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.
This presentation highlights the required steps for HR Departments to transition themselves into a formidable HR Analytics Team. It will show how to apply HR Analytics to a departmental case as well as the required skill sets for your HR Team to acquire in order to become savvy analytics professionals. #hranalytics #humanresources
HR Analytics: New approaches, higher returns on human capital investmentShanmukha S. Potti
As global economic and political conditions continue to concern business leaders, their attention turns to the various levers that can foster success in uncertain times by looking for competitive insights to the massive data they can now capture. But to date, HR departments have lagged behind the efforts of Marketing, IT, CRM and other functions. The purpose of this paper is to show how business function leaders can start mining data to measure and improve HR's contributions to business performance.
An overview of HR analytics. The slide can be used by everyone for their learning purpose as well as in institute presentation at the last moment. All basics are being covered.
Best of Luck.
What is HR analytics and how it is done? Along with Survey on 41 individuals from different organizations with an experience of about 6 months to 27 years.
This presentation highlights the required steps for HR Departments to transition themselves into a formidable HR Analytics Team. It will show how to apply HR Analytics to a departmental case as well as the required skill sets for your HR Team to acquire in order to become savvy analytics professionals. #hranalytics #humanresources
HR Analytics: New approaches, higher returns on human capital investmentShanmukha S. Potti
As global economic and political conditions continue to concern business leaders, their attention turns to the various levers that can foster success in uncertain times by looking for competitive insights to the massive data they can now capture. But to date, HR departments have lagged behind the efforts of Marketing, IT, CRM and other functions. The purpose of this paper is to show how business function leaders can start mining data to measure and improve HR's contributions to business performance.
An overview of HR analytics. The slide can be used by everyone for their learning purpose as well as in institute presentation at the last moment. All basics are being covered.
Best of Luck.
What is HR analytics and how it is done? Along with Survey on 41 individuals from different organizations with an experience of about 6 months to 27 years.
HR Analytics: Using Machine Learning to Predict Employee Turnover - Matt Danc...Sri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/-qfEOwm5Th4.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
In this talk, we discuss how we implemented H2O and LIME to predict and explain employee turnover on the IBM Watson HR Employee Attrition dataset. We use H2O’s new automated machine learning algorithm to improve on the accuracy of IBM Watson. We use LIME to produce feature importance and ultimately explain the black-box model produced by H2O.
Matt Dancho is the founder of Business Science (www.business-science.io), a consulting firm that assists organizations in applying data science to business applications. He is the creator of R packages tidyquant and timetk and has been working with data science for business and financial analysis since 2011. Matt holds master’s degrees in business and engineering, and has extensive experience in business intelligence, data mining, time series analysis, statistics and machine learning. Connect with Matt on twitter (https://twitter.com/mdancho84) and LinkedIn (https://www.linkedin.com/in/mattdancho/).
"If there is one thing I have learned from working on Machine Learning problems in the People/HR space, it is this: define and structure your problem up front!"
Keith McNulty
Big Data, Business Intelligence, HR Analytics - How they are related?Shojibul Alam Shojib
Big data, business intelligence, and HR analytics are three buzzwords that are frequently talked about. Do you really know what they mean? And what added value does big data and business intelligence bring to the field of HR?
Predictive Analytics for HR: A Primer to Get Started on your HR Analytics Jou...Dr Susan Entwisle
Traditionally, HR teams have made decisions on hiring, retaining, assigning and developing employees using intuition, experience, and basic descriptive statistical reports. Predictive analytics complements and extends on these approaches by enabling HR teams to make proactive ‘forward-looking’ data-driven decisions on its people across the employee lifecycle. Examples of this include gaining insights into the drivers and predicting who are our top performers, what employees are at risk of leaving, is our training program effective, and more. This capability can support HR teams to better align HR programs with strategic business goals.
This presentation outlines the limitations with current approaches and explain what predictive analytics is so business users can understand the business opportunity and problems it can be applied to. A number of case studies on its use across the employee lifecycle are described and guidance given on how to get started on your HR predictive analytics journey.
Workforce analytics, also called HR analytics or people analytics is getting much attention lately. And rightly so! Research has shown that companies using data to drive their decisions and actions are more succesfull than others. With (predictive) analytics an accurate view of the future requires predictions based on data rather than personal hunches or speculation.
The world is fueled by data, and HR professionals everywhere are wondering how to leverage tons of people data for better insights to enhance individual and organizational performance.
HR analytics entails the use of tools (say, big data, predictive analytics) by HR in their recruiting, compensation, performance measurement, and retention efforts.
Through this presentation, you will get an introduction to HR analytics and how you can make the most of it to drive sweeping strategic success. This presentation will address the following areas of the employer branding:
- Purposeful Analytics
- Basics of Data Analysis
- Understanding the Fundamentals of Analytics Capability
Building
- Establishing an Analytical Unit and the Right Culture
- Levels & Types of HR Metrics
- Linking Metrics to Analytics
- Workforce Analytics Model
For more info:
www.hackerearth.com/recruit
Strategic Workforce Planning (SWP) is the most sought after skill in talent management today. Master this critical skill so you can move your career and your organization's objectives forward simultaneously.
In this webinar you will learn:
The essential steps in SWP
How to identify talents gaps and
Actions to take to close those gaps.
And more…
In the fast-changing world of corporate recruiting, it’s important to be aware of and prepared for the problems and opportunities that you will soon face. In short, because it’s “better to be prepared than surprised”, both recruiting and hiring managers must find a way to be “proactive” in planning for these upcoming events, rather than being “reactive”. The most effective way to identify trends and to predict upcoming recruiting issues is through the use of analytics and predictive metrics This advanced webinar will be led by long time ERE.net author and global metrics expert Dr. John Sullivan. He will guide you through the goals, the action steps and the best emerging corporate practices in predictive recruiting metrics.
HR Analytics: Using Machine Learning to Predict Employee Turnover - Matt Danc...Sri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/-qfEOwm5Th4.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
In this talk, we discuss how we implemented H2O and LIME to predict and explain employee turnover on the IBM Watson HR Employee Attrition dataset. We use H2O’s new automated machine learning algorithm to improve on the accuracy of IBM Watson. We use LIME to produce feature importance and ultimately explain the black-box model produced by H2O.
Matt Dancho is the founder of Business Science (www.business-science.io), a consulting firm that assists organizations in applying data science to business applications. He is the creator of R packages tidyquant and timetk and has been working with data science for business and financial analysis since 2011. Matt holds master’s degrees in business and engineering, and has extensive experience in business intelligence, data mining, time series analysis, statistics and machine learning. Connect with Matt on twitter (https://twitter.com/mdancho84) and LinkedIn (https://www.linkedin.com/in/mattdancho/).
"If there is one thing I have learned from working on Machine Learning problems in the People/HR space, it is this: define and structure your problem up front!"
Keith McNulty
Big Data, Business Intelligence, HR Analytics - How they are related?Shojibul Alam Shojib
Big data, business intelligence, and HR analytics are three buzzwords that are frequently talked about. Do you really know what they mean? And what added value does big data and business intelligence bring to the field of HR?
Predictive Analytics for HR: A Primer to Get Started on your HR Analytics Jou...Dr Susan Entwisle
Traditionally, HR teams have made decisions on hiring, retaining, assigning and developing employees using intuition, experience, and basic descriptive statistical reports. Predictive analytics complements and extends on these approaches by enabling HR teams to make proactive ‘forward-looking’ data-driven decisions on its people across the employee lifecycle. Examples of this include gaining insights into the drivers and predicting who are our top performers, what employees are at risk of leaving, is our training program effective, and more. This capability can support HR teams to better align HR programs with strategic business goals.
This presentation outlines the limitations with current approaches and explain what predictive analytics is so business users can understand the business opportunity and problems it can be applied to. A number of case studies on its use across the employee lifecycle are described and guidance given on how to get started on your HR predictive analytics journey.
Workforce analytics, also called HR analytics or people analytics is getting much attention lately. And rightly so! Research has shown that companies using data to drive their decisions and actions are more succesfull than others. With (predictive) analytics an accurate view of the future requires predictions based on data rather than personal hunches or speculation.
The world is fueled by data, and HR professionals everywhere are wondering how to leverage tons of people data for better insights to enhance individual and organizational performance.
HR analytics entails the use of tools (say, big data, predictive analytics) by HR in their recruiting, compensation, performance measurement, and retention efforts.
Through this presentation, you will get an introduction to HR analytics and how you can make the most of it to drive sweeping strategic success. This presentation will address the following areas of the employer branding:
- Purposeful Analytics
- Basics of Data Analysis
- Understanding the Fundamentals of Analytics Capability
Building
- Establishing an Analytical Unit and the Right Culture
- Levels & Types of HR Metrics
- Linking Metrics to Analytics
- Workforce Analytics Model
For more info:
www.hackerearth.com/recruit
Strategic Workforce Planning (SWP) is the most sought after skill in talent management today. Master this critical skill so you can move your career and your organization's objectives forward simultaneously.
In this webinar you will learn:
The essential steps in SWP
How to identify talents gaps and
Actions to take to close those gaps.
And more…
In the fast-changing world of corporate recruiting, it’s important to be aware of and prepared for the problems and opportunities that you will soon face. In short, because it’s “better to be prepared than surprised”, both recruiting and hiring managers must find a way to be “proactive” in planning for these upcoming events, rather than being “reactive”. The most effective way to identify trends and to predict upcoming recruiting issues is through the use of analytics and predictive metrics This advanced webinar will be led by long time ERE.net author and global metrics expert Dr. John Sullivan. He will guide you through the goals, the action steps and the best emerging corporate practices in predictive recruiting metrics.
Big Data = Big Headache? Using People Analytics to Fuel ROItalent.imperative
• Interpret trend information to understand the business case for Big Data in HR.
• Examine your fears and assumptions about Big Data.
• Learn from best practice case studies how to demonstrate HR’s contributions to ROI.
• Understand how to engage key stakeholders as part of your organization’s people analytics journey.
This is a presentation that I gave recently to a group of over 300 HR recruiting professionals at a large consulting company. Thought it might be of help to others.
Workforce Insight And Change Making Comms V4 AaAladam
These slides provide an overview of how to do Workforce Planning & Analytics so leaders can make better, more informed decisions. Hope you like it!
Enabling Success With Big Data - Driven Talent AcquisitionDavid Bernstein
Adopting an evidence-based recruitment marketing strategy is not just reserved for large employers. In fact, a targeted sourcing strategy can in some ways have a greater impact on small and mid-size businesses who need to allocate already-limited resources to the areas that will provide the most value. Ultimately, hiring the right candidate means profitability for your business. How can talent acquisition professionals gain the insights their organizations need to make better-informed decisions about their recruitment marketing efforts?
Embracing Technology to Help Attract, Develop and Retain Talent, Mike FadelThe HR Observer
Human Resources is already the heart of the organization today as a Talent producer, developing and supporting Talent on a regular basis. But now, leading HR Departments are transforming into a “Talent Business Operations” function that is able to support major transformations and growth in the company and be a Partner to the business. The traditional HR model is fading away, and companies are looking for ways to transform their HR teams, technologies and skills.
In this session you will learn and understand how Technology is aligned to business part of the HR transformation journey.
This presentation was used at HR Summit and Expo 2013 www.hrsummitexpo.com
Making the Cut: A Review of Open Talent Analytics Job PostingsAndrea Kropp
A systematic analysis of Talent Analytics position descriptions aimed at revealing the backgrounds, skills, competencies and responsibilities most central to the role and how some requisitions stand apart from the crowd.
What is People Analytics - PPT | SplashHRSwati Gupta
People Analytics is a process in which the company’s data is transformed into insights. People analytics, also known as HR or talent analytics, uses analysis to help decision-makers interpret business and people data to improve the impact on business goal – and assess human resources initiatives’ effectiveness.
Max Blumberg: How can #PeopleAnalytics prevent incidents like the Twitter fir...Edunomica
Max Blumberg: How can #PeopleAnalytics prevent incidents like the Twitter firings?
People Analytics Conference 2022 Winter
Website: https://pacamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/pacamporg
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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
3
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…..
5
6. HR is Beyond Statistics ……………
6
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
7
8. Stages of Analytics
8
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.
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
11
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
12
Accuracy : TP+TN / (TP+FP+FN+TN)
14. Integrated View - HRMS, Micro, BOTs & Analytics
14
» 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
15
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
17. Step 1 for Analytics - How AI and ML will work?
17
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
19. 19
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
20
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
26. 26
Head Count and Attrition Report – Predictive
https://hris-demo.stratemis.com/ess
27. 27
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
28
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
29
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
30
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
31
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
36. To drive Analytics, HR need not be Statisticians…..
36
• Who is providing the Data ?
• Who is processing the data ?
• Who is benefitting from the data ?
38. About Stratemis
38
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
39
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