28-July-2016 Building Predictive Attrition Models · 28-July-2016 ABOUT CEB’S ATTRITION PREDICTION WORK CEB’s Process: Hypothesis-Driven Predictive Analytics Who We Worked With

28-July-2016

CEB Corporate Leadership Council™

Building Predictive Attrition Models

Featuring HCL TechnologiesWebinar15 February 2017

28-July-2016

A Framework for Member ConversationsThe mission of CEB Inc. and its affiliates is to unlock the potential of organizations and leaders by advancing the science and practice of management. When we bring leaders together, it is crucial that our discussions neither restrict competition nor improperly share inside information. All other conversations are welcomed and encouraged.

Confidentiality and Intellectual PropertyThese materials have been prepared by CEB Inc. for the exclusive and individual use of our member companies. These materials contain valuable confidential and proprietary information belonging to CEB, and they may not be shared with any third party (including independent contractors and consultants) without the prior approval of CEB. CEB retains any and all intellectual property rights in these materials and requires retention of the copyright mark on all pages reproduced.

Legal CaveatCEB Inc. is not able to guarantee the accuracy of the information or analysis contained in these materials. Furthermore, CEB is not engaged in rendering legal, accounting, or any other professional services. CEB specifically disclaims liability for any damages, claims, or losses that may arise from a) any errors or omissions in these materials, whether caused by CEB or its sources, or b) reliance upon any recommendation made by CEB.

!3© 2013 –2017 CEB. All rights reserved. CLC170908

28-July-2016

AGENDA

Objectives ■ Understand the key imperatives for building an attrition prediction model

■ Learn which predictors of attrition are the same across organizations

■ Learn how HCL approached its creation of an attrition prediction model

■ Understand which actions to take, and pitfalls to avoid, when building any predictive model

ContactPlease contact us with any questions you have following the session.

Robin BoomerExecutive Advisor, [email protected]

Gaurav VasuGlobal Market Intelligence and Analytics Lead, HCL Technologies

Neha JainResearch Director, [email protected]

Timing

Introduction 5 minutes

Five Imperatives for 20 minutes Developing Attrition Prediction Models

Common Predictors 10 minutes of Attrition Across Organizations

HCL’s Hypotheses-based 10 minutes Approach to Building An Attrition Prediction Model

Q&A with Gaurav Vasu, HCL 15 minutes

mailto:rboomer%40cebglobal.com?subject=

mailto:nejain%40cebglobal.com?subject=


28-July-2016

ATTRITION PREDICTION IS A PRIORITY FOR MOST TALENT ANALYTICS TEAMSMost Organizations Plan to Work on Attrition Prediction Projects

n = 212.Source: CEB 2017 Talent Analytics Agenda Poll.

61% of organizations rank attrition and retention as a high priority topic for 2017.

!5

28-July-2016

© 2013 –2017 CEB. All rights reserved. CLC170908

ANALYTICS INSIGHTS OFTEN DON’T DRIVE ACTION

n = 1,590.Source: CEB 2013 Business Barometer.

Limited Adoption of Analytics Insights

Project # xxxxxx

Catalog # CLC167269PR

Year Range xxxx–xxxx

15% of senior business leaders report that HR analytics has led them to change a business decision in the past year.

Executive Perspective:There’s a lot of data out there, but not a lot of information.


28-July-2016

ROADMAP

Five Imperatives

for Developing

Attrition Prediction

Models

Common Predictors of Attrition

Across Organizations

HCL’s Hypotheses-

Based Approach to Building an Attrition Prediction

Model

Q&A with Gaurav Vasu,

HCL


28-July-2016

ABOUT CEB’S ATTRITION PREDICTION WORK

CEB’s Process: Hypothesis-Driven Predictive Analytics Who We Worked With

■ Define key management challenges or questions

■ Create hypotheses ■ Collect required data ■ Analyze data ■ Review analysis and outputs ■ Leverage CEB insights to develop actions

Three attrition analytics projects completed across 2016:

■ Organizations from three distinct sectors: International Food Conglomerate, Pharmaceuticals, and Engineering Services.

■ Data sets sizes ranging from 200–20,000 employees

■ Attrition rates observed among talent segments: 11%–32%

Source: CEB analysis.

CEB Talent Management Labs Overview

The Labs build and test predictive analytics models and algorithms in close collaboration with our clients’ talent experts and business leaders. They use research and analytics rigor to identify solutions to clients’ pressing talent management problems by modelling the underlying drivers of and predicting key talent outcomes.

What have we learned that will help talent analytics teams develop their own attrition prediction models?

?


28-July-2016

COMMON CHALLENGES FROM DATA ACQUISITION TO SOLUTION DELIVERY

Talent analytics teams lack understanding of business needs, which hinders identifying the right challenge at the outset.

Identifying the Challenge1

Talent analytics teams struggle to identify and then acquire the right data. HR doesn’t own all the data.

Acquiring the Right Data2

Data can be low quality and messy. Some common issues are missing values, lack of consistency, or purges by one team without knowledge of other teams.

Overcoming Low Data Quality3

Balancing Actionability with Precision4

In most cases, line and business leaders prefer easy-to-interpret results, and they are willing to trade off precision and specificity for ease-of-use and actionability.

Models alone don’t drive action; business leaders need help translating predictive analytics into practical solutions.

Communicating Insights5



28-July-2016

FIVE IMPERATIVES FOR DEVELOPING ATTRITION PREDICTION MODELS


Tie Attrition Prediction to a Clear Business Need1

Identify Data Requirements and Map Them to the Right Data Owners2

Diagnose and Address Data Quality Issues Before Starting a Project3

Focus on Employee Factors the Business Can Drive4

Translate Results into Insights and Action Steps5


28-July-2016

Imperative 1

Tie Attrition Prediction to a Clear Business Need


28-July-2016

ATTRITION CHALLENGE IS TOO LOOSELY DEFINEDCommon Project Starting Point

I want to know if our high attrition can be fixed through data. Can you show me what we can do about it?

Our high-performing frontline retail staff in Singapore are leaving us earlier than in the past, causing branch productivity to go down. How can we

solve this through data?

Talent Analytics Leader

HRBP South East Asia

Ideal Project Starting Point



HRBP South East Asia

Analytics results are not put into use because:

■ They are too high-level to be translated into specific actions

■ They are misaligned with the leader’s talent priorities for his business unit

Result

Analytics results can be turned into actions because:

■ The business need is clearly articulated ■ The leader identifies a specific talent segment to focus the investigation on

■ The team knows what type of attrition it should address

Result


28-July-2016

TIE ATTRITION PREDICTION TO A CLEAR BUSINESS NEED

Drill down into the type of attrition the business leader cares about, for example voluntary, regretted turnover or involuntary turnover.

Ensure business and line management’s involvement in project scoping and throughout the stages of model creation and analysis.

Start thinking of change management needs at the beginning of the process, not only once insights have been created, to increase stakeholders’ acceptance—and application—of findings.

Look for opportunities to gain business input during the scoping stages and embed it into hypotheses and metrics.

Establish a clear link between variables used for analysis with hypotheses that the business wants to test.

Investigate relatively homogenous talent segments based on parameters such as work location, job profile, or level.

Ensure analytics results tie to business impact (i.e., cost savings, revenue generation, customer satisfaction), not just traditional talent outcomes (i.e., productivity, turnover, time to hire).

Talent Analytics LeadersFocus the model on solving business

problems, not talent problems, to deliver business impact.

TO DOs

Talent Analytics StaffEnsure that hypotheses, variables, and results

can be clearly linked to business needs and outcomes.


28-July-2016

Imperative 2

Identify Data Requirements and Map Them to the Right Data Owners


28-July-2016

NEEDED DATA HARD TO IDENTIFY, HARDER TO ACCESSData for Attrition Models Sit Across Multiple Systems and Platforms

What Makes It So Difficult to Access the Required Data?

■ HR doesn’t own all the data.

■ The core HRIS doesn’t contain information on all crucial predictors of talent outcomes.

■ Each system comes with its own custodians and data taxonomies, leading to incomplete, inconsistent and fragmented data.

■ HR’s interface with these custodians (e.g., IT, Finance, Customer Contact) is limited or non-existent.

■ Unstructured data is even harder to access and retrieve.

Demographics EngagementSkills and

CompetenciesCompensation and Rewards

Leadership Quality

Peer Interactions

L&D

Manager Quality

Performance GoalsLeave/

Time offData Owned by HR

Employee Costs

Incentive and Goal Estimates

Revenue

Technology Usage

Internal Social Media Tools

Data Owned by Business and Other Stakeholders

Economic Indicators

Employment Brand

Competitor Investments

Job Ads

External Data



28-July-2016

IDENTIFY DATA REQUIREMENTS AND MAP THEM TO THE RIGHT DATA OWNERS

Work closely with the business to identify the right sources of data based on shortlisted hypotheses.

Bring a business leader on board to help you make the case for accessing data owned by other functions.

Engage analytics leaders in other functions to identify “hidden” sources of talent data and learn how to leverage them.

Build a map of possible data sources the organization has access to. Engage HR and business stakeholders to identify their rightful owners.

Establish informal relationships or networks with data owners and/or other internal analytics functions to make it easier to gain access to their data or expertise.

Look beyond structured data sources, and see how unstructured data (resumes, performance feedback, learning feedback, etc.) can be used to predict attrition.

Talent Analytics LeadersEngage business leaders to identify relevant

data sources and to act as sponsor.

Talent Analytics StaffKeep an up-to-date list of data sources across

the organization and build relationships with data owners.

TO DOs


28-July-2016

BUILD AN INVENTORY OF POSSIBLE DATA SOURCESSample Inventory of HR Data Sources

Attraction and Selection

Learning, Development, and Onboarding Performance

Retention/ Engagement

Stru

ctur

ed

■ ATS ■ Assessments ■ HRIS ■ HRBP Feedback ■ Education Data

■ L&D System Data/LMS

■ Internal Academy ■ Learning Plan ■ Learning Effectiveness Survey

■ Onboarding System ■ Onboarding Effectiveness Survey

■ Performance Plan ■ Performance Reviews (Self/Manager/360)

■ KPIs/Scorecards ■ Manager Effectiveness Survey

■ Performance Management ■ Customer Performance ■ Work Samples/Documents

■ Compensation System ■ Time On/Off Tracking System

■ Employee Engagement Survey

■ Manager Effectiveness Survey

■ Exit Interview Data

Uns

truc

ture

d

■ Selection Process Data (Candidate resumes, cover letters, interview scores and, feedback from interviewer/hiring manager)

■ Feedback on Hiring Manager Survey

■ Feedback on Hiring Process

■ Alumni Circles/Groups

■ L&D Effectiveness Survey Text/LMS

■ Learning Needs ■ Informal Learning ■ L&D Program Feedback

■ Trainer/Training Feedback

■ Onboarding Process Feedback

■ MBOs/Objectives ■ Manager Effectiveness Feedback

■ 360 Degree Feedback ■ Customer feedback/complaints

■ Past performance appraisals ■ Manager reviews ■ E-Mail/Calendar Data ■ Internal Social Media ■ Voice and Video Recordings

■ Employer Value Proposition Data

■ Org-Wide Performance System

■ Exit Interview Data ■ E-Mail/Calendar Data ■ Internal Social Media



28-July-2016

Imperative 3

Diagnose and Address Data Quality Issues Before Starting a Project


28-July-2016

DATA QUALITY ISSUES INHIBIT ANALYTICS PROJECTS

Data Quality: Data’s fitness to serve its purpose in a given context.

54% of Heads of Talent Analytics cite “Data Quality” more than any other issue as being a barrier to

talent analytics effectiveness.

60% of Heads of Talent Analytics cite “Improving Data Quality” as their top talent analytics priority

for 2017.




28-July-2016

DIAGNOSE AND ADDRESS DATA QUALITY ISSUES BEFORE STARTING A PROJECT

Define data quality standards for individual datasets.

Assign designated data stewards with formal responsibility for defining, maintaining, and improving the quality of the data in each dataset.

Incorporate measures of data quality in dashboards and reporting that use the data.

Create simple data handling checklists for data quality issues.

Highlight examples of how possible quality issues can be resolved and ensure all datasets are being audited using the same level of scrutiny.

Ensure data “fixes” are the same across all datasets.

Escalate data quality issues early-on, even if this causes delays in new project launches.

Know when to stop searching for “perfect data” and how to apply judgment to the data you have.

Talent Analytics LeadersSet data quality standards and drive

accountability for it within and outside of your team.

Talent Analytics StaffCreate and disseminate a structured process

to ensure data quality across all datasets.

TO DOs


28-July-2016

CREATE SIMPLE DATA HANDLING CHECKLISTS Sample Data Cleaning Checklist (Excerpt)

Sequence/Order of Audits Type of Audit Examples Sample Solutions

Rule 1 Duplicate IDs Unique ID should be used for each record (e.g., employee ID)

■ Remove all blank or duplicate identifiers ■ Ensure re-hires have separate identifiers —and

records associated with them for their two tenures ■ …

Rule 2 Key Identification

Employee X’s record should have the same ID in all the systems being looked at

■ Match all records for the datasets that cover all employees, using a primary dataset.

Rule 3 Domain Checking

Management Level is only Junior, Mid or Senior (anything else needs to be standardized in these three categories)

■ Code fields to only allow limited responses

Rule 4 Range Checking

Work Experience should be only between 0–60 (anything below 0 or above 60 should be excluded)

■ Set limits to eyeball these values and recode outliers to “missing”

Rule 5 Format Consolidation

Date should be in one single format throughout. Revenue figures should be in a standard format

■ Transform all dates to the same format internally ■ Transform all revenue values into the same currency

based on the current exchange rate ■ Keep raw format as well

Rule 6 Name Standardization

Location can be Mumbai only, not Bombay

■ Use the most common representation to standardize names of previous employers, educational institutions, degrees, etc.

Rule 7 Handling Missing Values

Values could be missing in case they haven’t been filled in, or because of system changes or discrepancies.

Options ■ Ignore the case. ■ Fill in the missing value manually. ■ Use a global constant to fill in the missing value,

e.g., “unknown” ■ Imputation: Use the attribute mean to fill in the missing

value ■ …



28-July-2016

Imperative 4

Focus on Employee Factors the Business Can Drive


28-July-2016

CHOOSING BETWEEN ACCURACY AND ACTIONABILITY


Accuracy Actionability

Model accuracy is any quantitative metric that explains how well the model predicts the outcome of interest.

How do we justify the value of predictive modeling if we are handicapping our models to make them interpretable?

For all the employees who have actually left, how many did the model predict correctly?

If personality is found to predict attrition, it is hard to act on since it is not easily changeable.

How do we build confidence in our methods if those methods are not well understood by our stakeholders and do not enable action?

Actionability is a subjective model assessment. It helps HR and business leaders to understand the model and evaluate its utility.

Definition

Question for Talent Analytics

Teams

Example

Analytics teams have to make the right trade-offs.


28-July-2016

FOCUS ON EMPLOYEE FACTORS THE BUSINESS CAN DRIVE

Understand what the business hopes to learn (e.g., attrition risk of individual employees or which changes will lower attrition risks for all employees).

Investigate which aspects of employee experience can be influenced or changed and involve business/HR to help identify the predictors to choose.

Understand the time dimensions of different drivers and include short-term (e.g., improving learning effectiveness) and long-term (e.g., hiring) drivers in the model.

Assemble a list of all possible predictors with the help of HR and business leaders by working backwards from potential interventions.

Understand the hypotheses behind these predictors in explaining the outcomes.

Engage with business and HR teams to shortlist and select the predictors they would like to see in the model based on actionability.

Understand the time dimensions of different drivers and include short-term (e.g., improving learning effectiveness) and long-term (e.g., hiring) drivers in the model.

Talent Analytics LeadersExplain the tradeoff between accuracy and actionability to business leaders and guide

their choice.

Talent Analytics StaffBuild your HR acumen to create actionable

hypotheses and predictors.

TO DOs


28-July-2016

Imperative 5

Translate Results into Insights and Action Steps


28-July-2016

INSIGHTS WITHOUT RECOMMENDATIONS REMAIN UNUSED

Why Do Talent Analytics Teams Struggle to Drive Action from Insights?

■ Unclear Responsibility: They think that their job is done when analysis is complete.

■ Limited HR Acumen: They are not familiar enough with the tactics and actions HR or managers can take to address identified attrition reasons.

■ Inability to Probe Deeper: They don’t understand the “why” behind their results (i.e., aren’t able to separate indicators from root causes).

■ Limited Business Understanding: They are uncomfortable with making strong recommendations to business leaders.

■ Lack of Skills: They do not have the influencing or communication skills to convince business leaders to act.


Just 1 in 10 Heads of Talent Analytics believes their

organization effectively uses talent data to inform business decisions.


28-July-2016

TRANSLATE RESULTS INTO INSIGHTS AND ACTION STEPS

Business leaders might ask for prediction but seek prescription. Build out strong recommendations and opinions based on your understanding of the analysis results.

Share findings early and continuously with stakeholders so they participate in the analysis and understand its results.

Participation by the business at the start of a project also validates the choice of predictors and drivers, making it easier to draw insights in the end.

Analytics is about behavior change. Look for opportunities to show business leaders what they should be doing differently, not confirm what they know.

Build informal relationships with other HR staff to learn about their work and improve understanding of what levers can be pulled for different effects.

Shadow the employees in the segment you are analyzing to know their day-to-day work.

Conduct business brown bags to bring data scientists and line leaders in the same room.

Talent Analytics LeadersInclude leaders throughout all project phases,

from scoping to action planning.

Talent Analytics StaffBuild relationships with HR staff, line leaders, and employees to build your understanding

of which levers can be pulled.

TO DOs


28-July-2016

BUILD OUT STRONG RECOMMENDATIONS TO DRIVE BEHAVIOR CHANGESample Action Planning

Primary Causes of Attrition Identified from Analysis (Example) Proposed High-Impact Strategies (Examples)

Declining Collaboration

Poor Manager Quality

Build employee awareness ■ Organize workshops to improve self-awareness of individuals’ impact on their colleagues’ work and wellbeing

Reward collaboration ■ Adjust performance reviews to evaluate employees’ impact on their peers’ work

■ Encourage managers to act as role models by evaluating their contributions at the enterprise, not just the individual, level

Improve manager capabilities ■ Teach managers how to coach in the moment by balancing positive with development feedback

Involve skip-level managers in coaching ■ Assign career responsibility to the manager-once-removed

Re-examine the competitiveness of the rewards package ■ Consult with your compensation specialist

Look for reward alternatives ■ Learn what your employees prioritize that you are currently not offering

Below-Market Compensation



28-July-2016

ROADMAP

Five Imperatives

for Developing Attrition

Prediction Models



HCL’s Hypotheses-


Model


HCL


28-July-2016

CATEGORIES OF PREDICTORS COMMON ACROSS ATTRITION MODELSCategories of Predictors That Are Similar Across Attrition Prediction Models

1. Tenure and Experience 2. Performance Stability 3. Peer Concentration

4. Employee Productivity 5. Organization Structure and Changes

6. Compensation and Pay Parity

7. Employee Level and Seniority 8. Manager Impact 9. Team Size


28-July-2016

CATEGORIES THAT SO FAR DIDN’T APPEAR IN ANY ATTRITION MODELCategories We Expected To Be Significant But Weren’t Across Three Models

15. Growth and Future Career Opportunity

16. Work Location and Proximity

1. Tenure and Experience 2. Performance Stability 3. Peer Concentration

4. Employee Productivity 5. Organization Structure and Changes


7. Employee Level and Seniority 8. Manager Impact 9. Team Size

10. Leadership Impact 11. Employee Transfers and Movements 12. Job Characteristics

13. Work Life Balance 14. Learning and Development


28-July-2016

SPECIFIC PREDICTORS WITHIN CATEGORIES ARE DISTINCT FOR EACH ORGANIZATIONVariance in Specific Predictors Underneath Broad Categories (Examples)

Categories Specific Predictors

Company A Company B Company C

1. Tenure and Experience Educational Qualifications Time in Current Role Industry Specific

Experience

2. Performance Stability Low Performers Inconsistent Performance

Most Recent Rating Decline

3. Peer Concentration Smaller Teams (3–4 members)

Larger Teams (8–10) Members

4. Employee Productivity Overworked Employees Billed Hours Less

Than Average

5. Organization Structure and Changes Program Change No Department Change

in Last 3 years


Compensation More Than Peers

Compensation Range 50–60k

7. Employee Level and Seniority

Same or Consistent Seniority/Level Mid-Level Employees Entry-Level Employees

8. Manager Impact Below Average Manager Effectiveness

Older Generation Managers

Externally Hired Managers



28-July-2016

UNDERSTAND THAT ONE MODEL DOESN’T FIT ALL

Set project time frames that allow for creating segment-specific models to make targeted recommendations with more credibility and actionability.

Establish which challenge or which segment needs to be addressed first.

Build inventories of hypotheses that can be largely used across multiple segments.

Understand if there is a universal set of predictors that can be used to predict attrition outcome across a variety of talent segments (STEM talent, Entry Level staff, Front-line staff, Sales staff).

Standardize measurement of predictors across analytics projects done in-house (e.g., the definition of manager concentration should remain consistent)

Talent Analytics LeadersExplore hypotheses for the drivers of attrition

for different populations.

Talent Analytics StaffCreate lists of hypotheses and predictors

to draw on for different attrition prediction projects.

TO DOs


28-July-2016

Categories Predictor Definition Suggestions

Organization Structure

Management Hierarchy Number of levels within a team

Team Distribution Sum of miles/kilometers between individuals on a team

Leadership Presence Concentration of leaders in business unit: number of leaders by number of employees in a BU

Manager Impact

Span of control Management ratio: number of direct reports per supervisor

Manager Concentration Number of managers in a BU per number of employees in a BU (BU can be replaced with team, program, division, etc.)

Manager Tenure Number of continuous days the manager has worked for the organization

Position in Organization Hierarchy

■ Level in the organization ■ Manager level by employee level

Manager Performance ■ Performance rating of an employee’s manager ■ Manager performance change (YoY) ■ Manager advancement rate (Number of promotions/Tenure) ■ Manager highest education qualification—employee highest education qualification

■ Manager effectiveness score ■ Manager quality score

Manager Attrition Yes, if manager has left the organization (manager has an employment end date before employee date of departure), No otherwise.

Manager Generation ■ Manager age ratio: manager age by employee age ■ Manager generation gap: generation difference between manager and employees

STANDARDIZE MEASUREMENT TO USE PREDICTORS ACROSS MULTIPLE PROJECTSDefining Specific Predictors (Examples)


28-July-2016

ROADMAP

Five Imperatives


Prediction Models



HCL’s Hypotheses-


Model


HCL


28-July-2016

HCL’S HYPOTHESES-BASED APPROACH TO BUILDING AN ATTRITION PREDICTION MODELOVERVIEW

Attrition rates have been rising in HCL over the years in line with the rest of India’s IT services industry. To address the risk of increasing workforce churn, HCL’s head of HR tasked the talent analytics team to build an attrition prediction model that would enable line managers to address reasons for employee dissatisfaction and attrition. The team took a hypotheses-led approach to discover significant indicators to predict attrition intentions and in turn, to develop recommendations for engaging individual employees.

SOLUTION HIGHLIGHTS

■ Explore Non-Survey-Based Sentiment Indicators: Having realized that employees may misrepresent or misunderstand their reasons for leaving the organization, the analytics team questioned the conventional use of exit survey data and created behavior-led hypotheses to explore non-survey-based indicators.

■ Talk to the Business to Create Segment-Specific Hypotheses: The talent analytics team spoke to local business leaders and their HRBPs and talent management leads to inform additional hypotheses on which factors drive attrition.

■ Create Targeted Recommendations for HRBPs and Managers: A dashboard names employees most at risk of attrition and recommendations to retain them.

COMPANY OVERVIEW

HCL Technologies Limited

Industry: IT Services HCL Technologies is a global IT services company working with clients to impact and redefine the core of their businesses. HCL operates out of 32 countries offering a portfolio of services including Internet of Things Consulting, engineering services, outsourcing and integrated infrastructure services, applications services, and business services.

2016 Sales: US$6.6 Billion

Employees: More Than 109,000

Headquarters: Noida, India


28-July-2016

ATTRITION ON THE RISEAttrition at HCL (Estimate)

Source: HCL Technologies Limited; CEB analysis.

10%

15%

18%

Benefits of an Effective Attrition Prediction Model ■ Identify specific employees at risk of leaving. ■ Suggest targeted actions to managers. ■ Inform workforce planning decisions.

Need for talent analytics team to build actionable guidance to address attrition risk

0%

10%

20%

2007 2010 2013 2016 Forecast


28-July-2016

How do other organizations build attrition prediction models? Challenges Solutions

Most organizations use

exit survey data.

Employees don’t necessarily

know why they leave or they

might not tell us.

Explore non-survey-based sentiment indicators through behavior-led hypotheses.

A specialized team

concentrates on

building a beautiful

model.

Limited interactions with

corporate or business

leaders lead to models that

are not customized to the

business.

Talk to the business to create segment-specific hypotheses.

Predictions are made

at the population

level rather than the

individual level.

Without recommendations

that an individual

manager or HRBP can act

on, models fail to drive

actions.

Create targeted recommendations for HRBPs and managers.

Talent Analytics Team

EVOLVE PREDICTIVE MODEL BY TESTING AND REFINING HYPOTHESESProject Kickoff Exercise (Illustrative)

HCL takes a hypotheses-

based approach to building its

model.



28-July-2016

EXPLORE NON-SURVEY-BASED SENTIMENT INDICATORSProcess for Determining Hypotheses to Find Indicators of Employee Intentions

“Behave Like an Employee” Exercise

Participants Talent analytics team, data technology analyst

Purpose ■ Create hypotheses based on dissatisfied employees’ behaviors.

■ Trace the breadcrumbs employees leave through technology use to discover potential sentiment indicators.

Questions ■ If I was dissatisfied, how might my communication and

participation patterns reflect that? ■ If I was unhappy with my manager, how might my

actions change? ■ If I don’t see a career path for myself, what might I do? ■ If I begin looking for jobs, what will I do to get ready? ■ What would I do before handing in my resignation?

Exit SurveyTop Reasons for Leaving

1. Future career opportunity

2. Compensation3. Development

opportunities4. …

Post-Departure Responses,

Top Reasons for Leavinga

1. Problems with direct manager

2. Compensation3. Changes in team4. …

1. Question What Employees Say in Exit Surveys 2. Discover Attrition Indicators Through What Employees Do, Not Say

Source: HCL Technologies Limited; CEB analysis. a HCL engaged a vendor who used behavioral psychologists to interview 1,500 employees who had left HCL six months earlier. They used text analytics to

compare exit survey with post-departure, in-person responses.

Results of One-Off Vendor Engagement to Compare Exit Survey with Post-Departure Comments


28-July-2016

Data Technology Expert and Talent

Analytics Team

EXPLORE NON-SURVEY-BASED SENTIMENT INDICATORS (CONTINUED)Mechanics for Investigating Existing Platforms and Systems for Employee Sentiment Indicators


1. Identify a Data Technology Expert

Ask your IT business relationship manager for a contact with:

■ Knowledge of technology platforms in place or scheduled to be invested in, and

■ Understanding of system configurations (i.e., version, customization, compatibility) to ensure data quality.

2. Compile a List of Platforms and Systems That Could Be Used to Track Employee Sentiments

■ HRIS and LMS ■ Performance management system

■ Peer-to-peer feedback system

■ IT or housekeeping service requests

■ Internal social network

■ Innovations platform

■ E-Mail ■ Company drives ■ CRM ■ …

Indicators Discovered ■ Number of service desk requests raised, satisfaction with response

■ Ideas generated on innovations platform ■ Employee studies but doesn’t activate exit process ■ Employee checks remaining leave ■ Employee downloads salary slip

Hypotheses Created and Tested


28-July-2016

TALK TO THE BUSINESS TO CREATE SEGMENT-SPECIFIC HYPOTHESESLearn from Business Leaders and HRBPs What Drives Attrition in Their Market

Questions for Discussion ■ Has anything happened in your city recently that has or could affect your operations (e.g., general attractiveness, competitor relocation)?

________________________________________________________________________________________

■ What are the skills that are most in demand?________________________________________________________________________________________

■ How easy is it to attract these in-demand skills (e.g., new companies entering the market, compensation hikes)?

________________________________________________________________________________________

Implementation TipIdentify business leaders to speak with by looking at:

■ Leaders who have halted or reversed attrition rates, and ■ Leaders of teams that had lower-than-average accuracy of the model’s first iteration.


Business Leader/HRBP

Indicators Discovered ■ Hiring intensity in city or state (measured through exits on team)

■ In-demand skills ■ Salary gap to market

Strong Competitive Position

Weak Competitive Position

Low Talent Supply

High Talent Supply

Hypotheses Created and Tested

Conversation Focus


28-July-2016

ATTRITION PREDICTION MODEL VARIABLES

Source: HCL Technologies Limited; CEB analysis. a HCL measures the accuracy of its model based on whether employees flagged as high risk left within six months.

■ Employee demographics (e.g., age, gender, tenure, business unit, distance from home to work)

■ Work environment (e.g., move to manager with a lower 360-degree feedback score, number of team members in the same location)

■ Work structure/engagement (e.g., quality of project, engagement score)

■ Performance (e.g., performance rating)

Iteration 1 Variables Iteration 2 Variables ■ Employee demographics ■ Work environment ■ Work structure/engagement (e.g., type of role, exits in team)

■ Performance (e.g., drop in performance rating by one or two points)

■ External variables (e.g., salary gap to market, attrition in specific skill set, demand for skill at city or region level)

Iteration 3 Variables ■ Employee demographics ■ Work environment ■ Work structure/engagement (e.g., tech trigger: study but don’t activate exit process, check remaining leave, download salary slip)

■ Performance ■ External variables

■ Question conventional wisdom around using exit survey data.

■ Explore data based on hypotheses about employee behaviors.

■ Create and test hypotheses based on business and HRBP input.

■ Revisit data discovery post testing.

Jan 2015 May 2015 Dec 2015

Mod

el A

ccur

acya

100%

50%

0%

Iteration 145%

Iteration 276%

Iteration 390%

Actions to Improve Accuracy


28-July-2016

CREATE TARGETED RECOMMENDATIONS FOR HRBPs AND MANAGERSAccount-Level Reports and Action Recommendations for Groups of Employees

Account: ABC BankDespite lower voluntary attrition in comparison to other accounts, ABC contributes more to the exits due to sheer size of the account.

Attrition for ABC Bank closed at 19.67% with the third quarter at 23.17%. Q4 is projected at 21.16%.

An additional 150–160 high-risk employees need to be proactively engaged by the team.

Lead Indicators Increasing Attrition

■ Ninety-five percent of the employees ABC account is losing are highly engaged (almost half of these with high scores for two years).

■ Almost 40% of exits in ABC account are from employees with “above market” salaries in job band E0, which points to probable attrition causes to be other than salary (forced rating grievances, career growth, and/or learning opportunities).

Action Recommendations

■ Managers should suggest mentors to direct reports via Career Connect. ■ For the top three performers, discuss rotation to a different account. ■ …

June ‘16 Q3 ‘16 Q4 ‘16

ABC Bank 19.67% 23.17% 21.16%

Actual Pipeline — — 118

Projected Pipeline (Based on

Prediction Model)— — 32

Expected Pipeline — — 150


Create action recommendations for groups of employees with similar experiences. For HCL, this group is employees working on one account.

Highlight trends and drivers of attrition to create pressure to act.

Recommend actions to direct managers and HRBPs to help them target employees’ pain points effectively.


28-July-2016

CREATE TARGETED RECOMMENDATIONS FOR HRBPs AND MANAGERS (CONTINUED)Attrition Prediction Dashboard for HRBPs and Managers to Drive Individual Actions

Dashboard

Risk Period: 90 Days, Risk Category: Red, Location: Bangalore

Copy Excel PDF Print Column

Employee ID

BAND DROP IN MANAGER RATING

ATTRITION WITHIN TEAM

SALARY GAP TO MKT

RISK LEVEL 90 DAYS

1212549 E0

1352876 E0

1752176 E0

1555296 E0

1153277 E0

1852890 E0

1052326 E0

1359061 E0

Priority_2

Priority_1

Priority_2

Priority_2

Priority_2

Priority_2

Priority_1

Priority_2

15%–25%

9%–15%

0%–9%

0%–9%

15%–25%

More Than 25%

9%–15%

0%–9%

Medium

High

Medium

Low

Medium

Medium

Medium

High

-1

-3

-2

-2

-1

-2

-3

-1

Back

Showing 1 to 8 of 44 entries Previous 1 2 3 4 5 6 Next

Show the variables that are most predictive of attrition: For different locations or job bands, these three top variables will be different.

Highlight employees at highest risk of leaving: Managers and HRBPs can then decide who to focus retention efforts on based on additional considerations, such as skill criticality.



28-July-2016

SUCCESSFUL MODEL DRIVES RETENTION ACTIONS WHICH STALL ATTRITION RATESAccuracy#a of Attrition Prediction Model Stall in Attrition Rates Since Introduction

of Attrition Prediction Model

Source: HCL Technologies Limited; CEB analysis. a HCL measures the accuracy of its model based on whether employees flagged as high risk left within six months.

0%

50%

100%

20142014 20162016

India IT Services Industry

HCL

90%

18%

16% 16%

20%76%

45%


28-July-2016

ROADMAP

Five Imperatives


Prediction Models



HCL’s Hypotheses-


Model


HCL


28-July-2016

What questions or comments do you have?


28-July-2016

VISIT OUR TALENT ANALYTICS PORTAL

Analytics Insights and Trends

Live and Virtual Events

Tools and Project Support



28-July-2016

TAKE OUR TALENT ANALYTICS EFFECTIVENESS SURVEYMaximize the Impact from Talent Analytics Investments

Our 2017 Talent Analytics Effectiveness Survey will help you identify the way forward across the biggest talent analytics decisions you’ll have to make this year, such as on staffing, team structure, and technology investments.

Participates will receive: ■ A benchmark report with insights on how to maximize the impact of your talent analytics on business decisions and performance,

■ A personalized walkthrough of results with our research team, explaining the most impactful investments for your organization based on current capabilities and objects, and

■ Ongoing support from our advisory team on how to apply these insights to your talent analytics work in 2017.

Take the Survey


People Data Structure Technology Processed Relationship

Building blocks of an Organization’s talent analytics capability

https://www.cebglobal.com/member/corporate-leadership-council/benchmarks/survey/2017-talent-analytics-effectiveness-survey.html


28-July-2016

SUGGESTED NEXT STEPS FOR YOU


Send us feedback on our current talent analytics resources and let us know what you would like us to create for you. If you have exciting work you would like to share with the membership, let us know that too!

3

Share your metrics and models with us. We are currently building an inventory of the metrics and models that organizations are using to solve their most pressing strategic questions.

4

Bookmark the Talent Analytics Portal and return at any time.2

E-mail us to subscribe to the Talent Analytics E-Briefing Monthly Newsletter and Talent Analytics Quarterly.1

mailto:talentanalyticshelp%40cebglobal.com?subject=

https://www.cebglobal.com/member/corporate-leadership-council/benchmarks/metric/call-for-talent-analytics-metrics-and-models.html

https://www.cebglobal.com/member/corporate-leadership-council/resources-by-role/talent-analytics-portal.html

mailto:talentanalyticshelp%40cebglobal.com?subject=

GauravspecializesinBusinessandHRconsul4ngleveragingcompe4torintelligenceandanaly4cs.HehasprovideddecisionsupporttoCHRO/CEOofficewithbenchmarkingandanaly4cs.Hebringsextensive

experienceincombiningexternalmarketdatawithorganisa4onsinternaldatabuildingpowerfulrepor4ng,predic4veandprescrip4veanaly4cscapabili4esusingR,Tableau,WatsonTalentInsightsandQlik.

Hehasdevelopedpredic4vemodelsinHR/Opera4onssuchasDemandforecas4ng,ALri4onPredic4on,Opera4ngCostOp4miser

andMarginPredic4on.