Data mining research for customer relationship ...

Int. J. Business Information Systems, Vol. 3, No. 5, 2008

Data mining research for customer relationship management systems: a framework and analysis Sarika Sharma Apeejay School of Management Dwarka, New Delhi, India E-mail: [email protected]

D.P. Goyal* Institute of Management Education GT Road, Sahibabad, Ghaziabad, India E-mail: [email protected] *Corresponding author

R.K. Mittal GGS Indraprastha University Kashmere Gate, Delhi, India E-mail: [email protected] Abstract: Data mining is a new technology that helps businesses to predict future trends and behaviours, allowing them to make proactive, knowledgedriven decisions. When data mining tools and techniques are applied on the data warehouse based on customer records, they search for the hidden patterns and trends. These can be further used to improve customer understanding and acquisition. Customer Relationship Management (CRM) systems are adopted by the organisations in order to achieve success in the business and also to formulate business strategies, which can be formulated based on the predictions given by the data mining tools. Basically three major areas of data mining research are identified: implementation of CRM systems, evaluation criteria for data mining software and CRM systems and methods to improve data quality for data mining. The paper is concluded with a proposed integrated model for the CRM systems evaluation and implementation. This paper focuses on these areas, where there is need for more explorations, and will provide a framework for analysis of the data mining research for CRM systems. Keywords: data mining; Customer Relationship Management systems; CRM systems; evaluation; implementation; data quality. Reference to this paper should be made as follows: Sharma, S., Goyal, D.P. and Mittal, R.K. (2008) ‘Data mining research for customer relationship management systems: a framework and analysis’, Int. J. Business Information Systems, Vol. 3, No. 5, pp.549–565.

Copyright © 2008 Inderscience Enterprises Ltd.

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S. Sharma, D.P. Goyal and R.K. Mittal Biographical notes: Sarika Sharma is a Senior Lecturer (IT) at the Apeejay School of Management, New Delhi. She has done MCA from the Banasthali Vidyapith and is currently pursuing her PhD at the GGSIP University, Delhi. She has written research papers for various journals on data mining, data warehousing, customer relationship management and software engineering. D.P. Goyal, PhD, is serving as a Professor of Information Systems Strategy and as the Director of the Institute of Management Education, Ghaziabad, India. Prior to joining this institute, he served at the Institute of Management Technology, at the Punjab School of Management Studies, Punjabi University and at Thapar University, India. With over 22 years of teaching, executive development, consultancy, research and academic administrative experience, Dr. Goyal is the author of two books in the area of MIS and has regularly contributed to journals and presented his research in several national and international conferences. He has been the principal investigator of various sponsored research projects and has supervised a number of PhD candidates. Currently, he is involved in supervising research projects in the areas of strategic information systems, decision support systems and enterprise resource planning systems. Professor R.K. Mittal is currently the Dean of the University School of Management Studies at the Guru Gobind Singh Indraprastha University. He is also the Dean of the University School of Humanities and Social Sciences and University School of Education. His research articles have been regularly published in numerous research journals of national and international repute. He has been instrumental in organising several prestigious national conferences, seminars and faculty development programmes. His areas of interest include managerial economics, financial institutions and economic environment of business.

1

Introduction

Customer Relationship Management (CRM) is an enterprise’s approach to understanding and influencing customer behaviour through meaningful communications in order to improve customer acquisition, customer retention, customer loyalty and customer profitability. Companies maintain data warehouses to store customers’ records comprising their detailed personal information and every transaction made by them. Data is stored in large data warehouses and is further used for mining to identify the hidden patterns. This is achieved by the data mining process which, according to Pregibon (1997), is a blend of statistics, Artificial Intelligence, and database research. In today’s business scenario, extremely demanding customers and technology have introduced a new dimension of CRM. The CRM cycle (Figure 1) is given by Rigby and Ledingham (2004) and he suggested that the deployment of a comprehensive CRM system could automate every stage of a company’s relationship cycle. Data mining can improve the profitability in each of these stages through customer interaction with operational CRM systems or as independent applications. In fact it can provide the companies with models and methods using which, companies can interact with customers with more ease and confidence and give them better services.

Data mining research for CRM systems: a framework and analysis Figure 1

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The CRM cycle

Development of offering

Targeting and marketing

Sales

CRM cycle Retention and win-back

Superior experience

Many authors have stressed on the importance of CRM in maintaining and increasing customer satisfaction in order to create greater loyalty and thus enhance business performance for the organisation. A typical CRM system consists of three components, namely, analytical systems, operational systems and contact systems.

2

Data mining for customer relationship management

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining is the use of various techniques to extract comprehensible hidden and useful information from a population of data (Wu, 2000). Data mining is also the analysis of large observational data sets to find unsuspected relationships and to summarise the data in novel ways that are understandable and useful to the data owner as suggested by Hand et al. (2001). Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time-consuming and complicated to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. The process of data mining, according to Han and Micheline (2004), contain seven steps. The first three are the preprocessing steps, before the application of the data mining tool on the data warehouse. Step 1

Data cleaning – To remove noise and inconsistent data.

Step 2

Data integration – Where multiple data sources may be combined.

Step 3

Data selection – Data relevant to the analysis task are retrieved from the database.

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Step 4

Data transformation – Data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations.

Step 5

Data mining – Process where intelligent methods are applied in order to extract data patterns.

Step 6

Pattern evaluation – To identify the truly interesting patterns representing knowledge based on some interestingness measure.

Step 7

Knowledge presentation – Visualisation and knowledge representation technique are used to present the mined knowledge to the user.

The basic task of the data mining tools is to extract knowledge from the data such that the resulting knowledge is useful for a given application. Obviously only the user can judge if the performance of the tool fits into the requirement or not. Major vendors of data mining tools provide graphical views to the model generated as outcome of the tool (Ankerst, 2001). Various data mining tools are available in market, which can be used for specific business applications. These tools can be applied at every stage of the data mining cycle; if used in the right way at the right time, it can lead to a better customer relationship. Data mining is a technique that helps companies to provide more customised service to their customers, and data mining tools can predict future trends and behaviours. Han and Micheline (2004) identified the need to be aware of how data mining tools are used in real applications. According to him, in the life cycle of a new technology adoption, data mining falls in the chasm stage, which is considered as a gap stage, and more research is required on its applications. We need to focus on the integration of data mining into business technology.

3

Implementation of data mining and CRM systems

Most of the amount spent on the CRM systems goes down the drain because of lack of proper guidance regarding the implementation issues. However, while the importance of the CRM is recognised and interest is high, there is also a great deal of confusion regarding how effective CRM strategies can be developed. CRM software is increasingly being offered as the solution to many operational problems. The guidelines for effective data mining implementation given by Magnini et al. (2003) are: •

Match your IT priorities with an appropriate provider.

•

Build segmentation and predictive models.

•

Collect data to support the models.

•

Select the appropriate tools for analysis and prediction.

•

Demand timely output.

•

Refine the process.

•

Hire a well-trained staff and a knowledgeable IT manager.

Data mining research for CRM systems: a framework and analysis

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Dasgupta (2003) is of the view of implementing the CRM in stages and points out its three stages: operational, analytical and collaborative. Rigby and Ledingham (2004) believe that in evaluating and designing CRM systems, business needs should take precedence over technological capabilities. Managers should not be distracted by what CRM software can do; they should concentrate instead on what it should do – both for their companies and for their customers. SAS (2006) in its white paper provided the six business imperatives and their technology-based implications for a successful CRM strategy for implementing these systems in banking and financial institutions. 1

Establish a data architecture that supports a single view of the customer

2

Implement analytics that support customer segmentation and profiling

3

Implement modules to analyse and predict risk and profitability

4

Implement modules that maximise cross-sell and up-sell initiatives

5

Implement customer retention modules in your Decision Support System (DSS)

6

Implement an integrated campaign management system.

Ansari et al. (2001) proposed a high-level system architecture for applying the data mining process for managing customer interaction, which starts with the business data definition to customer interaction and moving to the analysis, and from the analysis phase again moving back to business data definition forming a cycle. Edelstein (2006) has given seven basic steps for data mining for effective CRM, which are: Step 1

Define business problem

Step 2

Build marketing database

Step 3

Explore data

Step 4

Prepare data for data mining

Step 5

Build model

Step 6

Evaluate model

Step 7

Deploy model and results.

Thearling (1999) in his book states that while implementing the CRM, one needs to automate the right offer, to the right person, at the right time, through the right channel. This will lead to a better understanding of the customers and can provide tangible benefits and a measurable return on investment. He also points out that the application of data mining technology should be relevant to the business process. Meltzer (2000) also exhibits another model, which combines technology, data mining process and the business problems together in a single view. He matched the methods to the business problem by considering the data mining process as a bridge between technologies and business problems.

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Chaudhuri and Shainesh (2001) identified the five core areas of business transformation. These are business focus, organisational structure, business metrics, marketing focus and technology. While selecting and implementing a technology-based solution, the success of CRM initiatives is contingent on various decisions pertaining to technology, which can be either the decision to: •

Buy or make the software

•

From whom to buy.

They also proposed a model for post implementation. Bordoloi (2000) also discussed a framework for the CRM success and gave the necessary steps for the implementation as follows: Step 1

Formulate a solid CRM vision

Step 2

Secure executive commitment to the CRM project

Step 3

Let business processes drive the CRM implementation

Step 4

Choose technology partners wisely

Step 5

Assemble a stellar implementation team

Step 6

Manage organisational change effectively.

The guidelines for implementing the CRM given by Boland et al. (2002) are as follows: •

Develop a vision

•

Focus on customer value

•

Empower the employee

•

Set targets and success metrics

•

Address customer needs throughout the life cycle.

Six steps are given by the Oracle Peoplesoft (2003) enterprise as follows: Step 1

Make CRM an enterprisewide strategic initiative.

Step 2

Take ownership for customer data.

Step 3

Identify our primary customer.

Step 4

Develop a definitive ROI strategy.

Step 5

Create a realistic budget and timeline.

Step 6

Seek experienced resources.

From the above analysis it can be concluded that most of the authors have emphasised on the importance of the proper and well-defined business problem for the successful implementation of the CRM systems. Also, technology should be deployed, by taking into consideration the company’s vision and the business strategy.


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Evaluation of data mining software

The evaluation phase consists of evaluating the analysis, interpreting the results and visualising the model. The data mining project is assessed on how effectively it met the objectives set at the beginning of the project. The presentation and communication of the data mining results must be clear, visual and textual. Communication models should be used in the presentation as given by Ceglar et al. (2003). Companies are spending a large amount on data mining and CRM systems. The evaluation of Return On Investments (ROI) is a complex procedure for these companies. Though researchers have tried to give the evaluation and measurement criteria for data mining and CRM, few researchers have explored the relationship between the two. As a result, there is no adequate literature available for the effectiveness of data mining tools and techniques for CRM systems. Collier et al. (1999) in his study proposed a framework for evaluating the data mining software, which consists of four categories of criteria. These criteria are: 1

Performance

2

Functionality

3

Usability

4

Ancillary task support.

After deciding upon the criteria for the data mining software evaluation, the evaluation can be carried out by the assessment model again suggested by Collier et al. (1999), which consists of six steps for the evaluation: Step 1

Tool preprocessing – This step is aimed at reducing the set of tools to a manageable number. Elimination of the tools (which clearly will not be selected owing to rigid constraints of the organisation) is done by the vendors.

Step 2

Identify additional selection criteria – Apart from the evaluation model, organisations can consider tools with respect to their particular environment.

Step 3

Weight selection criteria – Using the framework, weights are attached to each criterion so that the total weight within each category is equal to 1.0% or 100%.

Step 4

Tool scoring – This step scores the tools by comparing them with other tools and scoring is done relative to a reference tool. The reference tool selected should always receive a score of 3 and other tools are rated in reference to this ‘favourite’ tool.

Step 5

Score evaluation – Evaluation of the tool is done after reviewing the weights received by the tool as well as the intuition of the evaluator.

Step 6

Tool selection – The tool best suited to the application with good score will be selected.

Table 1 summarises the different criteria given by various authors for evaluating the data mining software.

Author(s)

King and Elder (1998)

Collier et al. (1999)

Qui et al. (2004)

Labovitz (2003)

Lee and Siau (2001)

S.No.

1

2

3

4

5

– –

√ √

–

–

–

–

–

√

–

√

√

√

Algorithm variety

√

√

Software architecture –

√

Interoperability

–

Heterogeneous data access

√

Prescribed methodology –

–

√

–

√

Model validation –

–

√

–

√

Data type flexibility √

√

–

√

√

Data sampling –

√

–

–

√

User interface √

√

–

–

√

User types –

–

√

√

√

√

–

√

–

√

Data visualisation

Usability criteria

Ancillary task support

–

√

–

–

√

Data cleansing

Functionality criteria

–

–

√

–

√

Data filtering

Computational criteria

–

–

√

–

√

Deriving attributes

Other

–

√

√

–

–

Table 1 Cost

556 S. Sharma, D.P. Goyal and R.K. Mittal

Data mining software evaluation


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From Table 1, the following attributes emerged to be most important for the evaluation of the data mining software, as most of the authors have included these in their evaluation criteria: •

Heterogeneous data access

•

Data type flexibility

•

User interface

•

User types

•

Data visualisation.

Other important criteria can be software architecture, interoperability, algorithm variety, prescribed methodology, model validation, data sampling, data cleansing, data filtering, deriving attributes and cost. From the above analysis it can be observed that for the evaluation, only three factors have emerged; however, some other factors such as software architecture, interoperability, algorithm variety, etc. are also important. These can be further tested by adequate research in this direction. Also, the authors have given some other attributes, which need to be validated through proper research in this area.

5

Evaluation of CRM systems

CRM in the organisations is deployed for the marketing department through the IT department. So the debatable question arises as to who will monitor, analyse, evaluate, and measure the performance of the CRM systems. The studies show that at times the performance is evaluated by the organisation, sometimes by the marketing department. There are no standards for the practice, and moderate literature is available for a proper, well-designed framework for the measurement of the CRM systems. Companies have not standardised the measures of returns and measures of cost. Mukhopadhyay and Nath (2001) emphasised on the importance of measuring the efficiency of CRM systems and proposed an efficiency model. Chang (2004) used a general two-parameter matrix to evaluate the functional requirements against product capability. The product evaluation is given as: VALUE OF REQUIREMENT = Requirement priority X vendor capability to deliver requirement where requirement priority is: HIGH (3 points) MEDIUM (2 points) LOW/NICE TO HAVE (1 point) and vendor capability is: DELIVERED WITH PRODUCT (4 points) REQUIRES SLIGHT MODIFICATION (3 points) REQUIRES HEAVY CUSTOMISATION (1 points) NOT AVAILABLE/POSSIBLE (0 points) allowing for a range of 0 to 12 points per function.

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Thearling (1999) recommended a simple method to evaluate the benefits of a data-mining model for the CRM applications through a gains chart where the diagonal line illustrates the number of responses expected from a randomly selected target audience and a profitability chart, which can help determine the number of prospects to include in the campaign. He also gave a profitability chart, which measures profit/loss with respect to campaign threshold score. Rigby and Ledingham (2004) suggested a model to calculate the cost of CRM. He divided the process of calculating the cost with four different situations and found the probable number of customers who will benefit from it. 1

Do nothing

2

Give $100 to everyone

3

Reward just the right customer

4

Purchase a CRM system.

He also pointed out that while estimating the cost of the CRM system, the additional expenses of training, data collection, data analysis, information dissemination and implementation cost should also be taken into consideration. Kellen (2002) is of the view that how a company measures its CRM activities depends upon who is doing the measuring and what activities are being measured. In his study, he provides a framework for measurement as follows: •

Brand building

•

Customer equity building

•

•

a

Customer behavioural modelling

b

Customer value measurement

Customer-facing operations a

Marketing operations

b

Sales force operations

c

Service centre operations

d

Field service operations

e

Supply chain and logistic operations

f

Website operations

Leading indicator measurement a

Balanced scorecards

b

Customer knowledge management.


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The evaluation should be done by considering the business problem and by taking the views of practitioners. Also, the evaluation criteria may differ for different applications. A research should be carried out to provide the framework for measuring the performance of the CRM systems and a standard framework is solicited in this area. From the above literature it is seen that there is no definite framework for evaluating the CRM systems in an organisation. The process of evaluation is very complex and difficult because of the lack of standard procedures for analysing the performance of CRM.

6

Methods to improve data quality for data mining

Organisational database are pervaded with the poor quality of data as most often the data is not collected for the purpose of data mining in mind, and the sources, which seem to be quite reliable, can provide deflecting information. The data mining tools when applied on this effective data will result in a poor quality model following the ‘garbage in garbage out’ principle of data. If business decisions are taken on these models, they may lead to the failure of the whole CRM initiative. So, business organisations are spending a great amount of their budget on improving the data quality and preprocessing of data for the model. The quality of information can be decided only by the organisation, which is going to use this information. But there is a need to have an agreed framework for the analysis of the quality of data. Some of the guidelines for the data quality are provided by the Department of Defence, which can be applied for most of the applications. Wang et al. (1999) took into consideration about 50 research papers written by various people and designed a framework for the data quality analysis. The elements of this framework can be used by the companies to know the status of quality of their data, which they are using for building the data-mining model. Various people have analysed the impact of the data quality for CRM applications and they have provided the implications. Most of the researchers felt that in a typical data mining session, much time is spent on extracting and manipulating the data (preprocessing), and not really doing the data mining explorations. Because of the bad data, missing values, etc., the data mining process becomes a time-consuming exercise. Various methods are given for the data cleaning, preparing and improving the quality of data by the various people working in this field. Some of the methods are summarised in Table 2. Many researchers have tried to provide models for improving data quality in the databases on which data mining algorithms are applied to generate a model. It can be derived from the above analysis that a majority of the authors have tried to improve data quality by using mathematics and the programming solutions. Some integrated model for improving data quality for the data mining and CRM is the necessity.

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Table 2

Methods for improving the data quality

S.No.

Authors

Methods/Solutions

Remarks

1

Cadot and Martino (2003)

A data cleaning solution by Perl scripts

Algorithm for cleaning the data using Perl.

2

McClean et al. (2001)

Aggregation of imprecise and uncertain information in databases

Database model for handling defective information using aggregation operators.

3

Ganti et al. (2001)

Demon: mining and monitoring evolving data

Data evolution and monitoring algorithms for real-time data.

4

Bohannon et al. (2001)

Detection and recovery techniques for database corruption

Techniques for the recovery of data, fault tolerance and database maintenance.

5

Hipp et al. (2001)

Data quality mining

Using data mining process to improve quality of data.

6

Wu and Barbara (2002)

Learning missing values from summary constraints

Linear Algebra and constraint programming as a basis for the summarised missing values.

7

Ballou and Pazer (2003)

Modelling completeness versus consistency trade-offs in information decision contexts

Measurement of completeness and consistency and trade-offs related to information.

8

Parthasarathy and Aggarwal (2002)

On the use of conceptual reconstruction for mining massively incomplete data sets

Using correlation structure of data representations on which data mining algorithms can directly be applied.

9

Liu et al. (2002)

Toward multidatabase mining: identifying relevant databases

Identifying the relevant databases for the particular business application.

7

Elements of the framework

There is need for clear, agreed-upon metrics and standards of practice. Basically data mining research for customer relationship management systems can be categorised into three broad areas, which constitute the elements of the designed framework. 1

Implementation (A) Data mining (B) CRM systems

2

Evaluation criteria (A) For data mining software (B) For CRM systems

3

Methods to improve data quality for data mining.


8

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Using the framework for analysing research on data mining and CRM

It is clear from the above analysis that the evaluation process for the CRM systems can be done using two broad approaches: the evaluation of the data mining tools and the evaluation of the CRM systems. The various data mining tools support the various algorithms, which are used for a particular type of business problem. A CRM programme involves complicated business and technology issues and requires significant investment of time and money, so it is required to study the effectiveness of these systems on a common ground. Typically a data mining research should focus on both before and after tool application. By revisiting the literature we can see that there are research gaps in this area also as the authors have proposed different implementation methods, but there are less or no common implementation strategies, which can facilitate the CRM deployment. There are studies on CRM systems evaluation and implementations; similarly there are studies on data mining tools evaluation. But very few authors have explored the relationship between the two. The framework for the analysis of data mining research is used to find out the research carried out in the area. Table 3 exhibits this by arranging the literature in tabular form. Table 3

Literature related to the data mining research for CRM systems Section 1

S.No.

Research

(A)

2 (B)

(A)

3 (B)

Evaluation 1

Collier et al. (1999)

2

King and Elder (1998)

√

3

Qui et al. (2004)

√

4

Labovitz (2003)

√

5

Mukhopadhyay and Nath (2001)

√

6

Chang (2004)

√

7

Thearling (1999)

8

Rigby and Ledingham (2004)

√

9

Kellen (2002)

√

10

Ceglar et al. (2003)

√

11

Lee and Siau (2001)

√

√

√

√

Implementation 12

Thearling (1999)

√

13

Dasgupta (2003)

√

14

SAS (2006)

√

√

562


Table 3

Literature related to the data mining research for CRM systems (continued) Section 1

S.No.

Research

15

2

(A)

(B)

Ansari et al. (2001)

√

√

16

Edelstein (2006)

√

√

17

Meltzer (2000)

√

18

Boland et al. (2002)

√

19

Oracle Peoplesoft (2003)

√

20

Magnini et al. (2003)

21

Bordoloi (2000)

√

(A)

3 (B)

√ √

Improving data quality 22

Cadot and Martino (2003)

√

23

McClean et al. (2001)

√

24

Ganti et al. (2001)

√

25

Bohannon et al. (2001)

√

26

Hipp et al. (2001)

√

27

Wu and Barbara (2002)

√

28

Ballou and Pazer (2003)

√

29

Parthasarathy and Aggarwal (2002)

√

30

Liu et al. (2002)

√

Table 4

Classification grid of evaluation, implementation, and data quality models/methods Implementation

Evaluation

Data Quality

1, 20

1, 2, 3, 4, 10, 11

22 ,23, 24, 25, 26, 27, 28, 29, 30

12, 13, 14, 17, 18, 19, 21

5, 6, 8, 9

–

15, 16

7

–

Data mining tools CRM systems CRM and data mining

Note:

The entries in the cell refer to the S.No. from Table 3.

8.1 Analysis of research •

The studies related to the implementation of the data mining tools and techniques, which otherwise are very important aspects of the data mining software application, are few and there are research gaps.

•

There are methods and models for evaluating data mining tools and CRM systems separately, but more research is required in the evaluation of the data mining and CRM systems together.


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•

There is almost no work done on improving the data quality by taking into consideration the CRM systems requirements. As these systems are based on the business problems and definitions, the requirement of data quality varies. Research scope in this area is immense and the need is to develop the methods or models to improve data quality, specifically taking CRM in consideration.

•

Improvement methods for the data mining and CRM systems are also needed as the matrix clearly shows research gaps in this area.

9

Conclusions and further research

The applications areas of data mining vary from defence, bio-informatics to businesses. Business people use data mining to sharpen their competitive edge, as well as to manage their customers. Data mining tools act as a backbone to the customer relationship management systems and thus it is very important to analyse the impact on CRM systems. Many researchers have tried to give the models for evaluating the effectiveness of these systems, but no specific and standard method has emerged. Also there are no agreed-upon metrics for practising CRM and findings after analysing the results of the technology implementations. It is clear from the above analysis of the literature that the implementation methods, evaluation criteria for effectiveness measure, and data quality requirements are different for different business applications. Since CRM and data mining are emerging areas, there is immense research scope. The conclusions drawn in this paper after analysing the three basic areas of CRM and data mining research can be taken as bases for further research. It can be concluded that further research is needed in this area and there is need to develop a model, which can be used to evaluate and implement the CRM systems more effectively. Though data quality plays a vital role in the application of data mining tool, the research in the area of improving data quality can be seen as a separate issue and can be treated as the statistical problem to be solved. Taking into consideration the above methods and models, we propose an integrated model for the evaluation and implementation of the CRM systems along with the data mining tools, which can be applied to a wide variety of business applications as given in Figure 2. Figure 2

Integrated model to evaluate and implement the CRM systems and the data mining tools

Business data definition

Architecture support for CRM system

Evaluation of data mining tools Evaluation of CRM systems

Implementation of data mining tools for CRM systems

Analysis of the results

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The proposed model combines the overall process into a simple integrated form, beginning with the business data definition and thereafter deciding upon the architecture support. The processes of evaluating the data mining software is combined with the evaluation of the CRM systems and are implemented together so as to save dual efforts of the implementation. The analysis is done in a similar way and feedback is used further to evaluate the system. The proposed model can be taken as a basic step towards the integration of all the processes and can be validated by further research.

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