The Role of Information Technology Infrastructure for Customer Relationship Management Implementation of Manufacturing Companies

Transcription

1 The Role of Information Technology Infrastructure for Customer Relationship Management Implementation of Manufacturing Companies Milad Farzin *, Tannaz Rostam Abadi ** * M.A, Department of Management, Islamic Azad University, Babol, Iran ** M.A, Department of Management, Islamic Azad University, Tehran, Iran Abstract Development of IT helps to improvement relationship of company with its customers by different ways. Including understanding of rapidity and development of e-commerce between companies and organization is very important. For instance, companies can communicate with their customers by providing their products in technology portals. Collecting and analyzing data about customer patterns, Customer relationship management (CRM) is a tool for managing the relations among customers and the organization. The businesses can enhance their relationships with customers through CRM and therefore it leads to greater customer loyalty, retention and profitability. Information technology and information systems have a significant role in CRM and can be used to enhance CRM process to satisfy customer needs. In this paper, at first we describe four basic CRM tasks, identification, differentiation, interaction and customization, then review the role of IT-based interactivity and IT factors of CRM, and finally we will identify the relations among CRM and central infrastructures to CRM applications, namely data warehousing technology, enterprise resource planning systems, internet and data mining. The results of the case study indicate that IT infrastructure (data warehouses, enterprise resource planning, Internet and Data mining) for CRM implementation of manufacturing companies in the automotive parts industry in the mazandaran province is effective. Keywords: Customer relationship management, information technology, data warehouses, enterprise resource planning, Internet, Data mining Introduction Interest in customer relationship management (CRM) began to grow in 1990s (Ling and Yen, 2001; Xu et al., 2002). Regardless of the size of an organization, businesses are still motivated to adopt CRM to create and manage the relationships with their customers more effectively. An enhanced relationship with one s customers can ultimately lead to greater customer loyalty and retention and, also, profitability. In addition, the rapid growth of the internet and its associated technologies has greatly increased the 159

2 opportunities for marketing and has transformed the way relationships between companies and their customers are managed (Bauer et al., 2002). Although CRM has become widely recognized as an important business approach, there is no universally accepted definition of CRM. Swift (2001, p. 12) defined CRM as an enterprise approach to understanding and influencing customer behavior through meaningful communications in order to improve customer acquisition, customer retention, customer loyalty, and customer profitability. Kincaid (2003, p. 41) viewed CRM as the strategic use of information, processes, technology, and people to manage the customer s relationship with your company (Marketing, Sales, Services, and Support) across the whole customer life cycle. Parvatiyar and Sheth (2001, p. 5) defined CRM as a comprehensive strategy and process of acquiring, retaining, and partnering with selective customers to create superior value for the company and the customer. It involves the integration of marketing, sales, customer service, and the supply-chain functions of the organization to achieve greater efficiencies and effectiveness in delivering customer value. These definitions emphasize the importance of viewing CRM as a comprehensive set of strategies for managing those relationships with customers that relate to the overall process of marketing, sales, service, and support within the organization. Moreover, information technology (IT) and information systems (IS) can be used to support and integrate the CRM process to satisfy the needs of the customer. CRM has attracted the attention of practitioners and academics. Research on CRM has increased significantly over the past few years. In paper wrote by Ngai (2005) identified 205 CRM articles published between 1992 and The results in paper have important implications. In review he found that 76 out of 205 articles were related to IT. However, IT plays an important role in the development and implementation of CRM. The three most popular topics addressed in IT and IS for CRM are software, tools, systems (DSS, ES, IS, ERP, DM, etc.), data mining, and knowledge management. Such software, tools, systems (DSS, ES, IS, and ERP, etc.) can be viewed as technology-based applications to support the CRM process. These application systems should include database capabilities to collect and analyze customer information using statistical techniques such as data mining. Data mining plays a fundamental role in the overall CRM process and is a critical component in the CRM system (Rygielski et al., 2002a). It helps transform customer data into useful information and knowledge. Customer information and knowledge is a company asset that must be managed. A deeper understanding of data mining and knowledge management in CRM is necessary in today s highly customer-centered business environment (Shaw et al., 2001). In this study, after describing key CRM tasks, the increased role of IT-based interactivity and IT factors of CRM, we will describe the technology factors such as data warehousing technology, ERP systems, internet and data mining. In the last section, seven basic steps of data mining for effective CRM are represented. 160

3 1-Key Crm Tasks "I know who you are, I remember you. I get you to talk to me. And then, because I know something about you, my competitors don't know, I can do something for you my competitors can't do - not for any price" [Newell, 2000] CRM differs from the previous method of database marketing in that the database marketing technique tried to sell more products to the customer for less cost. [Seiler and Gray 1999]. The database marketing approach is highly Company centric. However, customers were not kept loyal by the discount programs and the one-time promotions that were used in the database-marketing programs. Customer loyalty is, indeed, very difficult to obtain or buy. The CRM approach is customer-centric. This approach focuses on the long-term relationship with the customers by providing the customer benefits and values from the customer s point of view rather than based on what the company wants to sell. The basic questions that CRM tries to answer are: 1. What is the benefit of the customer? 2. How can we add the customer s value? Four basic tasks are required to achieve the basic goals of CRM. [Peppers, et al., 1999] 1-1. Customer Identification To serve or provide value to the customer, the company must know or identify the customer through marketing channels, transactions, and interactions over time Customer Differentiation Each customer has their own lifetime value from the company's point of view and each customer imposes unique demands and requirements for the company Customer Interaction Customer demands change over time. From a CRM perspective, the Customer s long-term profitability and relationship to the company is Important. Therefore, the company needs to learn about the customer continually. Keeping track of customer behavior and needs is an important task of a CRM program Customization / Personalization Treat each customer uniquely is the motto of the entire CRM process. Through the personalization process, the company can increase customer Loyalty. Jeff Bezos, the CEO of Amazon.com, said, Our vision is that if we have 20 million customers, then we should have 20 million stores. [Wheatley, 2000] The automation of personalization is being made feasible by information technologies. IT play a key role in the development of CRM (Kincaid, 2003; Ling and Yen, 2001).According to Kincaid (2003), West (2001) and Xu et al. (2002), CRM comprises three major functional areas: 161

4 (1) Marketing; (2) Sales; and (3) Services and Support. These three components may be seen as the life cycle of a customer relationship that moves from marketing, to sales, to service and support (West, 2001). Indeed, IT are the other crucial components in supporting and maintaining these three functional areas as well as the whole CRM process (Kincaid, 2003).Thus, the classification framework proposed is based on these four areas (Marketing, Sales, Services and Support, and IT and IS). They can be used to automate and enable some or all CRM processes. Appropriate CRM strategies can be adopted through the assistance of technology, which can manage the data required to understand customers. Moreover, the use of IT can enable the collection of the necessary data to determine the economics of customer acquisition, retention, and life-time value. Advanced technology involves the use of databases, data warehouses, and data mining to help organizations increase customer retention rates and their own profitability. 2-Increased role of IT-based interactivity The research from the CMP group has identified a number of factors that are causing the change in the nature of marketing practice (Brodie et al, 2000): the increasing emphasis on services and service aspects of products; the focus on financial accountability, loyalty and value management; the transformation of organizations; the shifts in power and control within marketing systems; and the increased role of IT-based interactivity. These changes are highly interrelated, but IT is an underlying force behind many of them because it changes the nature of products, services, structures, functions, processes and communications (Brookes et al., 2000). Since the 1980s, IT has moved to the front end in almost all industries (Cecil and Hall, 1988) and now links businesses and their suppliers, distributors, resellers, and customers into seamless networks of relationships and interactions throughout an industry s entire value system (Figure 1). Figure 1: IT links businesses and their suppliers, distributors, reseller and customers into networks of relationships and interactions 162

5 For example, mass customization of goods and services, which is often achieved through the direct collaboration between the manufacturer and customer, has been facilitated by developments in IT (Brown, 2001; Snehota and So derlund, 1998; So derlund and Johansson, 1997; Toffler, 1980). One in particular, Dell computers, has almost completely eliminated the need of carrying inventories, and is now manufacturing products that are Made according to customer specifications sent from the customers to Dell via s. Dell also has developed strong relationships with its suppliers and depends on them for components (Andrews, 2000; Dell and Fredman, 1999). The CRM approach to marketing has gained much currency in recent years, seeking to establish closer relationships and interactions between a business and its most important customers (Barnes, 2001; Brown, 2000; Foss and Stone, 2001; Greenberg, 2001; McKenzie, 2001). CRM-oriented businesses market their products and services through relationships and interactions with multiple markets, most notably the customer market, often taking advantage of IT-based interactivity (Ryals and Payne, 2001). This is why relationship marketing is termed customer relationship management when it emphasizes the customer market in particular. CRM frequently employs IT technology as a means to attract, develop and retain customers. For example, the application of IT technology allows for IT-based interactivity that makes it possible for customers to have access to product and service information much faster than earlier. It also permits businesses to leverage information from their customer databases to achieve customer retention, and to cross-sell new products and services to existing customers (Falque, 2000; Foss and Stone, 2001; Ghodeswar, 2000; Natarajan and Shekar, 2000). It must be emphasized, though, that CRM does not necessarily involve IT technology. The following is the widely accepted definition of this: Customer Relationship Management is a comprehensive strategy and process of acquiring, retaining and partnering with selective customers to create superior value for the company and the customer (Parvatiyar and Sheth, 2000, p. 6). 3 - It Factors of Crm Traditional (mass) marketing doesn t need to use information technologies extensively because there is no need to distinguish, differentiate, interact with, and customize for individual customer needs. Although some argue that IT has a small role in CRM, [Computing, 2000] each of the four key CRM tasks depends heavily on information technologies and systems. Table 4 shows this relationship for the marketing processes, for the goals, for traditional mass marketing, for CRM, and for the information technologies used in CRM. 163

6 CRM technology applications link front office (e.g. sales, marketing and Customer service) and back office (e.g. financial, operations, logistics and human resources) functions with the company s customer touch points (Fickel, 1999). A company s touch points can include the Internet, , sales, direct mail, telemarketing operations, call centers, advertising, fax, pagers, stores, and kiosks. Often, these touch points are controlled by separate information systems. CRM integrates touch points around a common view of the customer (Eckerson and Watson, 2000). Figure 2 demonstrates the relationship between customer touch points with front and back office operations Figure2: The relationship between customer touch points with front and back office operations 164

7 4. The technology factor Information technology (IT) has long been recognized as an enabler to radically redesign business processes in order to achieve dramatic improvements in organizational performance (Davenport and Short, 1990; Porter, 1987). IT assists with the re-design of a business process by facilitating changes to work practices and establishing innovative methods to link a company with customers, suppliers and internal stakeholders (Hammer and Champy, 1993). CRM applications take full advantage of technology innovations with their ability to collect and analyze data on customer patterns, interpret customer behavior, develop predictive models, respond with timely and effective customized communications, and deliver product and service value to individual customers. Using technology to optimize interactions with customers, companies can create a 360 degree view of customers to learn from past interactions to optimize future ones (Eckerson and Watson, 2000). Innovations in network infrastructure, client/server computing, and business intelligence applications are leading factors in CRM development. CRM solutions deliver repositories of customer data at a fraction of the cost of older network technologies. CRM systems accumulate, store, maintain, and distribute customer knowledge throughout the organization. The effective management of information has a crucial role to play in CRM. Information is critical for product tailoring, service innovation, consolidated views of customers, and calculating customer lifetime value (Peppard, 2000). Among others, data warehouses, enterprise resource planning (ERP) systems, Internet and Data mining are central infrastructures to CRM applications. 4.1 Data warehouse technology A data warehouse is an information technology management tool that gives business decision makers instant access to information by collecting islands of customer data throughout the organization by combining all database and operational systems such as human resources, sales and transaction processing systems, financials, inventory, purchasing, and marketing systems. Specifically, data warehouses extract, clean, transform, and manage large volumes of data from multiple, heterogeneous systems, creating a historical record of all customer interactions (Eckerson and Watson, 2000). The abilities to view and manipulate set data warehouses apart from other computer systems. Constantly extracting knowledge about customers reduces the need for traditional marketing research tools such as customer surveys and focus groups. Thus, it is possible to identify and report by product or service, geographic region, distribution channel, customer group, and individual customer (Story, 1998). Information is then available to all customer contact points in the organization. Data warehousing technology makes CRM possible because it consolidates correlates and transforms customer data into customer intelligence that can used to form a better understanding of customer 165

8 behavior. Customer data includes all sales, promotions, and customer service activities (Shepard et al., 1998). In addition to transaction details, many other types of data generated from internal operations can make significant contributions. Information related to billing and account status, customer service interactions, back orders, product shipment, product returns, claims history, and internal operating costs all can improve understanding of customers and their purchasing patterns. The ability of a data warehouse to store hundreds and thousands of gigabytes of data make drill-down analysis feasible as well as immediate. Brief outlines of organizational benefits with a data warehouse are: Accurate and faster access to information to facilitate responses to Customer questions; Data quality and filtering to eliminate bad and duplicate data; Extract, manipulate and drill-down data quickly for profitability analysis, customer profiling, and retention modeling; Advanced data consolidation and data analysis tools for higher level summary as well as detailed reports; Calculate total present value and estimate future value of each and every customer. 4.2 Enterprise resource planning (ERP) systems Enterprise resource planning (ERP), when successfully implemented, links all areas of a company including order management, manufacturing, human resources, financial systems and distribution with external suppliers and customers into a tightly integrated system with shared data and visibility (Chen, 2001). An overview of ERP systems is provided in Figure 3. Figure3: An overview of ERP systems Source: Adapted from Chen,

9 Major enterprise systems vendors, who have been successful in the ERP market, are gearing up for the growing needs of CRM by aggressively forming alliances with, or taking over other software companies that have been operating in the CRM market. Significant differences exist between ERP technology and CRM Applications. ERP serves as a strong foundation with tightly integrated back office functions while CRM strives to link front and back office applications to maintain relationships and build customer loyalty. ERP systems promise to integrate all functional areas of the business with suppliers and customers. CRM promises to improve front office applications and customer touch points to optimize customer satisfaction and profitability. While ERP systems address fragmented information systems, CRM addresses fragmented customer data. CRM applications are Web-enabled and designed to extend the data mining capabilities of ERP throughout the supply chain to customers, distributors, and manufacturers (Scannell, 1999). Organizations can use CRM analytical capabilities to predict and answer key business questions on customer intelligence and share the results across channels. Although ERP is not required for CRM, providing customers, suppliers, and employees with Web-based access to systems through CRM will only be beneficial if the underlying infrastructure, such as data warehouses and/or ERP, exists (Solomon, 2000). Companies with an ERP system, however, need to understand where they are in the implementation process, as well as assess where other technologies, such as data warehouses, fit in before plunging into CRM applications (Saunders, 1999). 4.3 Impact of the Internet The explosive growth of the Internet has also brought new meaning to building customer relationships. Greater customer access to the organization, such as online ordering and around the clock operations, has set the stage for a shifting paradigm in customer service. A recent report describes how successful Web sites are in building lasting relationships with e-customers by offering services in traditionally impossible ways (Peppers and Rogers, 2000). Using a series of richly detailed case studies, they also contended that in the broad arena of business-to-business commerce, organizations would rise or fall on the basis of their capabilities to cultivate one-to-one relationships with their customers (Peppers and Rogers, 2001). Customers expect organizations to anticipate their needs and provide consistent service at levels above their expectations. In return, customers are loyal to the organization for longer periods of time. For instance, the American Airlines Web site builds customized customer views in real time allowing two million frequent fliers to have a unique experience each time they log on (Peppers and Rogers, 1999). Prior to the Internet, there was not a cost-effective way to tell millions of Customers fitting a certain profile about an immediately available special fare. 167

10 With the interactive capability of the Internet, American Airlines can do exactly that without having to tell everyone about every special fare. As a part of CRM, American Airlines offers loyal customers promotional fares and special discounts to partner businesses based on individual customer Preferences. 4-4.Data mining The first and simplest analytical step in data mining is to describe the data for example, summarize its statistical attributes (such as means and standard deviations), visually review it using charts and graphs, and look at the distribution of values of the fields in your data. But data description alone cannot provide an action plan. You must build a predictive model based on patterns determined from known results, then test that model on results outside the original sample. A good model should never be confused with reality (you know a road map isn t a perfect representation of the actual road), but it can be a useful guide to understanding your business. Data mining can be used for both classification and regression problems. In classification problems you re predicting what category something will fall into for example, whether a person will be a good credit risk or not, or which of several offers someone is most likely to accept. In regression problems you re predicting a number such as the probability that a person will respond to an offer. In CRM, data mining is frequently used to assign a score to a particular customer or prospect indicating the likelihood that the individual will behave in the way you want. For example, a score could measure the propensity to respond to a particular offer or to switch to a competitor s product. It is also frequently used to identify a set of characteristics (called a profile) that segments customers into groups with similar behaviors, such as buying a particular product. A special type of classification can recommend items based on similar interests held by groups of customers. This is sometimes called collaborative filtering. The data mining technology used for solving classification, regression and collaborative filtering problems Applying Data Mining to CRM In order to build good models for your CRM system, there are a number of steps you must follow. The Two Crows data mining process model described below is similar to other process models such as the CRISP-DM model, differing mostly in the emphasis it places on the different steps. Keep in mind that while the steps appear in a list, the data mining process is not linear. You will inevitably need to loop back to previous steps. For example, what in the explore data step may require adding new data to the data mining database? The initial models you build may provide insights that lead to create new variables. The basic steps of data mining for effective CRM are: 1. Define business problem 2. Build marketing database 168

11 3. Explore data 4. Prepare data for modeling 5. Build model 6. Evaluate model 7. Deploy model and results Let s go through these steps to better understand the process Define the business problem Each CRM application will have one or more business objectives for which you will need to build the appropriate model. Depending on your specific goal, such as increasing the response rate or increasing the value of a response, you will build a very different model. An effective statement of the problem will include a way of measuring the results of your CRM project Build a marketing database Steps two through four constitute the core of the data preparation. Together, they take more time and effort than all the other steps combined. There may be repeated iterations of the data preparation and model building steps as you learn something from the model that suggests you modify the data. These data preparation steps may take anywhere from 50% to 90% of the time and effort of the entire data mining process! You will need to build a marketing database because your operational databases and corporate data warehouse will often not contain the data you need in the form you need it. Furthermore, your CRM applications may interfere with the speedy and effective execution of these systems. When you build your marketing database you will need to clean it up if you want good models you need to have clean data. The data you need may reside in multiple databases such as the customer database, product database, and transaction databases. This means you will need to integrate and consolidate the data into a single marketing database and reconcile differences in data values from the various sources. Improperly reconciled data is a major source of quality problems. There are often large differences in the way data is defined and used in different databases. Some inconsistencies may be easy to uncover, such as different addresses for the same customer. Making it more difficult to resolve these problems is that they are often subtle. For example, the same customer may have different names or worse multiple customer identification numbers. 169

12 Explore the data Before you can build good predictive models, you must understand your data. Start by gathering a variety of numerical summaries (including descriptive statistics such as averages, standard deviations and so forth) and looking at the distribution of the data. You may want to produce cross tabulations (pivot tables) for multi-dimensional data. Graphing and visualization tools are a vital aid in data preparation, and their importance to effective data analysis cannot be overemphasized. Some of the common and very useful graphical displays of data are histograms or box plots that display distributions of values. You may also want to look at scatter plots in two or three dimensions of different pairs of variables. The ability to add a third, overlay variable greatly increases the usefulness of some types of graphs Prepare data for modeling This is the final data preparation step before building models First you want to select the variables on which to build the model. Ideally, you would take all the variables you have, feed them to the data mining tool and let it find those which are the best predictors. In practice, this doesn t work very well. One reason is that the time it takes to build a model increases with the number of variables. Another reason is that blindly including extraneous columns can lead to models with less rather than more predictive power. The next step is to construct new predictors derived from the raw data. For example, forecasting credit risk using a debt-to-income ratio rather than just debt and income as predictor variables may yield more accurate results that are also easier to understand. Next you may decide to select a subset or sample of your data on which to build models. If you have a lot of data, however, using all your data may take too long or require buying a bigger computer than you would like. Working with a properly selected random sample usually results in no loss of information for most CRM problems. Given a choice of either investigating a few models built on all the data or investigating more models built on a sample, the latter approach will usually help you develop a more accurate and robust model. of the problem. Last, you will need to transform variables in accordance with the requirements of the algorithm you choose for building your model Data mining model building The most important thing to remember about model building is that it is an iterative process. You will need to explore alternative models to find the one that is most useful in solving your business problem. What you learn in searching for a good model may lead you to go back and make some changes to the data you are using or even modify your problem statement. 170

13 Most CRM applications are based on a protocol called supervised learning. You start with customer information for which the desired outcome is already known. For example, you may have historical data because you previously mailed to a list very similar to the one you are using. Or you may have to conduct a test mailing to determine how people will respond to an offer. You then split this data into two groups. On the first group you train or estimate your model. You then test it on the remainder of the data. A model is built when the cycle of training and testing is completed Evaluate your results Perhaps the most overrated metric for evaluating your results is accuracy. Suppose you have an offer to which only 1% of the people will respond. A model that predicts nobody will respond is 99% accurate and 100% useless. Another measure that is frequently used is lift. Lift measures the improvement achieved by a predictive model. However, lift does not take into account cost and revenue, so it is often preferable to look at profit or ROI. Depending on whether you choose to maximize lift, profit, or ROI, you will choose a different percentage of your mailing list to which you will send solicitations Incorporating data mining in your CRM solution In building a CRM application, data mining is often only a small, albeit critical, part of the final product. For example, predictive patterns through data mining may be combined with the knowledge of domain experts and incorporated in a large application used by many different kinds of people. The way data mining is actually built into the application is determined by the nature of the customer interaction. There are two main ways you interact with your customers: they contact you (inbound) or you contact them (outbound). The deployment requirements are quite different. Outbound interactions are characterized by your company originating the contact such as in a direct mail campaign. Thus you will be selecting the people to whom you mail by applying the model to your customer database. Another type of outbound campaign is an advertising campaign. In this case you would match the profiles of good prospects shown by your model to the profile of the people your advertisement would reach. For inbound transactions, such as a telephone order, an Internet order, or a customer service call, the application must respond in real time. Therefore the data mining model is embedded in the application and actively recommends an action. In either case, one of the key issues you must deal with in applying a model to new data is the transformations you used in building the model. Thus if the input data (whether from a transaction or a database) contains age, income, and gender fields, but the model requires the age-to-income ratio and gender has been changed into two binary variables, you must transform your input data accordingly. The ease with which you can embed these Transformations becomes one of the most important productivity factors when you want to rapidly deploy many models. 171

14 5. Case Study: manufacturing companies in the automotive parts industry According to the above study is a descriptive survey research because we are trying to find a correlation between variables as well. The study populations of interest, marketing managers of manufacturing companies, auto parts industry in the mazandaran province, are conducted survey, numbers about 98 people. Sample Table Sample members (Krejci & Morgan, 1970) have been selected. Thus the sample is 78. Data from two international questionnaires based on Likert 5 scale option is used, which consists of two parts. Due to the limited statistics of simple random sampling method was used Research hypotheses The main hypothesis IT infrastructure for CRM implementation of manufacturing companies in the automotive parts industry in the mazandaran province is effective Secondary hypothesis 1. Data warehouses infrastructure for CRM implementation of manufacturing companies in the automotive parts industry in the mazandaran province is effective. 2. Enterprise resource planning (ERP) infrastructure for CRM implementation of manufacturing companies in the automotive parts industry in the mazandaran province is effective. 3. Internet infrastructure for CRM implementation of manufacturing companies in the automotive parts industry in the mazandaran province is effective. 4. Data mining infrastructure for CRM implementation of manufacturing companies in the automotive parts industry in the mazandaran province is effective. 6. Results Data analysis was performed using SPSS software. Before testing the hypotheses to evaluate the normality of the distribution of Kolmogorov-Smirnov claims (KS) was used. Claim this test, normal distribution, and the conflicting claims of the distribution is not normal, is as follows: 172

15 Table 2 - Results of the Kolmogorov - Smirnov Data warehouses Enterprise resource Planning Internet Data mining IT N Normal Parameters Mean Std. Deviation Most Extreme Differences Absolute Positive Negative Kolmogorov-Smirnov Z Asymp. Sig. (2-tailed) The equipment to the table, as calculated for all variables sig IT infrastructure and customer relationship management to more than 5%, H0 is verified or not rejected. Thus, the normal distribution and related claims will be accepted. Therefore, the T- test was used to test the hypotheses is as follows. Table 3 - One-Sample Test results to IT infrastructure Test Value = 3 t Df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper Data warehouses Enterprise resource planning Internet Data mining IT

16 Given the output of the software SPSS, listed in Table 3, since a significant amount (sig) is smaller than 5%. Suppose H0 is rejected and H1 is accepted. In other words, IT infrastructure (data warehouses, enterprise resource planning, Internet and Data mining) for CRM implementation of manufacturing companies in the automotive parts industry in the mazandaran province is effective. 7. Conclusion IT plays a key role in the development of CRM (Kincaid, 2003; Ling and Yen, 2001). They can be used to automate and enable some or all CRM processes. Appropriate CRM strategies can be adopted through the assistance of technology, which can manage the data required to understand customers. Moreover, the use of IT can enable the collection of the necessary data to determine the economics of customer acquisition, retention, and life-time value. The results of the case study indicate that IT infrastructure (data warehouses, enterprise resource planning, Internet and Data mining) for CRM implementation of manufacturing companies in the automotive parts industry in the mazandaran province is effective. Development of IT helps to improvement relationship of company with its customers by different ways. Including understanding of rapidity and development of e-commerce between companies and organization is very important. For instance, companies can communicate with their customers by providing their products in technology portals. Collecting and analyzing data about customer patterns, customer behavior interpretation, delivery of products and services to special customers and creation and development of service-level increase models are other results that can be acquired by innovation and creativity in IT area and CRM utilization. Implement of CRM, however, requires huge investment in IT but it is expected that lead to profitable output as result. Support to expand marketing communications toward customers depend on how marketing is capable to be done based IT. Companies can produce products according needs and expectation of customers by using information technology and storage of customer information and also by advanced analyzing of this information. (bahrami et al, 2012) Advanced technology involves the use of data warehouses, enterprise resource planning, Internet and Data mining to help organizations increase customer retention rates and their own profitability. Reference -Andrews, F. (2000), Dell, it turns out has a better idea than Ford, The New York Times, 26 January, p. C12. -Bahrami Mahdi, Ghorbani Mazaher, S. Arabzad, Mohammad (2012), Information Technology (IT) as An Improvement Tool For Customer Relationship Management (CRM), Social and Behavioral Sciences 41 ( 2012 ) Barnes, J.G. (2001), Secrets of Customer Relationship Management: It s All About How You Make Them Feel, McGraw-Hill, New York, NY. - Bauer, H.H., Grether, M. and Leach, M. (2002), Building customer relations over the internet, 174

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