a study on attrition analysis

1.1 INTRODUCTION:

Attrition

A reduction in the number of employees due to retirement, resignation or death is

called attrition. In the best of worlds, employees would love their jobs, like their

coworkers, work hard for their employers, get paid well for their work, have ample

chances for advancement, and flexible schedules so they could attend to personal or

family needs when necessary and never leave.

Reasons for attrition:

People don't get integrated. Most organizations have an orientation program which is

more of data-dump or focused on compliance trainings being completed. The focus

should be more on enabling employees to form networks within themselves.

Performance goals are unclear. In a fast growing team or business the focus is on

getting the thing done today, but rarely are performance goals thought through and

employees told as to which resources to approach for help.

Development is always tomorrow's job. Culturally Indians are focused on learning. If

learning adds value only to the job and not to the overall career goals of the individual

then the organizations seems too transactional for the employee

The personal touch is missing. How comfortable are managers building personal

bonds with their subordinates? A lot of managers shy away fearing a bond will make

delivering hard messages difficult. I would argue that it's the other way round!

Knowing employees on personal level makes a manager know their strengths and

weaknesses. Work allocation and employee development become easier.

Reward systems are not transparent. Most employees who get salary increases

because they have a rare skill at a particular point of time think they got their raise for

excellent performance.

Perceived equity of reward systems is low. Like it or not, employees discuss salary

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details and if there is any perceived lack of equity then you have an issue!

Goal setting process is not scientific. Most organizations impose a normal curve

fitment, but do not train managers to set realistic goals or goals that tie up with

organizational or functional goals. This also leads to point number 6

External equity is missing too. Don't do an annual compensation survey when the

market moves every 3-4 months. If your practitioners feel that externally comparable

professionals are being valued more, then they will leave.

No communication around total value. If you offer benefits apart from only

monetary terms do you communicate that to employees too? Things like being a

global or niche industry leader, value of the brand of the organization, should also be

made explicit.

No career planning. Are people aware of the ways in which they can grow in the

organization? Who are the role models within the organization? Do they know what

they have to do to gain the competencies to move to various levels?

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1.2 COMPANY PROFILE:

Serviont Global Solutions

Servion Global Solutions was started in the year 1995 and was established in 1998.

Servion Global Solutions and Cisco, the US-based networking company, will jointly

deliver to global companies Internet Protocol (IP)-based Unified Communication

solutions that combines IP telephony technology, contact management technology and

contact centre applications. As a pre-requisite to this relationship, Servion, a Chennai-

based company, has achieved accreditation as a Cisco Advanced Technology Partner,

which demonstrates a company’s expertise in Unified Contact Centre solutions,

according to a Servion press release.

At Servion, we believe that every time a customer gets in touch with an organization,

there is an enormous opportunity to convert that interaction into a long-term relationship.

Making the most of these interactions however, requires the right processes and systems

to be set in place. Organizations are now beginning to realize that their customers see the

experience they have when interacting with various touch points as being the key

determinant of brand perception.

This is where we can help you. Our decade of experience in the area of Customer

Interaction has helped us to devise a generic model called the Contact Optimization

Model. This Model helps map customer contact as well as makes suggestions for

optimizing and enhancing those interactions. In other words, we can help you put the

smile back on your customer's face, where it belongs!

Every time you think 'customer interaction', think Servion. With end-to-end Contact

Center solutions from Servion, interacting with your customers has never been easier or

more rewarding.

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Contact Optimization

Using our various Contact Optimization solutions, we can help you analyze your

customer interaction strategy, offer suggestions, and where required, a corrective course

of action. Our experience of more than 10 years in the Customer Interaction Management

industry has resulted in a Contact Optimization Model that we utilize to help

organizations optimize and enhance their customer response mechanism.

This approach has already helped leading organizations across the world obtain more out

of their existing systems and infrastructure, reduce operational costs, increase revenue

generation, and enhance customer satisfaction. We use best-of-breed technologies,

products, and services to develop solutions for effective deployment - all aimed at

bringing a smile to your customer's face, every time.

Our domain expertise encompasses a wide range of business sectors such as banking and

finance, insurance, outsourced Contact Centers, telecom, government, and transportation.

This is reflected in our blue chip customer base of more than 400 clients and 1000

installations worldwide. Our clients include industry majors such as ABN AMRO Bank,

Bharti Group, Citibank, DBS Bank, Etisalat, Emirates, GE, HDFC Bank, Hutch, ICICI

Bank, LG, Prudential Group, Singapore Telecom, Shinsei Bank, Standard Chartered

Bank, State Bank of Mauritius, Tata Teleservices, Toyota, and Wescom Credit Union.

Consistent performance and a steady growth rate have facilitated significant investments

in the company's equity from TDA Capital Partners.

Quality

At Servion, improving quality is a constant focus. This is a basic requirement for

satisfying and retaining customers and to build our reputation for referrals and new

customers. To meet this objective, we embarked on an intensive quality initiative and

were assessed at Level 4 of the Capability Maturity Model (CMM) - the benchmark for

quality and rigorous processes in the software industry.

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The CMM Level 4 assessment and our sustained quality practices give us the capability

to manage software development, implementation, and maintenance activities efficiently

and effectively. As our customer, you gain tangible benefits including higher reliability,

faster delivery, reduced cost of operations, and most of all, a competitive advantage.

Corporate Overview

At Servion, we believe that every time a customer gets in touch with an organization, the

opportunity to convert that interaction into a mutually satisfying and long-term

relationship is very high. And making the most of these interactions requires the right

processes and systems to be in place.

Servion joins hands with Cisco to offer Unified Communications Solutions

Servion Global Solutions Expands North American Operations

Servion Partners With Microsoft For Availability Of Its CIM Solution With

Microsoft Dynamics CRM

Servion Global Solutions Acquires 5by5 Networks Inc.

Avaya Global Connect partners with Servion in India

Servion invests in the Malaysian market

SER and Servion Forge Partnership to Establish Distribution Channel in India

Servion seven city seminar series - 'The Customer Interaction Conclave 2005' ends

at New Delhi

Servion Announces CallBack Manager with Natural Language Interface

Servion Unveils the Contact Optimization Model to Map Customer Contact

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Servion and HDFC Bank setting technology standards in the Customer Service arena

Servion named Best New Partner of Blue Pumpkin Worldwide

SerWizSol partners with Servion to implement Business Response Systems as key

differentiator

Servion partners with Blue Pumpkin for Workforce Optimization Solutions

TouchTel walks 'the' extra mile, achieves an all time high in customer experience

Servion partners with ScanSoft for Speech based solutions

Servion Global Solutions achieves Level 4 of Capability Maturity Model (CMM)

Servion ties up with Peoplesoft Implements at Vanenburg, Asia Pac's first Web

Servion and IBM to offer Solutions for the Contact Center Industry

Servion Receives 'Most Significant Win' award

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1.3 PRODUCT PROFILE:

1. Computer Telephony Integration (CTI)

Computer Telephony Integration (CTI) integrates the telecom system, the IT

Infrastructure, and software applications with customer data.

RAP CTI is an enterprise-level CTI engine that functions as the single point of contact

between customer access channels (PBX, IVR, Dialer) and corporate business

applications. Available in three different options - Instant, Prime, and Optima, RAP CTI

is designed for easy installation and deployment, enabling Contact Centers to become

CTI ready very quickly.

2. enTRAC

enTRAC, a multi-channel messaging platform, works on real time, and is completely

automated. Data is gathered from various sources and delivered as information via

multiple channels as either wired or wireless; voice, e-mail, fax, and SMS that

accommodates wireless text messaging, globally.

enTRAC allows users to specify the medium through which they wish to receive

notifications. This helps organizations offer maximum flexibility and convenience to

customers. enTRAC's unique two-way capability allows users to not just receive a

message but also respond, using the same device.

3. Medius

Medius is an automated, progressive dialer that enhances customer interaction. This, in

turn, improves the marketing capabilities of a Contact Center. Medius automates routine

tasks giving Contact Center managers dynamic, real-time control over operations

including campaigns, individual calls, and agent desktops. This helps the manager

effectively manage customer interactions and responses with far greater efficiency.

4. Call Back Manager:

If you do not want to 'wait in queue', your customer does not want to either. The

importance of call queue management can not be emphasized on more. The challenge lies

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in balancing agent surplus with call surplus without having to make the caller wait in

queue for long periods or leave the agent idle. Many Call Centers experience periods of

unusually high call volume and a shortage of agents. The caller holding the line for a long

time experiences frustration. This ultimately results in call abandonment or the caller

venting her/his anger on the agent. This negatively impacts customer service and

performance levels of agents who face the angry caller.

But are queues unavoidable? Common sense seems to say so. A typical call volume chart

of a Call Center shows more spikes than plateaus. No Call Center would deploy an army

of agents to take care of every single spike. Lengthy IVR call flows and music on hold do

not seem to be the answer.

Yes, they are avoidable. Many service providers who have used Servion's CallBack

Manager (CBM) agree that call queues are avoidable. This simple application has the

potential to even out your peaks and move the calls that were not handled by the agents to

a subsequent dip. The CallBack Manager gives an option of a 'call back' to a customer in

a queue, and automates the 'call back' either on availability of the agent or at a time opted

for by the customer. The customer has control over the entire process, allowing her/him

to make an informed decision, based on expected wait time, on whether to stay on line for

an agent or leave a 'call back' request. It is simple, inexpensive, and easy to administer. It

maximizes efficiency and improves customer service

5. Speech Recognition:

Speech is the basic form of human communication. Technology has evolved to enable

humans to interact with software and telephony applications using commands, which

sound like natural language.

As a result, applications built on an Automated Speech Recognition (ASR) engine

have a tremendous impact on customer Self Service and Call Centers, in particular.

Speech applications can be broadly classified as Speech portals and Self Service

applications. Speech portals provide Speech user interface to Web portals. Speech

enabled Self Service applications enhance the productivity of traditional IVR applications

and improve agent productivity in a Contact Center.

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Technology Partners:

AdvaTel

Avaya Communications

Cisco

ClickFox Inc.

Nuance Inc.

Intervoice

Intel

Envox Worldwide

Ecosystem Partners:

Cleo

IBM

Microsoft

Business Partners:

Avaya

Cisco

IBM

i-flex Solutions

Infosys

Talisma

Tata Consultancy Services

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2.1 NEED OF THE STUDY:

The study enables the following,

Designing effective recruitment strategies

Effective compensation and rewards depending upon the job

Align the organizational strategies with the employees’ needs and wants

To balance between work and the personal goals and wants of an employee

contributes positively to the retention of employees

Maintain the employees in continuous learning and growth mode

Identifying correct leadership

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2.2 OBJECTIVES OF THE STUDY

Primary:

To study about attrition at Servion Global Solutions.

Secondary:

To identify the reasons for attrition

To find the employee’s perception towards Servion

To identify the employees satisfaction towards his job

To suggest ways for reducing attrition

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2.3 SCOPE OF THE STUDY

The study operates at different levels

Understands the most appropriate level of evaluating the employees

Teaches different employee retention techniques

The study identifies the employees perception about the company

The study identifies the job satisfaction of the employees.

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2.4 LIMITATIONS OF THE STUDY

The study was restricted only at Chennai branch.

The sample is 150. This may not be the true representation of total population.

Perceptions and attitudes of the employees would have influenced their responses.

Time constraints of the employees would have influenced their responses.

There may be ambiguity in responses and hence there could be bias in findings

2.5 REVIEW OF LITERATURE

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The high attrition rate in the IT industry has always been its greatest concern and

a subject of much analysis and debate. Organisations use different methodologies for

calculating their turnover rate. It is a known fact that turnover calculation is a grey area

which does not always depict the true picture. While a few techniques are common, there

are no proven theories. Further, the approach to this calculation might vary from

organisation to organisation. Disclosure of the figure not only has a direct impact on the

business but also affects employee morale and productivity. Significantly, it might also

trigger a chain reaction—a high attrition rate will lead to more people leaving the

organisation, while a lower rate will act as a retention strategy. It is therefore not

surprising that most industry observers are skeptical when organisations ‘disclose’ their

employee turnover.

A high attrition reflects poorly on an organisation’s ability to hold on to its

people. Monisha Advani, CEO, Emmay HR, says that attrition is unfortunately viewed as

a management flaw when in fact it could well be a recruitment error. In some cases it can

be simply seen as an organisation’s competitor appreciating its quality of hires, and its

output, post-training—almost a backhanded compliment.

“Ideally, attrition should be calculated on a monthly basis for companies that have

over 50 employees for the first five years of its business. Subsequently, a quarterly index

should be applied till a company’s 10th anniversary. After this, annual attrition figures

should be measured and accounted for. This is the optimum within the services industry

as companies tend to have different challenges at different stages of their business

lifecycle; also, maturity achieves stability around a company’s 10th anniversary,” opines

Advani.

Different theories:

Attrition can be ascribed to many factors. Suhas Nerurkar, President, TVA Infotech, lists

a few of them:

The employee base changes each month. So if a company has 1,000 employees in

April 2004 and 2,000 in March 2005, then they may take their base as 2,000 or as

1,500 (average for the year). If the number of employees who left is 300, then the

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attrition figure could be 15 percent or 20 percent depending on what base you take.

Many firms may not include attrition of freshers who leave because of higher studies

or within three months of joining.

In some cases, attrition of poor performers may also not be treated as attrition.

Essentially, the attrition number is also a PR or stock/analyst statement and is prone

to dressing up.

Varied theories are also applied as organizations like to brand themselves differently

as far as their HR and recruitment strategies are concerned. Explains Advani, “Each

company positions itself uniquely in a common market place by claiming to have

exceptional HR policies, procedures and management styles that directly impact retention

or attrition; hence the absence of a homogenous system. Also, in situations where a

common attrition measurement formula is applied, companies find a way to justify their

results to position their statistics differently from their peers on account of having

‘different’ operating practices.”

However, Anil Noronha, Director, HR, Indian Subcontinent, Onward Novell

Software (I) states that most companies use a fairly standard method—the number of

employees who left during the year divided by the average number employed for that

year.

The true picture

The attrition rate that is generally disclosed by most organizations does not

always show the correct picture. Nerurkar acknowledges this to be true. “I agree that the

figure has a direct impact on stock markets, employee morale and customer confidence.

There is too much at stake and neither the US GAAP (Generally Accepted Accounting

Principles) or SEBI requires that this be calculated in a particular way.”

The attrition rate has always been a sensitive issue for all organizations as it can

have major fallout on the bottom-line. Kranti Munje, Senior Manager, HR, Bristlecone

India furthers, “This is because the attrition rate is an indicator of many things intrinsic to

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the organization, and revealing it may affect it negatively. In fact at times disclosing this

data can be like a self-fulfilling prophecy—if you reveal that the attrition is high, it may

actually become higher.”

It is also not uncommon to find companies proclaiming an attrition rate that is

much less than that of others in the industry. Remarks Bijayinee Patnaik, HR Head at

Mahindra Special Services Group (MSSG) “Companies must be projecting their attrition

rate incorrectly because it tends to affect their brand image both internally and externally.

Internally, it sends a wrong signal to their employees and the board of members;

externally, it can affect the company in various ways such as developing a bad image or

dissuading fresh talent from joining.” She regrets that companies do not realise that

hiding their attrition rate is never a solution for reducing the same.

Attrition does not only reflect the hiring policies of an organisation, but also

induction/retention strategies, training methodologies, work culture and many other

factors. Munje reminds that it costs the company valuable time, money and often

credibility (especially where employees develop relationships with customers). “Some

companies just look at the employee turnover in terms of the cost (based on the PwC

Saratoga Institute theory) involved in the hiring and training of individuals. Others look

at the opportunity lost and its cost. Sometimes, companies also use the figure between 50

percent and 200 percent of the annualized salary.”

Organisations aim to reduce voluntary attrition of productive employees and

encourage unproductive staff to leave its fold. “It makes way for career progression, new

thinking and innovation. However, what that number should be again differs from

industry to industry and from country to country as economies vary. The demand vs.

supply of talent/resources plays a critical role too. What is considered a healthy attrition

number in an industry in India may not be so in a more stagnant economy where no new

jobs are being created,” explains Noronha. Nevertheless, zero attrition is unimaginable

and unhealthy for any organisation.

Trends in attrition:

Liberalization of the Indian economy in 1991 paved the way for the growth of the

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IT industry. The most prominent players in the Indian IT industry by the mid-1990s were

Tata Consultancy Services, Infosys, Wipro, Satyam Computer Services Limited, Polaris

Software Labs, and Patni Computer Systems Limited.

By 1995 there was a new trend of ‘poaching' of employees by rival IT firms. Poaching

necessarily meant luring skilled employees of a rival company by offering better pay and

fringe benefits. Over the years, more and more software professionals were also

emigrating to foreign countries, particularly to the US.

By late 1998, the Y2K problem was hanging over companies across the globe and

software services from Indian IT service companies were increasingly in demand. In

1999, of the total number of H1-B visas given to foreign workers by the US, half were to

Indian IT professionals. The average starting yearly salary in computer software jobs, in

that year was $ 60,000 - nearly 10 times the average salary for a computer professional in

a comparable job in India. The employee turnover in 1999-2000 in Indian IT companies

was around 15-20% with the cost of replacing an employee running at over 120% of the

salary per employee.

Combating attrition:

Experts are of the view that since the IT industry thrives on individuals with a

vital knowledge base, the industry should help employees develop their knowledge base

further in addition to giving them appropriate monetary and other compensation in order

to retain talent. Combating attrition involves management of people and a thorough

understanding of the human psyche. High levels of employee turnover occur due to a

combination of various workplace environment influences and personal choices made by

the employees. In 2003, a National Association of Software and Service Companies

(NASSCOM) survey identified some of the major drivers of attrition.

Recruitment: Effective recruitment strategies can help organizations in employee

retention. Companies following the traditional methods of recruitment observed

that a major drawback of the traditional selection processes was either a poor

response or a mismatch between company goals and individuals' expectations.

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Compensation and Rewards: Incentives to employees play a vital role in

motivating and retaining them in the organization. Compensation and rewards in

the IT industry have long included a basic pay component along with a bonus pay

when the company made higher profits. Later firms initiated performance based

pay that rewarded the employee based on his contribution to the overall company

profits.

Organization Culture: Studies and surveys analyzing the psyche of the employee

have found that the work environment has a major impact on the behavior of an

employee. An effective retention strategy would involve acknowledging the

employee as the internal customer and aligning the organizational strategies with

employee needs and wants.

Work-Life Balance: Employees differentiate a good employer from any other

employer through the feeling of ‘wellbeing' that is generated at the workplace. A

balance between work and the personal goals and wants of an employee

contributes positively to the retention of employees

Learning & Growth: The dynamic nature of technology requires the IT industry

to upgrade its operations frequently. So, another way to retain employees is to

help them update their knowledge from time to time through training programs.

Leadership: Surveys also identified poor leadership as one of the reasons for

employee attrition. It was observed that leaders incapable of motivating and

guiding employees pushed employees to change jobs frequently. Wipro initiated

the ‘Wipro Leaders' Qualities Survey in 1992

3.1 RESEARCH METHODOLOGY

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Research in common parlance refers to a search for knowledge. According to

Redman and May, research is defined as “a systematized effort to gain new

knowledge”. Research methodology is a way to systematically solve the problem. It may

be understood as a science of studying how research is done scientifically. The advanced

learner’s dictionary lay down the meaning of research as “a careful investigation or

inquiry especially through search for new facts in any branch of knowledge”.

Descriptive Research:

Descriptive research includes surveys and fact-finding enquiries of different

kinds. The major purpose of descriptive research is description of the state o affairs, as it

exists at present. In social science and business research, we quite often use the term ex

post facto research for descriptive research studies. The main characteristic o this method

is that the researcher has no control over the variables; he can only report what has

happened or what is happening. The methods of research utilized are survey method

including comparative methods.

In descriptive research design the researcher must be able to define clearly, what

he wants to measure and must find adequate methods for measuring it along with clean

cut definition of population, researcher wants to study, since the aim is to obtain complete

and accurate information in the studies. In descriptive study, the researcher takes out

samples and then wishes to make statements about the population on the basis of sample

analysis. In descriptive study the first step to specify the objectives with sufficient

perception to ensure that data collected are relevant. The data collected must be analyzed

and processed. Thus this is clearly stated that the researcher has applied descriptive

research design.

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Data collection:

The method for collecting primary data was through questionnaire and the secondary

method was through various books, company records, websites and previous researchers

finding.

Questionnaire Method:

A questionnaire consists of a number of questions printed or typed in a definite

order on a form or set of forms. The questionnaire is issued to the respondents who are

expected to read and understand the questions and write down the reply in the space

meant for purpose in the questionnaire itself.

Types of Questions:

1. Open – end questions: In these questions, the respondents are given freedom to

express their views, as there is a wide range choice.

2. Closed questions: these types of questions do not allow the respondents to answer

freely.

3. Multiple-choice questions: these types of questions consist of many questions. The

Data Collection

Primary Data Secondary Data

Observation Interview

Questionnaire Books Magazines Journals

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respondents have to select any one of their choices.

Collection Method:

The method used in study for the collection of data through Census Method. All

items in any field of inquiry constitute a Universe or Population. A complete enumeration

of all items in the population is known as census or inquiry. In this method all items are

covered, no elements are left and highest degree of accuracy is obtained.

Sampling Techniques:

Convenience Sampling:

Convenience sampling is that in which the study units that happen to be available

at the time of data collection are selected for purposes of convenience. It is common

method for selecting participants to a focus group discussion.

Types of sampling

Probability sampling Non-probability sampling

Simple random

Systematicrandom

Stratified Convenience Quota Judgment

Cluster

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Analytical Techniques:

Analytical techniques are used to obtain findings and arrange information in a logical

sequence from the raw data collection. After the tabulation of data the statistical tools

used in this project work are,

1) Weighted average

2) Chi-square

3) Interval estimation

4) Anova

5) Percentage method

Weighted average:

In case of data involving rating scales, the weighted ranking method has been

used for analysis. Using this method the net scores can be calculated and the analysis can

be done on the basis of the net score obtained. The formula used here is

Net score = weighted average for column * No. of respondents in that column

Total weight

Chi square test:

This is a test method used to test the null hypothesis statistically; hence this

technique was used to check the validity of the hypothesis statement. The formula used

here is

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Interval estimation:

The main purpose of conducting a sample survey is to estimate the population

parameters using the corresponding values obtained from the sample population.

Formula used in interval estimation is,

Percentage method:

The collected data is converted into 100% and the percentage has been analyzed

Karl Pearson’s Correlation Test:

It indicates the strength and direction of a linear relationship between two random

variables. In general statistical usage, correlation or co-relation refers to the departure of

two variables from independence.

The formula used is,

Where r is the correlation co-efficient

Anova:

ANOVA stands for analysis-of-variance, a statistical model meant to analyze

data. Generally the variables in an ANOVA analysis are categorical, not continuous. The

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http://en.wikipedia.org/wiki/Random_variables

http://en.wikipedia.org/wiki/Random_variables

term main effect is used in the ANOVA context. The main effect of x seems to mean the

result of an F test to see if the different categories of x have any detectable effect on the

dependent variable on average.

Correction Factor (CF) = T2/N

Total Sum of Squares = + + + + 2 - CF

Sum of squares of Column (SSC)

( ) + ( ) + ( ) + ( ) + ( )2 = _______________________________________________ - CF n

Sum of squares of Rows (SSR)

( ) + ( ) + ( ) + ( ) + ( )2 = _______________________________________________ - CF n

Sum of Square of Error (SSE) = TSS - SSC - SSR

Construct ANOVA table.

Calculate the values of F

F1 (for variance between the rows) = Variance between the rows / Residual variance

F2 (for variance between the columns) = Variance between the columns/ Residual variance

1. Find the tabulated values of F for their respective degrees of freedom.

2. If calculated F < tabulated F then we accept the null hypothesis.

3. If calculated F > tabulated F then we reject the null hypothesis.

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3.2 ANALYSIS & INTERPRETATION

Table 3.2.1 Table showing the age of the employees

S.No Options (Yrs) Respondents Percentage %

1 20 - 25 66 44

2 26 - 30 59 39.33

3 31 - 35 20 13.33

4 36 - 40 5 3.33

5 Above 40 0 0

Total 150 100

Findings: From the above table, 44% of the employees are in the age group between 20 -

25 yrs, 39.33% between 26 - 30 yrs, 13.33% between 31 - 35 yrs and 3.33% are in the

range 36 - 40 yrs.

Inference: It is inferred that majority of the employees belong to young age and majority

belong to middle age.

25

20 -

25

26 -

30

31 -

35

36 -

40

Abo

ve 4

0

010

2030

40

50

60

70

AGE OF EMPLOYEES

Respondents

Percentage %

Figure 3.2.1 Chart showing the age of the employees

Table 3.2.2 Table showing qualification of employees

S.No Options Respondents Percentage %

1 HSC 0 0

2 Diploma 0 0

3 UG 76 50.66

4 PG 72 48

5 PG Dip 2 1.33

Total 150 100

Findings: From the above table, 50.66% of the employees are bachelor degree holders,

48% are master degree holders 1.33% are PG diploma holders.

Inference: It is inferred that majority of the employees are Undergraduates and minority

are PG diplomas.

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Figure 3.2.2 Chart showing qualification of employees

Table 3.2.3 Table showing employees’ work experience


1 0 – 2 54 36

2 2 – 4 38 25.33

3 4 – 6 23 15.33

4 6 – 8 24 16

5 Above 8 11 7.33

Total 150 100

Findings: From the above table, 36% of the employees have 0 – 2 yrs work experience,

25.33% have 2 – 4 yrs work experience, 15.33% have 4 – 6 yrs work experience, 16%

have 6 – 8 yrs work experience and 7.33% have more than 8 yrs work experience.

Inference: It is inferred that majority of the employees are experienced and the minority

is entry level.

EDUCATION

51%48%

1%

Diploma

UG

PG

PG Dip

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WORK EXPERIENCE

37%

25%

15%

16%

7%

0 – 2 yrs

2 – 4 yrs

4 – 6 yrs

6 – 8 yrs

Above 8 yrs

Figure 3.2.3 Chart showing employees’ work experienceTable 3.2.4 Table showing whether attrition is an important issue in IT


1 Strongly agree 42 28

2 Agree 71 47.33

3 Neither agree nor disagree

32 21.33

4 Disagree 5 3.33

5 Strongly disagree 0 0

Total 150 100

Findings: From the above table, 28% of the employees strongly agree, 47.33% agree

21.33% neither agree nor disagree and 5% disagree that attrition is a serious issue in IT

industry.

Inference: It is inferred that majority of the employees agree and majority disagree on

attrition in IT sector.

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Figure 3.2.4 Chart Table showing whether attrition is an important issue in IT

Table 3.2.5 Table showing the reasons of attrition


1 Career development 52 34.66

2 Improper Leadership

16 4

3 Low CTC 41 27.33

4 Dissatisfied company policy

18 12

5 Monotonous work 23 15.33

Total 150 100

Findings: From the above table, 34.66% of the employees feel the reason for leaving the

company is due to career development, 4% feel improper leadership, 27.33% feel low

CTC, 12% feel dissatisfied company policy and 15.33% feel as monotonous work.

Inference: It is inferred that majority of the employees feel attrition is due to career

development & low CTC and minority feel as monotonous work and improper

leadership.

28%

48%

21%

3% Strongly agree

Agree

Neither agree norDisagree

Disagree

Strongly disagree

29

35%

11%27%

12%

15% Career development

Improper Leadership

Low CTC

Dissatisfied companypolicy

Monotonous work

Figure 3.2.5 Chart showing the reasons of attrition

One – Way Anova Test

Table 3.2.6: Comparative analysis of work experience and reasons for attrition

Ho: There is no association between work experience and attrition

ATTRITION

WORK EXP

CAREER DEVELOPM

ENT

IMPROPER

LEADERSHIP

LOW CTC

DISSATISFIED

COMPANY POLICY

MONOTONOUS WORK

TOTAL

0 – 2 yrs 22 3 17 5 7 542 – 4 yrs 9 4 11 6 8 384 – 6 yrs 9 4 11 6 8 236 – 8 yrs 8 3 5 3 5 24Above 8

yrs4 4 1 1 1 11

Total 52 16 41 18 23 150

Calculations:

Correction Factor (CF) = T2 / N

30

= 1502 / 25

= 900

Total Sum of squares (TSS) = + + + + 2 - CF

= 588

Sum of square between samples (SSB) =

= 198.8

Sum of square within samples (SSW) = TSS – SSB

= 398.2

One way Anova table

Source of variation

Sum square

Degree of freedom

Mean square Variance ratio

Between samples

SSB (n – 1) = 4 MSB = SSB/ n – 1 = 198.8 / 4

= 49.7F= 49.7/3.98

=12.48Within sample

SSW (n – c) = 10 MSW =SSW / (m – 1)(n – 1)

= 398 / 10 = 3.98

F1 = 12.48

Fα = 0.05 at (4,10) df = 3.48

Fα < Fcal,

31

Hence we reject Ho.

There is association between work experience & attrition

Conclusion:

Hence we conclude that there is association between work experience and the reasons for

attrition.

Table 3.2.7 Table showing the factors that minimizes attrition


1 Challenging work 41 27.33

2 Reduce work pressures

32 21.33

3 Reduce workloads 23 15.33

4 Employee empowerment

28 18.66

5 Effective grievance procedure

26 17.33

Total 150 100

Findings: From the above table, 27.33% of the employees feel the factor minimizing

attrition is providing challenging work, 21.33% feel by reducing work pressures, 15.33%

feel by reducing work loads, 18.66% feel by empowering employees and 15.33% feel

having an effective grievance procedure.

Inference: It is inferred that majority of the employees feel attrition can be minimized by

challenging work and reducing work pressures and minority feel can be by reducing work

32

loads

FACTORS MINIMIZING ATTRITION

28%

21%15%

19%

17%

Challenging work

Reduce work pressures

Reduce workloads

Employee empowerment

Effective grievanceprocedure

Figure 3.2.6 Chart showing the factors that minimizes attritionKarl Pearson’s Correlation Test

Table 3.2.8: Comparative Analysis between factors causing and factors minimizing attrition

Cause(x)

Solution(y)

xy x2 y2

52 41 2132 2704 1681

16 32 512 256 1024

41 23 943 1681 529

18 28 504 324 784

23 26 598 529 676

150 150 4689 5494 4694

33

r = 0.430

Conclusion: The correlation between factors causing attrition and factors minimizing

attrition is 0.43

Table 3.2.9 Table showing employees’ time with Servion


1 0 – 2 93 62

2 2 – 4 24 16

3 4 – 6 19 12.66

4 6 – 8 8 5.33

5 Above 8 6 4

Total 150 100

Findings: From the above table, 62% of the employees have 0 – 2 yrs work experience at

Servion, 16% have 2 – 4 yrs work experience, 5.33% have 4 – 6 yrs work experience,

12.66% have 6 – 8 yrs work experience and 4% have more than 8 yrs work experience.

Inference: It is inferred that majority of the employees new to the company and minority

are seniors.

34

0

10

20

30

40

50

60

70

80

90

100

1

WORK EXPERIENCE AT SERVION

0- 2 yrs

2 – 4 yrs

4 – 6 yrs

6 – 8 yrs

Above 8 yrs

Figure 3.2.7 Chart showing employees’ time with Servion

Table 3.2.10 Table showing the no. of previous employers


1 First employer 71 47.33

2 2 to 4 61 40.66

3 More than 4 18 12

Total 150 100

Findings: From the above table, 47.33% of the employees have Servion as their 1st

employer, 40.66% have already worked with 2 to 4 employers and 12% have worked

with more than 4 employers.

Inference: It is inferred that majority of the employees are new to job and minority have

already worked with more than 4 companies.

35

PREVIOUS EMPLOYERS

47%

41%

12%

First employer

2 to 4

More than 4

Figure 3.2.8 Chart showing the no. of previous employers

Table 3.2.11 Table showing employees’ satisfaction with the company


1 Highly satisfied 21 14

2 Satisfied 98 65.33

3 Neither satisfied nor dissatisfied

27 18

4 Dissatisfied 3 2

5 Highly Dissatisfied 1 0.66

Total 150 100

Findings: From the above table, 14% of the employees are highly satisfied with Servion,

65.33% are satisfied, 18% neither satisfied nor dissatisfied, 2% are dissatisfied and 0.66%

are highly dissatisfied respectively.

Inference: It is inferred that majority of the employees are satisfied and minority are

dissatisfied with Servion.

36

14%

65%

18%

1%2%Highly satisfied

Satisfied

Neither satisfied nordissatisfied

Dissatisfied

Highly Dissatisfied

Figure 3.2.9 Chart showing employees’ satisfaction with the company

Table 3.2.12 Table showing whether employees are given value by the management


1 Yes 145 96.66

2 No 5 3.33

Total 150 100

Findings: From the above table, 96.66% of the employees are given value by the

management and 3.33% are not.

Inference: It is inferred that majority of the employees are valued and minority aren’t

valued by the management.

37

EMPLOYEES ARE VALUED

97%

3%

Yes

No

Figure 3.2.10 Chart showing whether employees are given value by the management


Values:p = 0.9667 q = 1-p

q = 0.0334

Zα/2 = 1.96 n = 150

Substituting in the above formula,

IE = (0.9379, 0.9953)

Conclusion: At 95% confidence level, the intervals are between 0.9379 & 0.9953

38

Table 3.2.13 Table showing employees perception towards Servion


1 Excellent 24 16

2 Good 88 58.66

3 Neither good nor

bad

38 25.33

4 Bad 0 0

5 Worst 0 0

Total 150 100

Findings: From the above table, 16% of the employees feel the company is excellent,

58.66% feel the company is good, 25.33% neither good nor bad.

Inference: It is inferred that majority of the employees feel the company is good and

minority feel as excellent.

39

EMPLOYEES' PERCEPTION TOWARDS SERVION

16%

59%

25%

0%0% Excellent

Good

Neither good norbad

Bad

Worst

Figure 3.2.11 Chart showing employees perception towards Servion

Two way Anova

3.2.14: Comparative analysis between employees’ period with the company and

perception towards the company

Ho: There is no association with the period and perception about the company.

Perception Period

Excellent GoodNeither

good nor bad

Total

0 – 2 yrs 3 65 25 932 – 4 yrs 5 12 7 244 – 6 yrs 7 10 2 19

Above 6 yrs 9 1 4 14Total 24 88 38 150

40

Calculations:

Let X1, X2, X3 be the responses for perception

And Y1, Y2, Y3, Y4 be the period

Correction Factor (CF) = T2 / N

= 1502 / 12

= 1875

Total Sum of squares (TSS) = + + + - CF

= 3365

Sum of square of rows (SSR) =

= 570.5

Sum of square of columns (SSC) =

= 1379.66

Sum of square of error (SSE) = TSS – SSR –SSC

= 1414.84

Two-Way Anova Table

Source of variation

Sum square Degree of freedom

Mean square Variance ratio

Between rows SSR (m - 1) = 3MSR = SSR/ m – 1

= 570.5 / 3= 190.16

F1=MSE/MSR=1.33

Between columns SSC (n – 1) = 2

MSC = SSC/ n – 1= 1379.66 / 2

= 689.83F2=MSC/MSE

=2.71

41

Within sample SSE (m – 1) (n – 1)= 6

MSE =SSE / (m – 1)(n – 1)

= 1414.84 / 6= 253.80

F1 = 1.33 F2 = 2.71

Fα = 0.05 at (3,6) df = 4.76 Fα = 0.05 at (2,6) df = 5.14

Fα > Fcal Fα > Fcal

Hence we accept Ho

Conclusion:

Hence we conclude that there is no association between period with company and

employees perception towards the company.

Table 3.2.15 Table showing whether employees are matched with right job & compensation


1 Yes 143 95.33

2 No 7 4.66

Total 150 100

Findings: From the above table, 95.33% of the employees are placed in the right job with

the right compensation and 4.66 are not.

Inference: It is inferred that majority of the employees placed in the right job with the

right compensation and minority aren’t.

42

JOB & COMPENSATION MATCHED

95%

5%

Yes

No

Figure 3.2.12 Chart showing whether employees are matched with right job & compensation


Values:p = 0.9533 q = 0.0.0466

Zα/2 = 1.96 n = 150


IE = (0.9195, 0.9863)


43

Table 3.2.16 Table showing the changes found in employees after joining Servion


1 Increased domain/ job knowledge

78 52

2 Attitude changes 31 20.66

3 Became efficient 13 8.66

4 Became effective 22 14.66

5 No change 6 4

Total 150 100

Findings: From the above table, 52% of the employees have got increased domain and

job knowledge after joining Servion, 20.66% have changes in attitude, 8.66% became

efficient, 14.66% became effective and rest 4% found no changes respectively.

Inference: It is inferred that majority of the employees have got increased domain/job

44

knowledge and minority found no changes after joining Servion.

CHANGES AFTER JOINING SERVION

51%

21%

9%

15%4% Increased domain/

job knowledge

Attitude changes

Became efficient

Became effective

No change

Figure 3.2.13 Chart showing the changes found in employees after joining Servion

Table 3.2.17 Table showing whether employees would refer Servion to others


1 Yes 142 94.66

2 No 8 5.33

Total 150 100

Findings: From the above table, 94.66% of the employee says they will refer their

friends or relative to join Servion and the rest 5.33% would not.

Inference: It is inferred that majority of the employees will refer to their friends and

relatives and minority would not.

45

0

20

40

60

80

100

120

140

160

RESPONDENTS PERCENTAGE

Yes

No

Figure 3.2.14 Chart showing whether employees would refer Servion to others


Values:p = 0.9466 q = 0.0533

Zα/2 = 1.96 n = 150


IE = (0.9106, 0.9826)


46

Table 3.2.18 Table showing the ranks of the motivational factors by Servion

Options Rank 1

Rank 2

Rank 3

Rank 4

Rank 5

Promotion 42 36 24 30 18

Rewards 45 39 21 21 24

Awards 15 30 72 6 27

Salary hikes 30 24 21 60 15

Mentors 18 21 12 33 66

Applying weighted average method

Ranks First Second Third Fourth

Fifth

Weights 5 4 3 2 1

47

W1 W2 W3 W4 W5 Total WtdAvg

Rank

210 144 72 60 18 504 33.6 II

150 96 63 120 15 444 29.6 IV

75 120 216 12 27 450 30 III

225 156 63 42 24 510 34 I

90 84 36 66 66 369 24.6 V

Findings: From the above table, 1st rank is given to salary hikes, 2nd rank to promotion,

3rd rank is given to awards, 4th rank is given to awards and 5th rank mentoring for the

motivational factors given by Servion to the employees. .

Inference: It is inferred that 1st ranks falls on salary hikes and 5th rank to mentoring.

23%

19%

20%

22%

16%

Promotion

Rewards

Awards

Salary hikes

Mentors

Figure 3.2.15

48

Chi-square Test

Table 3.2.19: Analysis of motivational factors

Ho: There is no association between the motivational factors.

Oi Ei (Oi - Ei)2 (Oi - Ei)2 / Ei33.6 30 12.96 0.432

29.6 30 0.16 0.005

30 30 0 0.0004

34 30 0.16 0.533

24.6 30 29.16 0.972

Total 1.94

cal = 1.94

49

0.05 = 9.49

cal < 0.05

Hence, we accept Ho

Conclusion:

Hence we conclude that there is no association between the motivational factors.

Table 3.2.20 Table showing the flexibility of the work schedule


1 Highly Flexible 43 28.66

2 Flexible 63 42

3 Neither flexible 42 28

4 Inflexible 2 1.33

5 Highly inflexible 0 0

Total 150 100

Findings: From the above table, 28.66% of the employees feel the work schedule is

highly flexible, 42% feel as flexible, 28% feel its neither flexible nor inflexible, 1.33%

feel as inflexible.

Inference: It is inferred that majority of the employees feel the work schedule is flexible

and minority feel as inflexible.

50

FLEXIBILITY IN WORK SCHEDULE

29%

42%

28%

1%

Highly Flexible

Flexible

Neither flexible

Inflexible

Figure 3.2.16 Chart showing the flexibility of the work schedule

Chi Square test

Table 3.2.21: Comparative analysis of flexibility in work schedule and changes in

the skills

Ho: The flexibility in work schedule and changes in the skills are independent

Factors 1 2 3 Total

Flexibility 102 42 6 150

Changes 78 37 35 150

Total 180 79 41 300

51

Ei = Row total * Column total

Grand total

Oi Ei (Oi - Ei)2 (Oi - Ei)2 / Ei

102 90 144 1.6

78 90 144 1.6

42 39.5 6.25 0.15

37 39.5 6.25 0.15

6 20.5 210.25 10.25

35 20.5 210.25 10.25

Total 24.02

cal = 24.02

0.05 = 5.99

cal > 0.05

Hence, we reject Ho

Conclusion:

Hence we conclude that flexibility in work schedule and changes in the skills are

dependent

52

Table 3.2.22 Table showing the importance of career planning to overcome attrition


1 High 75 50

2 Medium 63 42

3 Low 12 8

Total 150 100

Findings: From the above table, 50% of the employees feel career planning should be

given high priority to overcome attrition, 63% feel medium priority should be given and

12% feel low priority.

Inference: It is inferred that majority of the employees feel career planning should be

given high priority to overcome attrition and minority feel low priority.

53

CAREER PLANNING

50%

42%

8%

High

Medium

Low

Figure 3.2.17 Chart showing the importance of career planning to overcome attrition

Table 3.2.23 Table showing the importance of Performance appraisal to overcome attrition


1 High 107 71.34

2 Medium 37 24.67

3 Low 6 4

Total 150 100

Findings: From the above table, 71.33% of the employees feel performance appraisal

should be given high priority to overcome attrition, 24.66% feel medium priority should

be given and 4% feel low priority.

Inference: It is inferred that majority of the employees feel performance appraisal should

be given high priority to overcome attrition and minority feel low priority.

54

PERFORMANCE APPRAISAL

71%

25%

4%

High

Medium

Low

Figure 3.2.18 Chart showing the importance of Performance appraisal to overcome attrition

Chi Square test

Table 3.2.24: Comparative analysis between career planning & performance appraisal

Ho: The factors used to overcome attrition are independent

Factors High Medium Low Total

Career planning

75 63 12 150

Performance appraisal

107 37 6 150

Total 182 100 18 300

55

Ei = Row total * Column total

Grand total

Oi Ei (Oi - Ei)2 (Oi - Ei)2 / Ei

75 91.67 277.77 3.03

107 91. 67 235.11 2.56

63 46. 67 266.77 5.71

37 46. 67 93.44 2.00

12 11. 67 0.11 0.01

6 11. 67 32.11 2.75Total 19.49

cal = 19.49

0.05 = 9.49

cal > 0.05

Hence, we reject Ho

Conclusion:

Hence we conclude that the factors used to overcome attrition are dependent

56

3.3 FINDINGS

It is found that nearly 80% of the employees’ belong to younger generation that is

between 20 and 30

It is found that 50.66% of the employees are bachelor degree holders, 48% are master

degree holders 1.33% are PG diploma holders

It is found that nearly 75% of the employees feel that attrition is a serious issue in IT

industry.

It is found that 34.66% of the employees feel the reason for leaving the company is

due to career development, 4% feel improper leadership, 27.33% feel low CTC, 12%

feel dissatisfied company policy and 15.33% feel as monotonous work.

It is found that most of the employees feel that attrition can be minimized by

providing challenging work by reducing work pressures and by reducing work loads,

It is found that 80% of the employees are satisfied with Servion,

57

It is found that 96.66% of the employees are given value by the management.

It is found that 16% of the employees feel the company is excellent, 58.66% feel the

company is good, 25.33% neither good nor bad.

It is found that 95.33% of the employees are placed in the right job with the right

compensation.

It is found that 52% of the employees have got increased domain and job knowledge

after joining Servion, 20.66% have changes in attitude, 8.66% became efficient,

14.66% became effective and rest 4% found no changes respectively.

It is found that 94.66% of the employee says they will refer their friends or relative to

join Servion.

It is found that the company is giving importance to salary hikes and to promotion for

motivating the employees.

It is found that 28.66% of the employees feel the work schedule is highly flexible,

42% feel as flexible, 28% feel its neither flexible nor inflexible, 1.33% feel as

inflexible.

It is found that career planning and performance appraisal should be effectively and

efficiently utilized in order to reduce attrition.

58

3.4 SUGGESTIONS

The management can boost intrapreneurship to increase the morale of the employees.

The management can arrange for one day workshops.

Understand employee’s individual problems and provide right solutions and can also

provide facilities they need.

The management need to provide more challenging and newer assignments

The management must give chances to employees to implement their ideas and give

support to achieve their best performance level.

The management arrange for monthly meetings with Supervisors to know the actual

problems and for suggestions.

The functional heads should make a point to check about the employees’ satisfaction

often.

59

Continuous employee engagement activities can be practiced.

The company must concentrate on recreational activities as there is no such thing up

to now.

3.5 CONCLUSION

From the study it is clear that the attrition in IT sector is mainly due to career

development and low CTC. People change their job once a company gives more

compensation and better job compared to the current employer. The employees at

Servion are satisfied with the company and their perception towards the company is also

good. The company has given continuous motivation and encouragement to the

employees. Based on the above factors the company is having a good control over

60

attrition.

61

a study on attrition analysis

Documents

bachelor degree

pg diploma

companys 10th

contact optimization

computer telephony

capability

employee contributes

reducing work