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India's First and most comprehensive report on Attrition

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Page 1: Attrition Report
Page 2: Attrition Report

Global Talent Metrics Pvt. Ltd Center for Talent Research Indian Institute of Management, Bangalore

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India’s First and Comprehensive Research on Talent Attrition

INTRODUCTION

Employee attrition and candidate absconding are significant business concerns for organizations, one even bigger than attracting talent. A survey of 33,000 employers from 23 countries found that 40% of them had difficulty finding and hiring the desired talent (Manpower Inc, 2006) and approximately 90% of nearly 7,000 managers indicated talent acquisition and retention were becoming more difficult (Axelrod, Handfield-Jones and Welsh, 2001).

Employee attrition is a serious issue, especially in today’s knowledge-driven marketplace where employees are the most important human capital assets; attrition impacts an organization’s competitive advantage.

Annual attrition rates in the IT/ITes industry are estimated to be in the range 20 to 80 percent, 15% for high-skilled IT services and on average 40% in the BPO industry.

The tangible costs of employee attrition would be the cost of

training new employees, the recruitment and selection costs,

adjustment time, possible product and/or service quality

problems, costs of agency workers/ temporary staff (Morrell,

Loan-Clarke, and Wilkinson, 2004a), the cost of training, the

cost of loss productivity, the cost of lost knowledge and the cost

of the position remaining vacant till a suitable replacement is

found. (Sharma 2007). The intangible costs, which may be

even more significant than the tangibles, involve the effect of

turnover on organizational culture, employee morale, social

capital or organizational memory (Morrell, Loan-Clarke, and Wilkinson 2004a). All

these costs would significantly take away the profitability and the competitive advantage

of the firm.

Development, 

Design and 

Implementation 

of an assessment 

model that can 

predict EARLY 

attrition (0‐6 

months) risk 

candidates would 

results in 

significant 

shareholder value 

by increasing 

managerial 

effectiveness, HR 

organizational 

productivity and 

better manpower 

planning 

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Global Talent Metrics Pvt. Ltd Center for Talent Research Indian Institute of Management, Bangalore

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The organizational costs associated with turnover in terms of hiring, training, and productivity loss costs can add up to more than 5% of an organization’s operating costs according to a study by Waldman, Kelly, Aurora & Smith, 2004).

It is imperative for organizations to understand what attracts and retains a potential candidate and predict attrition early in the recruitment process to curtail significant loss of productivity among hiring managers, recruiters and eventual loss of revenue and moneys.

The STAR™ (System for Talent Attraction and Retention) research was aimed to design such a predictive model.

OBJECTIVE OF THE REPORT

This report is India’s first and most comprehensive research on

a) Key attributes (brand, salary. Job security etc.) that are important to candidates in a potential employer.

b) Job candidate’s preferences/needs from their employer or company

c) Understanding if the needs are uni- dimensional or multi-dimensional and

if

d) multidimensional to develop practical and meaningful clusters/segments based on preferences

e) Reasons for Early Attrition (Leave within the first 9 months). It focuses on the psychographics, demographics and sociological factors impacting a candidate/employee decision to stay or leave.

THE RESEARCH 

IDENTIFIED 3 

INDIVIDUAL 

DIMENSIONS AND 8 

ORGANIZATIONAL 

FACTORS BASED ON 

66 ITEMS AND 

UTILIZES CROSS 

DISCIPLINARY 

APPROACH TO 

MODEL THEIR 

INFLUENCE  ON A 

PROSPECTIVE 

EMPLOYEE’S 

DECISION  TO  GET 

INTERESTED,  

SELECT, STAY  OR 

LEAVE A COMPANY.  f) Understanding if there are their simple variables like

gender, age, number of previous jobs that can be a good predictor of attrition risk

If Not

Develop a match making model that can assist predict high attrition risk candidates at the time of sourcing and selection.

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METHODOLOGY AND DATA

Preliminary theoretical research was conducted prior to the empirical investigation. A theoretical model, called the STAR Select Model, see appendix, was developed based on a literature review of research papers and projects conducted globally in the area of attrition, brand selection and predictive modelling.

The empirical research was conducted in three phases over a period of 18 months:

Phase One (Interviews and Focus Groups) - The empirical data for the first phase of STAR research was collected through qualitative research from eight organizations in India. The STAR team gathered perceptions of both job incumbents and senior executives/managers on reasons for employee turnover through a series of in-depth, one-on-one interviews and focus groups. The team met with 626 CXOs, GMs, Vice-Presidents, HR executives, heads of operations, line managers and job incumbents over a period of six weeks in 2007.

Phase Two Surveys – Based on the outcomes of the interviews and focus groups a survey was designed and was rolled out as a pilot study; the web based questionnaire was administered to job incumbents across the country and generated 971 responses. Factor and Cluster Analysis was conducted to develop a final version of the survey called STAR™ Personal Consumer survey. This survey was administered to job incumbents across the country and 5067 responses were received. Respondents represented a wide cross-section of the population across with various age ranges, work experience and job titles, companies of various size in India. Few notable examples are: Accenture, Cognizant, Wipro, Infosys, Cisco Systems, LogicaCMG, Dell, WNS, KLA Tencor, Kotak Bank, ABN AMRO, Deutsche Bank, ICICI Bank, Price Waterhouse Coopers, Deloitte, Goldman Sachs, Thomson Financials, Airtel, Motorola and Nokia Siemens

Statistical analyses including Descriptive statistics, ANOVA and Independent T-test, were performed to determine if observed factors causing attrition were statistically significant. Analyses by employees’ gender, age, marital status, parental status, place of residence, place of origin, number of previous employers, duration of employment with previous employers were conducted, and significant findings are noted in this report where appropriate.

Phase Three– The research findings helped develop a system and a predictive model that can predict attrition risk early in the sourcing and selection process. This model

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incorporates needs and preferences of candidates as well as a ‘company culture profile’

of the organization to be matched with the candidate profile.

STAR™ INDIVIDUAL FACTORS:

Category Dimension Findings/Items

Psychological Personality

Attitudes

Values

Beliefs

- Need for recognition

- Need for coaching and support on technical aspects

- Need for advice or support on career decisions

- Security/stability versus risk taking

Demographical Age

Marital status

Parental status

Geography

Family

(nuclear/joint)

- college studies/degree

- size of the company (previous employment)

- place of study

- place of first employment

- place where parents stay

- sibling status (first born, second born)

- Travel distance work/home

Sociological Peers

Community

- Brand of the organization as perceived by family and friends

- Peers perception of area of work, salary, technology and

organization

STAR ™ ORGANIZATIONAL FACTORS:

JOB CONTENT - Clarity, expectations, technologies/products, learning opportunities, challenging, interesting work

CAREER GROWTH - Promotions, training, overseas postings

COMPANY POLICIES e.g., salary scales, bond of retention, performance rating and rewards, personal e-mail access at work, employee benefits.

PLACEMENT AND TRANSFER - time waiting for assignment/job task, mismatch between training and assignments, posting in secondary towns or other cities or branches, night shift, task allocation, transfers to other teams CULTURE e.g., quality of work life, freedom, monitoring, helpful managers, communication, feedback, peer (colleagues) support.

TOP MANAGEMENT e.g., visibility of top management, recognition and appreciation from top management.

MANAGERIAL SUPPORT e.g., concern for people preferences, cooperation, mentoring, appreciation, career guidance.

BRAND IMAGE OF THE COMPANY e.g., the reputation of the company; how well known and social prestige associated with the company and accepted it is.

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KEY FINDINGS

Summary of the findings:

1) ATTRITION IS NOT A PERSONALITY OR BEHAVIORAL TRAIT OR DEMOGRAPHICALLY ORDAINED.

• At an aggregate level there is insignificant difference between respondents that have had one employer in five years vs. two or more employers in two years in what they value (motivators) when deciding to join, stay or leave an employer.

2) DIFFERENT STROKES FOR DIFFERENT FOLKS

• Candidates and employees have MULTIDIMENSIONAL sociological and psychographic needs and can be clustered into distinct segments/clusters (discussed later in the report). These segments can be effectively used to match their needs with employers ability to meet them to reduce attrition. These clusters are not distinguishable by a single demographic characteristic like age, gender, marital status and region.

• 3) DEMOGRAPHICS ARE POOR PREDICTORS

OF PREFERENCES. • Statistically there is no significant difference

in what people value when selecting a company based on their age, gender, region.

To improve selection and reduce attrition employers first need to understand where they stand competitively in the marketplace in terms of providing these motivators and hire individuals from the STAR Clusters that best match to their offerings.

The risk of Attrition is interplay of these multi-dimensional needs with and employer’s competitive position to satisfy or meet them.

STAR CLUSTERS 2/3rd of the Indian White Collar workforce can be categorized into 6 distinct employee preference clusters based on of combination of factors (they value) or motivators when selecting, staying with or leaving a company.

4) WORK FORCE IS LESS MERCENARY THAN

EMPLOYERS BELIEVE THEY ARE. • Only 9% of the respondents rate salary as the

only motivator/factor selecting or deciding to stay or leave an employer.

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5) MORE PEOPLE ARE CAREER DRIVEN THAN GREEDY • 67% of the people rate career advancement issues more important than

salary related factors when selecting or deciding to stay or leave an employer.

6) MOST PEOPLE PRIORITIZE CAREER RELATED ISSUES OVER

FAMILY ORIENTED ISSUES • While people often use family oriented reasons as a “convenient”

explanation for their departure, only 15% rate family oriented issues as important reason to select, stay or leave a company.

7) REASONS WHY PEOPLE SELECT OR LEAVE A COMPANY ARE

SIMILAR BUT DIFFERENT FROM WHY THEY STAY IN A COMPANY. • Most common factors to select or leave a company are career growth and

pay related while most common motivator to stay are relationships and recognition oriented.

THE STAR CLUSTERS/ SEGMENTS:

STAR(tm) PREFERENCE  CLUSTERS ( %age of respondents)

Demanding (16 %) Work not Pay (1 1%) Start Up  (12%) Just a Job (13%) Mercenaries (9%) Cash & Brand (5%)

Pay and Benefits Company Brand/Image Career Growth/Learning Relationship / Recognition from Supervisor

HIGH

LOW

Demanders – These candidates are demanding and have very high expectation from their employers. They want the best pay, at a top brand company which can offer them high growth career path; they need constant recognition from their supervisors.

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Work not Pay – These candidates want high carreer growth, challenging work, at a prestigious company with good management. This compnay does not necessarily pay the best in the market – e.g Tatas, Brilas.

Start Ups – These candidates look for exciting work, learning, fast growth and value relationships. They do not value pay and brand and thus are ideal for smaller high growth companies of high growth divisions ( corporate start-ups) of larger companies that can provide challenging work and growth.

Mercenaries - 9% of the Indian white collar work force is purely motivated by pay. These candidates want the highest pay irrespective of what work they do and where they do it. These employees will be unhappy if they feel their employer is not the best pay master. In certain ways it is easy to satisfy them as they are diven by one factor: pay.

Cash and Brand – These candidates are like mercenaries, except they are conscious about the brand name of the company. They work for social gratification and seek the best pay but only at companies with good brand images.

Just a Job - These candidates are opposite of Demanders. They are just looking for a job that pays OK and have relatively low expectation across other factors

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OTHER FINDINGS

Pay, contrary to popular notion is not the top reason for attrition. Lack of Career Growth opportunities and recognition from supervisors are more compelling drivers for attrition.

50% or more people rate the following four reasons important or very important factors in selecting or leaving a company

• Clarity of Career Path • Relationship with and recognition from supervisor • Career Growth Opportunities • Pay

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1. 70% of the respondents do not consider family oriented events as important factors to leave a company, although employees conveniently use family oriented events like – marriage, child birth, need to stay closer to family – as explanations for departure

2. Relationships with peers are one of the top three reasons why employees stay with a company.

3. People who have held stable jobs vs. who have moved across employers are not very different in terms of tracking the importance of factors to select or stay or leave an employer. Both category of respondents have similar scores and identical ranking of the STAR™ factors:

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STAR ORGANIZATION PROFILE

Just as people are different and multidimensional in what they value when SELECTING, STAYING WITH OR LEAVING A COMPANY, employers/companies are diverse in terms of what they offer on the key STAR™ factors in comparison to their talent competitors.

The following is an example of a STAR ORGANIZATION PROFILE of two companies

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EMPLOYEE ATTRITION REDUCTION RECOMMENDATIONS:

Employers need to distinguish between efforts they out to reduce attrition vs. they put in place to increase engagement. Employers need to understand where they stand competitively in the marketplace in terms of the STAR factors.

The seeds of attrition are sowed at the time of sourcing and selection. Employers need to provide tools to recruiters not to just skills but tools to match candidate’s STAR Needs with the company’s strengths. The match process starts….

Employees need to provide tools to managers to guide them what drive a new employee so that new joiners do not become disappointed and leave within the first six months. Employers need to understand what they are good at offering to prospective employees.

Banks should focus on hiring more people from Cluster Work not Pay and Start-up segment as the bank is not the best pay master but provides string brand, carrier growth and supervisor relationships

STARSELECT™ (SYSTEM FOR TALENT ATTRACTION AND RETENTION)

STARSELECT is a psychometric tool that helps organizations to isolate high attrition risk candidates early in the selection process and also helps in managing attrition risks in their existing talent pool by identifying high risk candidates.

The main objective is to eliminate cost associated with early attrition and to monitor talent engagement levels within the company to develop a more robust talent supply chain management system.

This attitudinal and behavior measurement tool designed for global organizations and specifically adapted for Indian conditions, has been utilized by over 500,000 job applicants and 20,000 managers.

Objectives of STARSELECT

1. Understand the organizational, job and personal factors that contribute to an individual’s decision to “stay” or “leave” the organization.

2. Understand the complex demographic, psychographics, behavioral, vocational and other variables that contribute to

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a. An individual’s propensity to stay or leave and b. An individual’s actual decision to stay or leave.

3. Develop a reliable model and validate a comprehensive tool that will help organizations based in India better predict, manage and control turnover.

STARSELECT Model

 

 

STAR™ Modeller 

STAR™ Retention Predictive Risk Score

STAR™ Retention Risk Band

STAR™ Profile

FEATURES & BENEFITS OF STARSELECT™

STARSELECT ™ increases recruiter efficiency and enhance managerial hiring

productivity.

Reduce early attrition by 20-70%

STARSELECT™ is the first and only validated psychometric and behavioral test

bank developed for India and based on pure India data set for better results

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STARSELECT ™ is based on models that have been tested and proven over the

last 30 years and current used by leading Fortune 50 companies in selection and

hiring.

STARSELECT is proven and validated by leading research institutions.

STARSELECT™ is flexible in that the organization’s Cultural Retention Index

(CRI) will have an impact on the profiles of applicants who will fit your particular

organization.

STARSELECT™ is designed for high volume screening.

STARSELECT™ is easy to use; it can be plugged into your career portal for

reliable rapid pres screening or it is easy to use so that your talent acquisition

team can learn the system quickly and there is no need for psychologists or

outside experts.

STARSELECT™ will help you develop deep understanding of issues impacting

talent engagement for YOUR organization.

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Bibliography

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Budhwar, P., Varma, A., Singh, V. and Dhar, R. (2006b), “HRM systems of Indian call centres: an

exploratory study”, The International Journal of Human Resource Management, Vol. 17

No. 5, pp. 881-97.

CIPD (2007a). Employer branding, the latest fad or the future for HRM? Chartered Institute of Personnel and Development, reference 4185, July.

CIPD (2007b). Employer branding, a no-nonsense approach. Chartered Institute of Personnel and Development, reference 4093, December.

Ghiselli, E. E. 1974. Some perspectives for industrial psychology. American Psychologist, February: 80–87.

Gilliot, D., Overlaet, B. & Verdin, p. (2002). Managing academic personnel flow at universities. Tertiary Education and Management, 8(4), 277-295 Lockwood, D. and Ansari, A. (1999). Recruiting and retaining scarce information technology talent: a focus group study. Industrial Management & Data Systems, vol. 99 (6), pp.251-256

Manpower Inc. (2006). Confronting the coming talent crunch: What’s next? Retrieved April 19, 2006, from http://www.manpower.com/mpcom/files?name=Results_TalentShortage_2006pdf

Mitchell, T.R., Holtom, B.C., Lee, T.W., Sablanski, C.J., and Erez, M. (2001). Why People Stay: Using Job Embeddedness to Predict Voluntary Turnover, Academy of

Management Journal, Vol. 44, No. 6. pp. 1102-1122

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