On the design concepts for CRM system

(1)

On the design concepts for CRM system

Jeong Yong Ahn

Department of Computer Science and Informatics, Seonan University,

Namwon, Korea

Seok Ki Kim

Division of Mathematics and Statistical Informatics,

Chonbuk National University, Chonju, Korea

Kyung Soo Han

Division of Mathematics and Statistical Informatics,

Chonbuk National University, Chonju, Korea

1. Introduction

The World Wide Web (WWW, Web) not only contains a vast amount of useful

information but also provides a powerful infrastructure for communication and information sharing. Many activities including education and research are getting on the Web and many organizations rely on the Web for competitive advantage (Jemmeson, 1997). Nowadays, the Web is continuing to expand at an amazing rate as a medium for conducting business in addition to disseminating information. Cheung (1998) noted that the Web is used for many diversified business purposes

including direct sales, advertisement, customer support, etc. and the astonishing growth of Web users is clear evidence of this. Pitkow and Kehoe (1996) mentioned that the number of Web users is increasing remarkably, too.

With the growth of Web users, the Web is producing large volumes of data (Buchner et al., 1999) and data analysis to extract information from the data is considered as a significant topic. Recently, techniques such as data warehousing, data mining, and online analytical processing (OLAP) are steadily being studied, and Web data mining to extract information from Web data has become a popular research area (Cooley, 2000; Peiet al., 2000).

Advances in information technology are changing the research surrounding the business and marketing fields, and a considerable number of studies ± for example, Peters and Saidin (2000), Stone and Good (2001) ± have been conducted on the use of information technology. A focusing topic in the fields is electronic customer

relationship management (CRM). Bose

(2002) noted, CRM involves acquisition, analysis and use of knowledge about customers in order to sell more goods or services and to do it more efficiently. In other words, CRM is a process designed to grasp features of customers and apply those features to marketing activities. It differs from classical marketing in the point that it uses ``customer centric thinking'' in

marketing. The primary reasons for the emergence of CRM are the changes in the marketing environment and advances in Web technology.

In this study, we provide a review of CRM and Web data sources used in online marketing. Additionally, we propose some design concepts for the development of an effective CRM system.

2. Changes in the marketing

environment

Recently, information technology has stimulated several innovations in the business and marketing fields. Burkeet al. (1999) discussed the effect of information technology in management and marketing fields, and they emphasize the

re-arrangement of a new marketing paradigm that takes advantage of Web technology.

Before 1990, the main concern of many companies was focused on performing business transactions with customers, and the companies had strategies of sales promotion to address those basic concerns effectively. After 1990, however, many companies began focusing their concerns on the aspect of how to maintain positive relationships with customers, how to raise customers' loyalty, and how to enlarge customer life value (Wayland and Cole, 1997).

The Emerald Research Register for this journal is available at

http://www.emeraldinsight.com/researchregister

The current issue and full text archive of this journal is available at

http://www.emeraldinsight.com/0263-5577.htm

Industrial Management & Data Systems 103/5 [2003] 324-331 #MCB UP Limited [ISSN 0263-5577] [DOI 10.1108/02635570310477370] Keywords Customers, Relationship marketing, Management, Design, Information technology Abstract

In the past few years, information technology has stimulated several innovations in the business and marketingfields, and advances in the technology are changing the research surroundingthose fields. Recently, focusingtopics in the management and marketing field are electronic customer relationship management (CRM) and the practical use of marketing data and information technology. The goal of this article is not to provide an all-inclusive tutorial on CRM but rather to provide fundamental concepts behind CRM and some aspects of the system development process. This article provides a comprehensive review of CRM and marketingdata sources, and consider some design concepts for creating an effective CRM system from the viewpoint of practical use of the data sources.

(2)

Thus, recent strategies of companies are changing toward customer-oriented

strategies. In particular, grasping the needs of customers and offering added services are recognized as factors that decide success or failure of companies.

Because companies are changing to customer-oriented strategies, marketing environments are changing repeatedly. Mass marketing of the past is now changing into one-to-one marketing via target marketing, niche marketing, database marketing, and Internet marketing. In this section, we briefly examine several marketing techniques:

. _{Mass marketing}_{. Mass marketing presents} sales promotion materials to an unspecific general public, and was used in the mass production and large quantity sale marketing environment in the past. In this environment, mass media such as newspapers, magazines, television, and radio were mainly used for the marketing. Of course, this marketing method is used frequently these days. For example, direct bulk mailing is a common mass

marketing tool. However, problems about the efficiency of mass marketing have been raised recently. The reason is that customers do not pay attention to mass marketing and require better individual services.

. _{Target marketing}_{. Target marketing} presents marketing strategies to specific customer groups to solve the problems of mass marketing and to raise marketing efficiency.

. _{One-to-one marketing}_{. One-to-one} marketing forms an individual

relationship with customers or potentail customers, and presents marketing strategies. This technique is similar to database marketing and different from mass marketing in that it is a

customer-oriented style. Mass marketing is a product-oriented style. In other words, one-to-one marketing can be explained as a strategy to sell several products to one customer, while mass marketing is to sell one product to several customers.

The most important functions of

customer-oriented marketing are to collect and accumulate information about

customers and to provide services to customers. It is very difficult to get information if we do not take advantage of information technology, and the rapid advances of technologies such as database, data warehousing, and data mining are playing a central part in the change of marketing paradigms.

3. CRM

CRM is a process designed to collect data related to customers, to grasp features of customers, and to apply those qualities in specific marketing activities (Swift, 2001). CRM is not a new concept. In fact, CRM has continuously existed from the past. However, CRM has recently become the focus of attention. The backgrounds are as follows (Ahn, 2001):

. _{the relationship with customers is newly} recognized as a key point to solidify competitive power of a company; . _{as companies procure large volumes of}

data related to customers, they can perform customer management more easily and efficiently using data warehousing, data mining, and other information technologies; and

. _{the Web has opened up a new medium for} business and marketing, and we can express customer actions in online into data. In other words, the scope of data to analyze behaviors of customers is extended, and the environment for one-to-one marketing have been enhanced. Figures 1 and 2 are Web pages that appear when customers visit Amazon, an Internet book sale company. Figure 1 is a picture displayed to potential customers (in fact, general Web users rather than customers) who visit for the first time, and Figure 2 is a picture displayed to customers who often visit the site. We can see the difference between the two parts marked by circles in the figures. Figure 2 shows one-to-one marketing through personalized service, i.e. CRM is being performed. In Figure 2, when a customer visits the site, the site recommends books that the customer may be interested in, and provides new information. This

marketing method keeps a continuous relationship with customers. As a result, the difference between Figure 1 and Figure 2 is very sharp from the viewpoint of marketing.

To provide these services efficiently, processes that collect and analyze the data related to customers are required, and Web data mining, a technology for discovering knowledge in Web data, has become a new emerging research area.

4. Web data sources

In the Web, various forms of data are acquired. Many researchers classify the data according to special qualities. For example, the data can be classified into server level, client level and proxy level data according to the collected location (Srivastavaet al., 2000).

JeongYongAhn, Seok Ki Kim and KyungSoo Han

On the design concepts for CRM system

Industrial Management & Data Systems

(3)

Web data from the server level are called server logs. Web data from the client level are known as user's activity information, which is created in the client browser. Web data from the proxy level are information about user's activity, which exists in the proxy server.

Another method to classify Web data has been presented in Spiliopoulou (2000). In the article, the data are classified into obtrusive data (for example, server log) and non-obtrusive data (for example, user registration or profiles). Figure 1

Displayed picture to general Web user

Figure 2

Displayed picture to a customer

(4)

In this study, we classify Web data from the viewpoint of marketing as follows:

. _{Log data}_{. Log data are information} extracted from Web server systems. The data contain information about the items accessed and referred by visitors. Log data have different properties from data used in the past, and thus new techniques are required to analyze the data. The features of the data are discussed in section 4.1. . _{User data}_{. User data are information}

collected from customers, and they can not be obtained from log data. The data include several forms of information ± for example, sex, age of customers, e-mail address, the path approaching to Web site, and so on. User data can give us the ability to collect more detailed and specific information. We can extract information such as the potential value of customers and customer segmentation.

. _{Market data}_{. Market data include} information about products that are produced in company and information about electronic commerce. The data are used for setting up plans such as goods sale strategy and customer management, and we can use the data for cross-selling analysis, market basket analysis and customer value analysis.

We can extract traffic information and the visiting patterns of Web pages through log data, can grasp the information for customer segmentation and purchase patterns, and can also manage efficient customers using user data and market data.

4.1 Features of log data

Information about Web users is gathered automatically by the Web server, and managed in the form of a ``Web log''. Web log data follow generally the common log format or extended log format as shown below: . Common log format:

165.194.11.110±[11/Jul/2000:21:06:32+0900] ``GET/defaultms.asp HTTP/1.1'' 200 18514 165.194.11.110±[11/Jul/2000:21:06:32+0900] ``GET/image/stat.jpg HTTP/1.1'' 200 7376

. _{Extended log format}_:

01:30:23 210.117.171.66 GET /jyahn/ sampling/Default.htm 200 01:31:35 210.182.144.220 GET /Emp/

Guide/t042.html 200

Web log data include information such as the client IP (Internet protocol) address, access time, request method, the URL of the page accessed and so on. Web log data have the following features:

. _{Particular formality to collect the data is}

not necessary. Web log data are gathered automatically in the Web server, and visitors do not usually recognize the occurrence of the data. Thus we need not request anything of the visitors in order to collect the data.

. _{Web log data include a lot of unnecessary}

information in analysis. Web log data include a lot of redundant data for the analysis tasks. Therefore, the process of data cleaning to remove the redundant or irrelevant data is required.

. _{Need additional information for analysis}_. To use Web log data effectively, we need some additional information such as WHOIS information, Web administrator, Web search engine and so on.

. Usually,Web log data exist in large

volumes. Web log data are usually large volumes of data, because the data record accesses information of all visitors on the Web server. Thus, techniques to handle the large volumes of data are required. These features of Web log data clearly show the differences in the (statistical) data used in the past. Unlike experimental data, most data that appear in the real world do not always have general features of statistical data (for example, low dimensional, homogeneity, and so on), and do not have the form that can be directly used for analysis. Therefore, new methods are required to analyze such data, and preprocessing is an indispensable task.

4.2 Web data mining

Web data mining can be defined as the extraction and analysis of useful information and patterns from the Web data (Cooley, 2000; Cooleyet al.., 1997). Web data mining extracts useful information from Web data, such as server access logs, user profiles, and user transactions. In this section, various techniques related to Web data mining are examined according to subjects such as information discovery for construction of efficient Web sites, customer segmentation for service, pattern discovery of customer behavior, and so on.

Web site efficiency is very important from the company standpoint. With construction of an efficient Web site, a business can make the number of visitors increase and minimize traffic.

Customer segmentation is an analytic technique required for personalized services. Clustering and classification are common techniques for customer segmentation in the business world (Groth, 2000). For the

segmentation, we can use a number of variables such as sex and age, as well as the

(5)

visiting order and the visiting time of Web pages.

The representative techniques to extract patterns of customer behavior are

association rules and sequential patterns. Discovering association rules is a method to define relationships among the Web pages (Agrawal and Srikant, 1994; Mobasheret al., 1996). Discovering sequential patterns is a method to extract some visiting patterns from data.

With these techniques, businesses can provide customers with services such as individual Web page service, individual proposals in commercial transaction, and managers with services such as customer information, purchase patterns, hit information, and so on. In most research mentioned above, however, the target that we want to analyze is the log data. For efficient CRM, businesses should integrate and analyze user data and market data as well as log data.

4.3 Data preprocessing

To analyze Web data, several processes such as data cleaning, transaction identification and data integration are required. First, collected data must undergo the process of data cleaning. Web log data include several elements such as images, sound and video files inserted on Web pages as well as information for the Web pages. Thus, in order to analyze log data, the process of data cleaning is essential.

Second, cleaned log data must be partitioned into logical clusters that represent a single user transaction. One of the main tasks that should be conducted in this process is transaction identification. The information from transaction identification is used in identifying sequential patterns, association rules and clustering (Ahn, 2002).

Third, in order to use the cleaned data effectively, we must integrate the data and some additional information. Here we need additional information such as WHOIS and the structure of Web site. WHOIS

information is the assignment information of IP address supplied in the Internet

information center of every country.

Finally, we integrate all data (log data, user data and market data), transform the data into proper form for data analysis, and carry out data analysis.

5. Some design concepts of

CRM system

In general, implementation of CRM system is begun with collecting various data related to

customers and constructing a data warehouse. A data warehouse is a large physical database that holds a vast amount of information from a wide variety of sources (Maet al., 2000). Lee and Hong (2002) indicated that one-to-one and relational marketing concepts can hardly be implemented without using a data warehouse containing various customer data. We think that constructing a data warehouse is not a step simply to store data but a preliminary step to use data. Therefore, the following questions should be considered: . _{What is the purpose of data analysis?} . _{What data must we prepare?}

. What form must we use the data in? These concepts are very important in designing the system to analyze the data stored in databases (for example, CRM, OLAP system). In general applications for data analysis, the data are inputted in various forms and then analyzed. For example, the data can be inputted directly from users, and users can use the data stored in a file. Recently, analysis of the data stored in databases has been available. However, to use the data stored in a database in the applications, users must definitely know the structure of the tables in the database. In other words, it is very difficult to analyze the data if we do not know the structure of the tables in the database. Because the data in a database generally consists of many tables, we must join the tables to analyze the data. Bose (2002) outlines a CRM development plan based on the typical life-cycle approach. In the article, the outlines are focused on the areas that are unique to or require special attention for CRM. In this section, we propose some focusing concepts and

functions in the viewpoint of practical use of the data sources:

1 Data collection. One important issue in CRM is how to express the customer's online behaviors into data. However, many researches at present tend to treat this problem indifferently. What is the information that we want to extract from data? To efficiently collect the data, we must first consider this question.

In several CRM systems, Web data such as log data and customer profiles are mainly used. We can easily get these data on the Web server side. But client-side data, in addition to server-side data, are important and valuable information. For example, we often use a JAVA applet (JAVA is a programming language) to construct Web pages. When a customer runs the applet, the information happens on the client-side, and we can use the

(6)

information to collect data for CRM. These concepts to express all behaviors of online customers into data are key points of data collection.

2 Data preprocessing. Data preprocessing consists of all the actions taken before the actual data analysis process starts. As mentioned in section 4.3, data

preprocessing includes several processes, such as data cleaning, transaction identification and data integration. It is a time-consuming task, which in many cases is semi-automatic. The growing amount of data produced by modern process monitoring and data acquisition systems has resulted in correspondingly large data processing requirements, and efficient techniques for automatic data preprocessing are important (Familiet al., 1997).

3 Data mining/analysis. Lee and Siau (2001) noted that successful application of data mining techniques can be an enormous payoff for the organizations. A purpose of data mining/analysis in CRM is to extract the information necessary to provide efficient service to customers (Cooley et al., 1997). To achieve the goal, various techniques are used at present. The techniques are classified as follows: techniques for pattern discovery (association rules, sequential patterns); clustering (K-means algorithm, Kohonen network); classification (decision trees, nearest neighbor, multi-layer perceptron), evaluation of customer value (RFM, ROI), and so on.

Usually, Web data such as log data are large volumes of data. Log data of more than hundreds of giga-bytes are

accumulated everyday in cases of a big Web site, although there are many differences according to each Web site. We must consider the property for the first time when we analyze the data. The methods for the analysis of large volumes of data are as follows:

. _{statistical viewpoint: the method that} uses sampling;

. _{computing viewpoint: the method that} uses parallel (or distributed)

computing; and

. database viewpoint: the method that uses the summary tables.

4 User interface and customizing. The design of user interface is one of the most important factors of advanced CRM systems. The most widely preached and important user interface design principle is to understand who the users are and what they want to do (Cooley, 2000). In CRM system, users are both customer and

marketer. Marketers need the analysis information of customer behavior and customers require good service.

Therefore, CRM system must be designed to support efficiently the following factors: . _{data analysis results;}

. _{campaign management;} . _{real-time decision support; and} . integrating data mining and campaign

management.

Another factor of user interface design is customizing problems. The systems to analyze the data stored in databases are fundamentally different from general applications for data analysis. The reason is that not only the used data but also the environment of data warehouse are different according to companies. 5 Knowledge base. The main concern in

CRM system is to understand and make practical use of customer information. How information is stored, augmented and organized will determine how effective any organization's customer service efforts will be. The core of the knowledge base in CRM system consists of individual information items, and CRM customer service has demanding requirements for a knowledge base. Dynamic knowledge bases, properly designed and implemented, can remove many of the administrative demands and present better information to customers at an even lower cost (Warner, 2000). 6 Personalized service. In today's

competitive business environment, providing value to the customer is an important matter for businesses to survive. The way to provide value is to know the customers and serve them as individuals (Kobsaet al., 2001). Today, every Amazon customer gets personal recommendations for books due to Amazon's personalization technology (see Figure 2). The addition of thousands of customers to Amazon's base has a minimal effect on their cost to sustain this high level of service. This scalable personalization is unprecedented in business history and efficient

personalized service is crucial in CRM. 7 ASP(application service provider). A

significant factor in the design of an effective CRM system is the ASP (application service provider). The ASP hosts and manages a software application and delivers it to the customer over the Internet or over private, leased

communication lines. Recently, many enterprises are turning to the ASP model to deploy CRM solutions. This is

especially true for emerging and

(7)

mid-market companies that often lack the infrastructure of information technology staff and systems required for large-scale software implementations.

We believe that ASP is here to stay, and it is changing the economics and

operating assumptions of the software, communications, and computing industries. Important ASP benefits include:

. _{lower total cost;}

. _{faster, less disruptive implementation;} . _{improved functionality; and}

. scalability and reliability.

8 Privacy issues. As indicated by Agrawal and Srikant (2000), privacy issues are further exacerbated now that the Web makes it easy for the new data to be automatically collected and added to databases. In particular, Web data such as log data and market data are created and collected while visitors do not recognize the occasional occurrence of the data. When we use the data, we ought to consider privacy and its related legal and moral issues, and technical methods of realizing privacy-preserving data analysis are required.

6. Conclusions

In the present competitive environment, organizations need to retain existing high-value customers to remain competitive. One technique that can be used to achieve greater loyalty from customers is to personalize services provided. CRM is now making it possible to recreate an

old-fashioned customer service experience in every sector of the economy.

In this article, we reviewed CRM and Web data sources used in online marketing. Additionally, we proposed some design concepts for the development of an effective CRM system. The design concepts are essential in order to develop the system. Currently, we are implementing an entire web-based CRM system. In the system, we are trying to apply the concepts mentioned above.

Some interesting areas for CRM are as follows:

. _{Text mining}_{. Most information on the Web} is not numeric data but text. So, text mining is a very useful technique to discover customer information from the unstructured text.

. Development of techniques for data

analysis. With an enormous amount of data stored in databases and data warehouses, it is increasingly important

to develop powerful tools for analysis of such data and mining interesting knowledge from it.

References

Agrawal, R. and Srikant, R. (1994), ``Fast algorithms for mining association rules'',

Proceedings of the International Conference on Very Large Databases, pp. 487-99.

Agrawal, R. and Srikant, R. (2000), ``Privacy-preserving data mining'',

Proceedings of the ACM SIGMOD

International Conference on Management of Data, available at: www.almaden.ibm.com/ cs/people/srikant

Ahn, J.Y. (2001), ``Some aspects on the Web data mining for effective eCRM from a statistical viewpoint'', PhD dissertation, Chonbuk National University, Chonju.

Ahn, J.Y. (2002), ``A study on the mining access patterns from Web log data'',IEICE Transactions on Information & Systems, Vol. E85-D No. 4, pp. 782-5.

Bose, R. (2002), ``Customer relationship management: key components for IT success'',Industrial Management & Data Systems, Vol. 102 No. 2, pp. 89-97.

Buchner, A.G., Anand, S.S., Mulvenna, M.D. and Hughes, J.G. (1999), ``Discovering Internet marketing intelligence through Web log mining'',ACM SIGMOD Record, Vol. 27 No. 4, available at: www.acm.org/sigs/sigkdd/ proceedings/webkdd99/toconline.htm Burke, R.R., Rangaswamy, A. and Gupta, S. (1999),

``Rethinking marketing research in the digital world'', working paper, eBusiness Research Center, Pennsylvania State University, University Park, PA.

Cheung, W. (1998), ``The use of the World Wide Web for commercial purposes'',Industrial Management & Data Systems, Vol. 98 No. 4, pp. 172-7.

Cooley, R.W. (2000), ``Web usage mining: discovery and application of interesting patterns from Web data'', PhD dissertation, University of Minnesota, Minneapolis, MN. Cooley, R.W., Mobasher, B. and Srivastava, J.

(1997), ``Web mining: information and pattern discovery in the World Wide Web'',

Proceedings of the IEEE International Conference on Tool with Artificial Intelligence, available at: www-users.cs. umn.edu/~ cooley/

Famili, A., Shen, W.M., Weber, R. and Simoudis, E. (1997), ``Data preprocessing and intelligent data analysis'',Intelligent Data Analysis, Vol. 1 No. 1, available at: www.elsevier.com/ locate/ida

Groth, R. (2000),Data Mining ± Building Competitive Advantage, Prentice Hall, Englewood Cliffs, NJ.

Jemmeson, P. (1997), ``Using the Internet for competitive advantage'',Industrial Management & Data Systems, Vol. 97 No. 4, pp. 139-42.

(8)

Kobsa, A., Koenemann, J. and Pohl, W. (2001), ``Personalized hypermedia presentation techniques for improving online customer relationships'',The Knowledge Engineering Review, Vol. 16 No. 2, pp. 111-55.

Lee, S.J. and Siau, K. (2001), ``A review of data mining techniques'',Industrial Management & Data Systems, Vol. 101 No. 1, pp. 41-6. Lee, S.M. and Hong, S. (2002), ``An enterprise-wide

knowledge management system

infrastructure'',Industrial Management & Data Systems, Vol. 102 No. 1, pp. 17-25. Ma, C., Chou, D.C. and Yen, D.C. (2000), ``Data

warehousing, technology assessment and management'',Industrial Management & Data Systems, Vol. 100 No. 3, pp. 125-34. Mobasher, B., Jain, N., Han, E.H. and Srivastava,

J. (1996), ``Web mining: pattern discovery from World Wide Web transactions'', Technical Report, University of Minnesota, Minneapolis, MN.

Pei, J., Han, J., Mortazavi-asl, B. and Zhu, H. (2000), ``Mining access patterns efficiently from Web logs'',Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, available at: http://db.cs.sfu.ca/sections/publication/kdd Peters, L. and Saidin, H. (2000), ``IT and the mass

customization of services: the challenge of implementation'',International Journal of Information Management, Vol. 20 No. 2, pp. 103-19.

Pitkow, J.E. and Kehoe, C.M. (1996), ``Emerging trends in the WWW user population'',

Communications of the ACM, Vol. 39No. 6, available at: http://viror.wiwi.unikarlsruhe. de/webmining/bib/

Spiliopoulou, M. (2000), ``Data analysis for Web marketing and merchandizing applications'',

Proceedings of the 4th European Conference on Principles and Practice of Knowledge

Discovery in Databases (PKDD'2000), available at: www.wiwi.hu-berlin.de/~myra

Srivastava, J., Cooley, R.W., Deshpande, M. and Tan, P.N. (2000), ``Web usage mining: discovery and applications of usage patterns from Web data'',SIGKDD Explorations, Vol. 1 No. 2, available at: www.acm.org/sigs/ sigkdd/explorations

Stone, R.W. and Good, D.J. (2001), ``The assimilation of computer-aided marketing activities'',Information & Management, Vol. 38 No. 7, pp. 437-47.

Swift, R.S. (2001),Accelerating Customer Relationships using CRM and

Relationship Technologies, Prentice Hall, Englewood Cliffs, NJ.

Warner, D. (2000),The Insider's Guide to Building an Effective Knowledge Base, white paper, RightNow Technologies.

Wayland, R.E. and Cole, P.M. (1997),Customer Connections ± New Strategies for Growth, Harvard Business School Press, Boston, MA. JeongYongAhn, Seok Ki Kim

and KyungSoo Han On the design concepts for CRM system