Personalized Customer Service Management for Networked Enterprises Alexander Smirnov, Mikhail Pashkin, Nikolai Chilov St.Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 39, 14-th Line, 199178, St.-Petersburg, 199178, Russia, E-mail:
[email protected] Abstract Since members of networked enterprises have to intensively cooperate, interoperability between them is of a highest importance. Proposed generic pattern of this organisational form made it possible to apply customer service management mechanism to this area. The paper describes a related to personalized customer service management part of the developed by the authors approach to knowledge logistics and its implementation as a CSM system for an industrial company. Presented personalized customer support is based on application of profiling and grouping techniques for better recognition of customer requests given in a free text form. A common shared ontology is used for terminology description and providing for interoperability between different companies – members of the networked enterprise. The paper omits technical details presented in other publications (the appropriate references are given) and discusses major principles of the approach and implementation of them. Keywords Networked enterprise, interoperability, customer service management, customer profiling, customer clustering
1
Introduction
Nowadays most of the industrial companies have been restructuring and/or establishing new cooperative relationships in order to succeed in the face of market globalisation and increasing competition. As a result of these changes new organisational forms have appeared. One of such successful forms is a networked enterprise. In a networked enterprise each unit (enterprise member) has its direct customers. E.g., unit D has its customer units ("customer companies") F and E (cf. Figure 1). Given no centralised management of the networked enterprise units that could influence upon a choice of their customers, a generic pattern can be defined as a unit with its direct customers (cf. Figure 1). As a result an efficient customer service management for the companies constituting the enterprise could improve the interoperability between them. A
D
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Figure 1: Structure of a generic networked enterprise pattern
Many research efforts have been undertaken in this area to perform a shift from "product-centric" production to "customer-centric" production based on the idea of mass customization. One of the ways to improve communication with customers is so-called distribution channels. Distribution channels are the connection between a company's propositions and its target customers [Currie, 2004]. They describe how a company gets in touch with its customers. The purpose of the distribution channels is to make the right quantities of the right products or services available at the right place, at the right time to the right people [Pitt, Berthon, Berthon, 1999]. This tightly correlates with the aim of Knowledge Logistics (KL). KL is a new direction of knowledge management dealing with activities on acquisition, integration, and transfer of knowledge from
distributed sources to decision makers/knowledge customers by demand for complex tasks solving [Smirnov, et al., 2004a]. With regard to individual customer requirements, available knowledge sources, and current situation analysis in an open information environment, KL addresses problems of intelligent support of customer activities. It focuses on development of methods and tools allowing to turn distributed information into useful knowledge. This would be very helpful when implemented in a customer service management (CSM) system as a part of customer relationship management (CRM) complex. CSM provides the company’s face to the customer and provides a single source of customer information [Croxton, et. al., 2001]. Usually, the first step for building a dialog with a customer is to open an access to company's product catalogues as a part of corporate knowledge repository. This can be done via specially designed software, e-mailing, corporate Web site and other distribution channels. But such strategy does not allow understanding which information is the most interesting for the customer, which modifications can improve proposed services and products and has other drawbacks. The next step is in providing feedback tools: e.g., questionnaires, interface forms for results estimations, etc. But the experience shows that customers usually do not participate in such actions without a stimulation from the side of the company. The further step is to gather history of customer requests and to learn it. Based on the results of the learning it is possible to increase customizability of existing information systems, improve their advantages and facilitate "surfing" of customers in the "ocean" of corporate knowledge memory. Figure 2 presents the evolution of the developed by the authors CSM system based on the KSNet-approach to KL described in [Smirnov et al., 2004b]. The approach considers the KL problem as a problem of a network configuration that includes end-users / customers, loosely coupled knowledge sources / resources, and a set of tools and methods for information / knowledge processing. The figure takes into account the following major features: (i) intelligence of the system in providing interface forms: static templates for special structured inputs and precise results for specific tasks → free text inputs for knowledge sources search → learning-based intelligent adviser; and (ii) customizability: unknown unspecified customer → building and supporting target groups (e.g., by job titles, area of interests etc.) → personal profile-based support. The target users of the system are sales manages and planning and design engineers of the "customer companies". However, the list of users can vary while the system evolves and extends with regard to types and content of knowledge sources. …
Customizability Learning and understanding Grouping of customers
Target groups Free text requests Unknown (anonymous) customer
Structured customer requests Intelligence Templates Spelling, stemming, etc. Active adviser
Figure 2: Evolution of the developed CSM system based on the KSNet-approach
The first two levels (structured customer request and free text request) have been addressed and described in detail in [Hinselmann, et. al., 2004]. Structured customer requests represent templates (specially designed forms for searching within a limited group of products/solutions) that allow achieving high relevance of the found results but miss universality. Free text requests
have maximal universality but achieving high levels of the result relevance is a challenging task. Described here CSM system developed by the authors does this by setting some syntactical constraints on the free text requests and by using a part of the common shared ontology of the company. To improve further free text request processing it is possible to accumulate information about customers' interests by profiling and grouping. These are the topics the paper concentrates on. The step of learning and understanding assumes analysing customer inputs for predicting their actions and interests, better recognition of their requests, etc. This is the topic of future research. The paper presents usage of elements of the KSNet-approach to KL in the designed CSM system. The system has been developed for a company producing manufacturing equipment that has more than 300.000 customers in 176 countries supported by more than 50 companies worldwide with more than 250 branch offices and authorised agencies in further 36 countries. The system is referred to as the system “Intelligent Access to Catalogue and Documents” (IACD). Current version of the system provides for customers a common way to search for products and solutions and presents information about different applications: (i) technical data of company’s products, (ii) project-specific solutions based on tasks’ conditions given by customers and (iii) corporate documents and available Web sites taking into account customers’ interests and constraints stored in the corporate ontology. It helps to easily find solutions for planning simple methods and for alternative comparison. Grouping customers can show similarities between different customers that would make it possible to better serve them, to provide interesting for them information "just-in-time" or even "just-before-time". Besides, producing "good" groups can provide additional useful benefits (e.g., better filtering of results corresponding to customers' interests). For this purpose clustering of the customers into a number of distinct segments or groups in an effective and efficient manner is required. Clustering is one of key areas surrounding inter-record and knowledge base structures, particularly those that enable customer recognition [Talburt, et. al., 2004]. The paper is structured as follows: section 2 describes research efforts related to the topic considered, section 3 concentrates on such elements of the approach as structure of customer profiles and methodology used for customer clustering, section 4 discusses implementation of the CSM system and findings related to it.
2
Existing Theories and Work
Knowledge sharing and exchange in a networked enterprise is of a highest importance. This is to be achieved at both technical and semantic levels. The interoperability at the technical level is addressed in a number of research efforts. It is usually represented by such approaches as e.g., SOA (service-based architecture) and on the appropriate standards such as WSDL and SOAP. The service-based architecture of the described here KSNet-approach was described in [Smirnov et al., 2004b]. The semantic level of interoperability in networked enterprises is also paid significant attention. As an example (probably the most widely known) the Semantic Web initiative is worth to be mentioned [Semantic Web, 2005]. The main idea is to use ontologies for knowledge and terminology description. Another significant topic is customer profiling, which enables personalised CSM. Below references to some of profiling systems are given. Though, these systems are not aimed at working with customers, the main features of profiling are preserved. CONNEX is a knowledge project initiated by the library function within Hewlett Packard Laboratories. The goal of this project is to provide a guide to human knowledge resources. It uses a Web browser as an interface to a relational database. The primary content of the database is a set of expert profiles, or guides to the backgrounds and expertise of individuals who are
knowledgeable on particular topics. Browsing or searching CONNEX allows finding someone with required knowledge and skills. It is supposed that the experts themselves furnish their original knowledge profiles and maintain them over time [Davenport, 1996]. The goal of Microsoft’s SPUD is to create an online competency profile for jobs and employees. The project is focused not on entry level competencies, but rather on those needed and acquired to stay on the leading edge of the workplace. There were five major components to the SPUD project [Davenport, 1997]: development of a structure of competency types and levels, defining the competencies required for particular jobs, rating the performance of individual employees in particular jobs based on the competencies, implementing the knowledge competencies in an online system, and linkage of the competency model to learning offerings. One of the key goals of the project "Knowledge On-Line" (KOL) is to get consultants to work more collaboratively. KOL is based on a centralized, many-to-one client/server architecture [Tristram, 1998]. The goal of the SAGE KM system development is to create a repository of experts at the State of Florida State University System [Becerra-Fernandez, 2000]. This system searches for experts in a number of connected repositories (databases), which belong to different institutions and departments.
3
Research Approach
The main goal of the presented here CSM system based on the KSNet-approach is to provide information about solutions to customers as well as own personnel in addition to existing product catalogue and to find products and solutions by means of expressing customer’s task. Therefore, besides company's documents two other data sources were selected as knowledge sources: (i) the product catalogue containing information about 20’000 items produced by the company: technical data, price, etc. for different languages, and (ii) the application “Project” containing a set of rules for configuration of handling system projects, structured data for industry segment and automation function description, and technical data of carried out products. These applications are oriented to industrial engineers and designers. Extension of these target groups with new ones allows increasing the number of potential clients and providing additional benefits to the company. Based on this information a part of the common shared ontology of the company is built. The ontology uses frame-based knowledge representation model and includes classes and attributes. Usually, it is proposed to have a common shared ontology for a networked enterprise. However, the practice shows that this is not always possible due to the large number and heterogeneity of enterprise members. When the generic pattern defined in the introduction of the paper is considered it is enough to build a smaller common shared ontology for one networked enterprise member only (later in the paper referred to as "ontology"), but this ontology should also support synonyms that might be used by its customers to provide for interoperability. This is how it was implemented in the presented approach. The following scenario of the customer access to corporate information was developed: the customer passes authentication procedure, selects an appropriate interface form and enters a request into the system. The system recognizes the request and defines which data the customer needs. If the customer needs to solve a certain problem the system defines load conditions (parameters describing a certain problem: e.g. mass to be moved, direction of the transportation, environmental conditions etc.) and looks for handling system projects. If no certain problem is defined by the customer the system checks information in product catalogue, database storing technical data of standard handling systems and searches through company’s documents. Among the major tasks to be solved the following should be outlined: 1. Keep existing facilities of the applications and avoid doubling of data;
2.
Extend opportunities of fast provision of information about the company’s products by new features; 3. Provide multilingual interface; 4. Implement local and Web versions of the software; 5. Index existing documents against information stored in the database. The detailed technical description of the system can be found in [Hinselmann, et. al., 2004]. For better customer serving, the approach assumes creation of customer profiles correlating with the ontology. The customer profile has the following constituents: 1. customer identifier; 2. personal data: customer name, e-mail, home page, demographic data, contact information, etc; 3. system data: login, password, customer type, individual attitudes, preferences and settings (including interface language and settings, utilized software and platform, etc.); mail box – storage of system messages (to provide for a "messaging" service). 4. collected data: consisting of (i) contact history: sessions protocols (date and time of registration in the system, performed actions, etc.), and (ii) an archive of requests entered into the system. Implemented algorithm of customer and request clustering implemented in the IACD system has the following steps: 1. Extract words/phrases from the request (text processing). 2. Calculate similarity between the request and ontology elements (i.e. compare text strings extracted from the request and names of classes and attributes). The algorithm of fuzzy string comparison is used for this purpose. It calculates occurrence of substrings of one string in the other string. For example, string “motor” has 5 different substrings (m, o, t, r, mo) contained in the string “mortar”. The total number of different substrings in “motor” is 13 (m, o, t, r; mo, ot, to, or; mot, oto, tor; moto, otor). The resulting similarity of the string “motor” to the string “mortar” is 5/13 or 38%. 3. Construct weighted graph consisting of ontology classes and attributes, and customers. Weights of arcs are calculated on the basis of (i) similarity metrics (i.e. they are different for different customer requests) and (ii) taxonomic relations in the ontology. 4. Construct weighted graph consisting of customers (when classes and attributes are removed arcs weights are recalculated). 5. Cluster customers graph. For the clustering procedure a weighted customer – ontology graph is considered. It contains three types of nodes: C – classes from the ontology, A – attributes of the classes, U – customers (system users). The graph consists of two types of arcs. The first type of arcs I (СА, СС) is defined by the taxonomy of classes and attributes in the ontology. The second type of arcs II (CU, AU) is defined by relations between customer requests and classes/attributes (cf. Figure 3a). Weights of arc between nodes corresponding to classes and customers CUweight and corresponding to attributes and customers AUweight are defined via the similarity CUsim and AUsim of the class or attribute (calculated via the fuzzy string comparison algorithm described above). The similarity is a property of relations between class – customer/request or attribute – customer/request. Weights of arcs are defined as follows: CUweight = 1 – CUsim; AUweight = 1 – AUsim. Arcs CA and CC tying together classes and attributes via taxonomic relations (defined by ontology relations class-class, class-atribute) have CAweight, CCweight ∈ ( ε , 1) defined by the
IACD system administrator. CCweight means arcs’ weight of linked classes in the ontology. CAweight – arcs’ weight of linked attributes and classes. Since customers are represented by their requests, based on this graph the requests and consequently customers are clustered on the basis of the lowest weights of connecting arcs. This is performed in the following sequence. First, the shortest routes between customers are calculated (cf. Figure 3b). E.g., weight of the arc U1U2 will be calculated as follows U1U2 weight = A1U1 weight + C1A1 weight + C1U2 weight; weight of the arc U2U3 can be calculated by 3 ways, it is considered in Figure 3b that U2U3 weight = C1U2 weight + C1C3 weight + C3U3 weight is the shortest one; etc. Based on the calculated weights a new graph consisting of the customers only is built (cf. Figure 3c). Before the clustering procedure the IACD system administrator sets the value of the parameter Dmax. Assuming that U1U2 weight > Dmax, U1U3 weight > Dmax and U2U3 weight < Dmax, two clusters can be identified: the first cluster includes customers U2 and U3, and the second one includes customer U1 (dashed circles in Figure 3c). a)
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Figure 3: Weighted customer – ontology graph and customer clustering procedure.
4
Implementation and Findings
For the industrial company a corresponding to the considered task fragment of the ontology has been built. Its taxonomy includes more than 240 classes and more than 355 attributes from different sources and has 4 levels. Based on the ontology the IACD system performs search for products / projects and solution in available catalogues. In [Chen, 2001] the following eight properties of modern applications related to free text processing (which is necessary for customer request recognition) were analysed: •
multi-language support, currently IACD system supports three languages: English, Russian, German.
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automatic taxonomy creation, the ontology is built automatically in the IACD system based on the available knowledge sources;
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domain-specific knowledge filter (using vocabularies or ontologies), four level ontology is used in IACD;
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conceptual associations (automatic thesauri), currently this is out of the scope of the IACD system;
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indexer or phrase creator, in IACD all documents are indexed for fast access;
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multi-document format support, IACD system supports MSOffice documents, RTF documents, web pages, Adobe PDF files, ASCII text files;
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natural or statistical language processing; in IACD natural language processing consists of tokenization, spelling, stemming, etc.;
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term extraction, in IACD names of ontology classes and attributes, units of measures (e.g. “kg” and “mm”) are extracted from customer requests; For implementation of the last two items (natural or statistical language processing and term extraction) a pattern-based mechanism was designed for the system IACD. Together with
company experts there have been developed several language dependent patterns (free text constructions) that can be recognised by the system. Such patterns include class names and their synonyms (e.g., "drive"), attribute names with restrictions and measurement units (e.g., "diameter 5 mm" or "length > 10 cm"), numbers that are not parts of the previous patterns (e.g., "119603") and misspelled words (e.g., "MDH") that can be parts of product numbers and designations, and other. Each pattern is processed separately and then the results are combined and the final relevance of the answers to the request is calculated. Here presented example illustrates clustering of 10 requests and 26 classes related to them (attributes are omitted). Figure 4 demonstrates the requests (some of the requests are replaced with meaningful patterns), classes and relevance between them: the darker cells represent classes corresponding to the appropriate requests with a higher degree of relevance. After the clustering procedure the results represented in Figure 4b have been obtained. The dotted horizontal lines separate request groups from each other. Presented example is rather small and is used for illustrative purposes only, however having enough statistics it is possible to identify real regularities in customer's interests. a)
Classes
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Figure 4: Example results of the clustering procedure. Darker cells mean higher similarity between requests/patterns (rows) and ontology classes (columns). Figure (a) represents requests and classes before clustering, figure (b) – after.
5
Conclusions
Successful customer service management requires customer-centric production strategy across all business processes of companies – from their culture to integration of business processes, which is critical for networked enterprises. In such enterprises customer information should be used as a common foundation in providing interoperability between their members, yet allows each member to optimize around its assigned mission. Since customer-centricity can only be as good as the quality of the customer information upon which it is based, it is critical to assure the highest quality, consistency, and flexibility when managing customer information.
Acknowledgement Some parts of the research were done by the Contract “Intelligent Access to Catalogues and Documents” between Festo and SPIIRAS, and as parts of project # 16.2.44 of the research program "Mathematical Modelling and Intelligent Systems" and project # 1.9 of the research program “Fundamental Basics of Information Technologies and Computer Systems” of the Russian Academy of Sciences. References Becerra-Fernandez, I.: The Role of Artificial Intelligence Technologies in the implementation of People-Finder Knowledge Management Systems. In: Knowledge-Based Systems, Elsevier, 13, 2000, pp. 315 – 320. Croxton, K. L., García-Dastugue, S. J., Lambert, D. M., Rogers, D. S.: The Supply Chain Management Processes. In: The International Journal of Logistics Management, Vol. 12, No. 2, 2001, pp. 13 – 36. Currie, W. L. (ed.): Value Creation from e-Business Models. Elsvier, 2004. Hinselmann T., Smirnov A., Pashkin M., Chilov N., Krizhanovsky A.: Implementation of Customer Service Management System for Corporate Knowledge Utilization. In: Proceedings of the 5th International Conference on Practical Aspects of Knowledge Management (PAKM 2004), LNAI 3336, 2004, pp. 475 – 486. Pitt, L. Berthon, P., Berthon, J.-P.: Changing Channels: The Impact of the Internet on Distribution Strategy. Business Hirizons, March-April, 1999. Smirnov, A., Pashkin, M., Chilov, N., Levashova, T.: Knowledge Logistics in Information Grid Environment. In: Zhuge, H. (Ed.): The special issue "Semantic Grid and Knowledge Grid: The Next-Generation Web" of International Journal on Future Generation Computer Systems. Vol. 20, No. 1, 2004(a), pp. 61 – 79. Smirnov, A., Pashkin, M., Chilov, N., Levashova, T., Krizhanovsky, A.: Continuous Business Engineering for Virtual Enterprise Configuration Based on Adaptive Services. In: proceedings of the 10th International Conference on Concurrent Enterprising (ICE 2004), 2004(b), pp. 385–393. Talburt, J., Wang, R., Evans, M., Edirisinghe, N., Katz-Haas, R., et. al.: Customer-Centric Information Quality Management (CCIQM). CCIQM Work Group whitepaper, 2004. Internet References Chen, H.: Knowledge Management Systems: A Text Mining Perspective. 2001 URL http://dlist.sir.arizona.edu/archive/00000483/01/chenKMSi.pdf. Last accessed: Jan., 2005. Davenport, T. H.: Knowledge Management Case Study: Knowledge Management at Hewlett-Packard, 1996. URL: http://www.bus.utexas.edu/kman/hpcase.htm. Last accessed: Jan., 2005. Davenport, T. H.: Knowledge Management Case Study: Knowledge Management at Microsoft, 1997. URL: http://www.bus.utexas.edu/kman/microsoft.htm. Last accessed: Jan., 2005. Semantic Web, 2005. URL: http://www.semanticweb.org. Last accessed: Jan. 2005. Tristram, C.: Common Knowledge. CIO Web Business Magazine, 1998. http://www.cio.com/archive/webbusiness/090198_booz.html. Last accessed: Jan., 2005.
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