Fuzzy approach for searching in CRM systems

Transcription

1 Fuzzy approach for searching in CRM systes BOGDAN WALEK, JIŘÍ BARTOŠ, CYRIL KLIMEŠ Departent of Inforatics and Coputers University of Ostrava 30. dubna 22, Ostrava CZECH REPUBLIC Abstract: This paper is focused on design of ethodology for searching relevant data in CRM systes based on vague input ters. The paper proposes ethodology for creating an expert knowledge base and a tool for processing vague input ters and extracting relevant data and also presenting these data as evaluated search results and sorting the according to given significance and vague input search criteria. The proposed approach for searching in CRM systes under uncertainty is verified on searching relevant data fro the selected CRM syste. Key-Words: fuzzy; uncertainty; custoer; CRM; expert syste; searching; search results. 1 Introduction In present days the concept of Custoer Relationship Manageent (CRM) is very popular and frequently used. Custoer Relationship is a coprehensive approach for creating, anaging and expanding custoer relationships and it is the basis of arketing and sales branches [1], [2] [6]. CRM describes a odel for anaging interactions and relationships between a co (an organization) and a custoer [5]. CRM systes are used to organize and autoate business processes for arketing and sales activities, custoer service and support, and other activities that help copanies to increase sales and profit [7]. The ain goals of CRM are: to care about current custoers, to find and win new custoers, to regain forer clients, to reduce the cost of arketing, to iprove efficiency of business processes in co, to track product sales, to track projects and their activities. Currently there are any ipleentations of CRM systes that work with large aounts of data about projects, custoers, products, sales, etc. During the search process in these data it is appropriate to find the required data quickly and accurately. The article describes one of the possible ways of finding relevant data in CRM syste. 2 Proble Forulation To search and analyze relevant data belonging to custoer, product or project full-text searches, filters or data ining ethods are usually offered by CRM systes. However, these ethods are unsuitable for processing uncertain data that consist either in searching criteria or in data of the syste itself [3]. Sales anagers, arketing anagers and top anageent on the one side often use vague ters to describe inforation that they want to find in the CRM syste, and on the other side, the relevance of the data is fixed. Working with these vague ters is ore difficult, but it is appropriate to process vague ters and to extract relevant data and afterwards present various searching scenarios and views on the resulting search outputs. 3 Proble Solution In this paper a fuzzy approach for searching relevant data in CRM syste based on vague input ters and expert knowledge base is proposed. Knowledge base consists of IF-THEN rules which are based on vague input ters. Proposed approach consists of the following steps: 1. Definition of desired search results 2. Definition of criteria for searching 3. Creating the knowledge base 4. Loading the relevant data fro database 5. Processing input vague ters and evaluation of search results 6. Showing evaluated search results ISBN:

2 Proposed approach is visually represented in the following figure: Proposed knowledge base consists of IF-THEN rules which is coposed fro defined criteria and deterine degree of relevance of search result. Each criterion represents one input linguistic variable of IF-THEN rule. Output linguistic variable of IF-THEN rule is represented by degree of relevance of search result. Degree of relevance of search result is described by appropriate linguistic value which represents degree of ebership of an ite in set using ebership function: Fig. 1 Proposed fuzzy approach for searching in CRM systes 3.1 Definition of desired search results In the first step, definition of desired search results which will be shown to user is needed. There are any search results that can be shown to anager, anagers and other users who are working with CRM syste: Most significant custoers Most profitable products Best products Most profitable periods, etc. 3.2 Definition of criteria for searching Subsequently, it is necessary to define criteria that will be used for searching. Based on this inforation, the creation of the knowledge base that contains IF-THEN rules for evaluating search results will be possible. It will also be possible to create and copose appropriate query for retrieving relevant data fro database. Here are a few possible criteria for searching the ost significant custoers: Nber of eployees Products quantity Products price Frequency of purchase 3.3 Creating the knowledge base After definition of desired search results and criteria for searching, the knowledge base can be created. 3.4 Loading the relevant data fro database Next, based on criteria defined in section 3.2, the relevant data will be loaded fro database. The type of database that is used is relational database [8], [9]. For each desired search result is necessary to load partial data which will be used for finding the ost appropriate IF-THEN rule fro knowledge base. Afterwards, founded IF-THEN rule will deterine degree of relevance of search result. Loading the relevant data fro database will be described on searching the ost significant custoers (criteria were defined in section 3.2): Nber of eployees inforation about nber of eployees of the specific custoer (co), loaded fro database table custoers Products quantity nber of products that were purchased by specific custoer, loaded fro table products with identifier of the specific custoer Products price total price of all purchased products by specific custoer, loaded fro table products with identifier of the specific custoer, prices of founded records are sed together Frequency of purchase nber of orders realized by specific custoer in specific period, loaded fro table orders 3.5 Processing the input vague ters and evaluation of the search results In this step input vague ters (declared in section 3.2) will be processed and then search results will be evaluated by expert syste (that contains knowledge base created in section 3.3 and uses appropriate IF-THEN rule. Here are few exaples of input ters (desired search results) vaguely defined by user: Most significant custoers ISBN:

3 Significant custoers Very profitable products Good products Worst products 3.6 Showing evaluated search results Finally, desired and evaluated search results are shown to user. Search results are sorted and ordered according to evaluation generated by expert syste. For exaple in case of searching the ost significant custoers, the ost significant custoers will be shown at the top of the list with results and the least significant custoers will be shown at the botto of the list with results. 4 Results Proposed approach will be verified on searching ost significant custoers which are stored in CRM syste. Verified CRM syste stores inforation about custoers, products, purchases, eployees, etc. 4.1 Definition of desired search results In first step user defines that he wants to show the ost significant custoers that will be sorted by significance. Search results are declared as find the ost significant custoers. 4. Frequency of purchase (FREQUENCY_OF_PURCHASE) exaple of values: nearly, rarely, often, very often. Output linguistic variable: 1. Significance of custoer (CUSTOMER_SIGNIFICANCE) exaple of values: low, edi, high, very high. Vague descriptions of linguistic variables are represented by fuzzy sets. One of the is shown in the following picture (see Figure 2) x-axis represents range of the interval, in which fuzzy sets interpreting the input variable are defined (lower and upper bound); y-axis defines ebership degree of the input variable in fuzzy set (evaluation). Figure 2 represented the input linguistic variable FREQUENCY OF PURCHASE, which has 4 possible input attributes, attribute nearly_ is highlighted (left suppree=1, left kernel=5, right kernel=8 and right supree=12). 4.2 Definition of criteria for searching In next step criteria for searching the ost significant custoers were defined: Nber of eployees Products quantity Products price Frequency of purchase 4.3 Creating the knowledge base Then the knowledge base is proposed and constructed. Input linguistic variables are autoatically loaded fro the previous step. Input linguistic variables: 1. Nber of eployees (EMPLOYEES) exaple of values: few, edi, any. 2. Products quantity (PRODUCTS_QUANTITY) exaple of values: sall, edi, big. 3. Products price (PRODUCTS_PRICE) exaple of values: low, edi, high. Fig. 2 Definition of fuzzy set for linguistic variable FREQUENCY_OF_PURCHASE Based on input and output linguistic variables the IF-THEN rules are created. Here are few exaples of IF-THEN rules created by expert: IF(EMPLOYEES ARE MANY) AND (PRODUCTS_QUANTITIY IS BIG) AND (PRODUCTS_PRICE IS HIGH) ISBN:

4 AND (FREQEUNCY_OF_PURCHASE IS HIGH) THEN CUSTOMER_SIGNIFICANCE IS VERY HIGH IF(EMPLOYEES ARE MEDIUM) AND (PRODUCTS_QUANTITIY IS SMALL) AND (PRODUCTS_PRICE IS VERY LOW) AND (FREQUENCY_OF_PURCHASE IS VERY LOW) THEN CUSTOMER_SIGNIFICANCE IS LOW IF-THEN rules are designed and created in LFLC tool, which is described here [4]. Visualization of IF-THEN rules created in LFLC software is shown in the Figure 3: Fig. 3 Definition of IF-THEN rules 4.4 Loading the relevant data fro database Next, based on criteria defined in section 4.2, the relevant data will be loaded fro a database. The database consists of database tables for storing inforation about custoers and their eployees, products, orders, etc. Part of database schea is shown in the Figure 4: Fig. 4 Part of database schea of CRM syste Based on criteria, the SQL queries for each criterion are created. Exaples of SQL queries shows loading inforation about first custoer in database table custoers (CustoerID = 1). 1. Nber of eployees SELECT eployees FROM custoers WHERE id =1; 2. Products quantity SELECT s(quantity) FROM products p, orders o, custoers c WHERE o.product_id = p.id AND o.custoer_id = c.id AND c.id =1; 3. Products price SELECT s( price * quantity) FROM products p, orders o, custoers c WHERE o.product_id = p.id AND o.custoer_id = c.id AND c.id =1; 4. Frequency of purchase selected period is a onth May 2012 SELECT count( product_id ) FROM products p, orders o, custoers c WHERE o.product_id = p.id AND o.custoer_id = c.id AND o.date >= ' ' AND o.date <= ' ' AND c.id =1; Results of SQL queries for each custoer (co) in database are shown in the Table 1, here are explanatory notes: CUST - custoer, EMP -nber of eployees, PRQ products quantity, PRP products price, FoP frequency of purchase: Table 1 Inforation about custoers loaded fro database CUST EMP PRQ PRP FoP ACo ABCo CCCo ECo DECo FACo ESCo BOCo HECo JICo ISBN:

5 4.5 Processing the input vague ters and evaluation of the search results Now, the results of SQL queries for each co are sent as input data to expert syste created in LFLC software. Then, appropriate IF-THEN rules are used for evaluation of the degree of custoer s significance. Evaluation of degree of significance of custoers is shown in the Table 2, evaluation is a value fro interval [0,1] transfored to percentiles: Table 2 Inforation about custoers with evaluated degree of significance CUST EMP PRQ PRP FoP SI ACo ABCo CCCo ECo DECo FACo ESCo BOCo HECo JICo few low sall nearly 2.3 few low sall nearly 2.7 few low sall nearly 4.6 few sall edi rarely 29 few sall edi rarely 28.7 ediu sall edi rarely 28.3 ediu sall edi rarely 30.3 any big high very 89.7 often any big high very 90.3 often any big high very 90.7 often 4.6 Showing evaluated search results Finally, evaluated search results are sorted and shown to user. Custoers sorted by significance are shown in the Table 3: Table 3 Sorted list of the ost significant custoers CUST EMP PRQ PRP FoP SI JICo any big high very often 90.7 HECo any big high very often 90.3 BOCo any big high very often 89.7 ESCo ediu sall edi rarely 30.3 ECo few sall edi rarely 29 DECo FACo CCCo ABCo ACo few sall edi rarely 28.7 ediu sall edi rarely 28.3 few low sall nearly 4.6 few low sall nearly 2.7 few low sall nearly Conclusion The result of this paper is a coprehensive odel representing the search ethodology using vague input ters. The ethodology that uses fuzzy approach for searching relevant data in CRM syste was proposed together with steps to construct the knowledge base that is used to evaluate the significance of the results and the fuzzy evaluation process itself. The proposed odel was verified and soe of the results were presented. Increased involveent of this tool in the other proble doains is also a topic of further research direction. 6 Acknowledgent Presented topic is also a part of internal grant SGS10/PřF/2012, called Fuzzy odeling tools for analysis and design of inforation systes, at Departent of Infoatics and Coputers, University of Ostrava. References: [1] Brown SA., Custoer Relationship Manageent: A Strategic Iperative in the World of E-Business, John Wiley: New York, [2] Swift RS., Accelerating Custoer Relationships: Using CRM and Relationship Technologies. Prentice Hall: Upper Saddle River, NJ, [3] Wong K. W., Data Mining Using Fuzzy Theory for Custoer Relationship Manageent, 4th Western Australian Workshop on Inforation Systes Research, [4] Habiballa H., Novák V., Dvořák A., Pavliska V., Using software package LFLC nd ISBN:

6 International Conference Apliat 2003, Bratislava, 2003, pp [5] Light B., A review of the issues associated with custoer relationship anageent systes, 9th European Conference on Inforation Systes, Bled, [6] Jeong Y. A., On the design concepts for CRM syste, Industrial Manageent & Data Systes, 2003, Vol. 103, Issue 5, p [7] Morgan R. M., Hunt S. D., The coitent trust theory of relationship arketing, Journal of Marketing, 1994, Vol. 58, No. 3, p [8] Atzeni P., Antonellis V. D., Relational Database Theory. The Benjain/Cings Publishing Co, Inc.: Redwood City, [9] Harrington J. L., Relational database design and ipleentation. Elsevier Inc.: Burlington, ISBN: