planned in
advance (appears genuine)
No Unsched Leave taken
External Interviews Heard from the associate himself/ herself
Have a haunch (word on the floor)/Confirmed
from other sources
No sign
Behaviour/ Motivation
Energy drop/spreads negative attitude
Indifference towards process.
Close to associates with negative energy. Not open to
changes
Low energy/ tends towards negative attitude
but can be addressed through pep talk
No participation in process related extra
activities. Does not have a negative approach to
work but needs to b
Extremely high energy, positive attitude
Willingness to take on higher responsibility.
Takes initiatives and shows the crave for
learning more. Has the right appraoch
Personal Effectiveness
Has not taken any initiatives with regards to process
improvement.
Does not believe in team work.
Has major issues with reporting manager.
Has potential for additional responsibilities ,
needs to be pushed & delegated. Only Meets
the performance criterion.
Extensively involved in projects , additional
responsibilities & keen to take On
assignments.
Got promoted within last year. Helps the
manager
Career Growth
Has been in the same role for more than 2 years and
strongly feels that Career growth is over-due
Has been in the same role for more than 18
months and has indicated that Career growth
is due
No Issues
Skill set
Skill set Mismatch
New to the industry and mentioned the difficulty in
understanding the process, once the employee is on the
floor post successful completion of OJT.
Has experince in the industry but needs
coaching and supervision.
Is taking time to come up the learning curve
No Skill Issues
SIPOC Question before Modelling
1. Who is providing the Data ?
2. Who is processing the data ?
3. Who is benefitting from the data ?

29. Attributes to Attrition or Performance - Contributors
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Reference Guide for Attribute no. 1 (as stated above)
One on One assessment - Concerns-
Redeployment/ Night shift/ Family
problem/ Outstn FTE/ Training Issues /
Marriage / Salary/ Higher education/
Others (If others, substantiate the reason
on comments section)
1 3 5
Redeployment ( to other process / business)
or Relocation ( to other location)
Actively seeking redeployment / difficulty in
redeploying
Has talked about redeployment though not
pursuing actively
No issues
Marriage in offering Would definitely leave after marrriage Might leave… No issues
Night shift
Has health problems due to night shifts…asked
persistently for a day shift
Has mentioned about moving to day
shift…not persistent
No issues
Family problems
We know these are causing problems at
job…performance, late to work, etc
Seems to be able to cope No issues
Salary
Extremely dissatisfied has been talking about it on
a regular basis.
Was dissatisfied, after discussion seemed
satisfied and has not raised it again
No issues
Higher education
Studying for entrance exam, might leave once
he/she clears the same
Mentioned interest in higher
education…might be applying for the same
No issues
Medical problems Severe problems interfering with work Problems…able to cope with difficulty No issues
Warnings - diciplinary, behavioural
Given more than one warning on discipline
/behaviours to be followed
Given warning on discipline /behaviours
to be followed and shows good
improvement
No issues
Maternity
Asking for frequent leaves or too many last minute
leaves in the initial period of maternity.
Staying alone with husband & worried
about lack of support at home in terms of
Child care.
No issues

30. Legends to Determine the Output
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Legend
1 High Risk
Red - Attrition anticipated in the next 1 month, immediate attrition
(we know that the employee has gone for an interview, told
someone that he/she is going to leave, patterns in absenteeism that
reflect poor engagement, or any other reason etc)
3 Medium Risk
Yellow - Attrition anticipated in the next 30-90 days. Factors that
can lead to the employee leaving the job in near future but don’t
expect it to be immediate. (Slow but steady signs of poor
engagement, lacking attention to details, lack of ownership,
increased error rate etc., contrary to the individual's usual self)
5 Low Risk
Green - Attrition not anticipated, required levels of engagement
seen.

31. Supporting Enablers for HR to derive to a Attrition
Analysis – Prediction
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 BOTS Engagement Data
 Demographic Data
 Supervisor Data
 Individual Engagement Data
 360 Feedback
 Stay Interview
 Leaves (Absenteeism)
 Performance
 Motivation measure check
 Learning Agility
• Compensation – Competition
Compa ratio and Performance
Tertile
• Supervisor and Mentor

32. Interaction 30 Days

33. Interaction 90 days

34. Interaction 180 days

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Attributes to Attrition or Performance and Behavior
Influencers BOX Model

36. To drive Analytics, HR need not be Statisticians…..
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• Who is providing the Data ?
• Who is processing the data ?
• Who is benefitting from the data ?

37. Our Digital Solutions & Enablers
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38. About Stratemis
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The only
SaaS based
HRMS
technology
provider with
Stratemis is an agile and intelligent HRMS platform, focused at making HR processes
intuitive for operating managers and employees
1. 12 Core modules and
2. 12 Micro-service modules
Enriched by:
A. ML based BOTs and
B. Intuitive and Predictive
Analytics
Our platforms bring together all constituents – Employees, HR Managers, Operations
Managers – on to a single, easy to use and intuitive operating environment
The only SaaS based
HRMS platform
providing complete
Employee Life Cycle
Management

39. Stratemis’ Promise and Commitment
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Stratemis’ Beliefs
 Pace
 Passion
 Transformation
Stratemis’ Purpose
Help customers achieve HR
digitalization with marginal cost
& minimal disruption
Stratemis’ Promise
To Provide best in class AI
driven modular HRMS
framework
Stratemis’ Commitments
Meet customers’ HRMS requirements
through a single set of platforms.
Achieve customers’ HR Digitalization
objectives through Configurable and
Modular platforms
Help implement highly configurable and
agile HR processes across all industries
Minimize Customers’ CapEx
Ensure smooth implementation
through our published API

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Thank You!

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