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Applying Personalization Methods to Improve Web-based Customer Care Research paper by Olena Parkhomenko Abstract This paper outlines aspects and potentials of the emerging field of personalized customer relationships and focuses on personalization strategies that can be applied to web-based online customer care systems. While there exist various personalization techniques, such as collaborative filtering, rule-based analysis and data-mining methods that are currently used in e-business applications there are still drawbacks and issues to be solved, such as generating effective customer profiles. This paper presents a solution strategy to implementing personalization in online customer care systems - the explicit-implicit conversational approach in combination with such implementation tool as PIDL - Personalized Information Description Language. While the explicit-implicit personalization technique can generate holistic customer profiles and provide possibility to resolve various security issues, such as fraud detection; PIDL facilitates personalization of online information by providing enhanced interoperability between personalization applications. 1. Introduction Today, customers have more choices than ever. They are more aware of the possibilities and more demanding of personal attention. They choose their providers on whatever basis they wish – from price to features to service to customized arrangements. This situation shifts the focus from the product toward the individual customer. Convenience, return policies and credit translate into customer service. The more personal this becomes, the more customers will be loyal to an organization. If all providers have the same high level of product quality and convenience, the focus shifts again to trust in the relationship. Therefore, in various e-business applications, ranging from dynamic Web content presentation, to personalized ad targeting, to individual recommendations to the customers, personalization has become an important business problem. For example, the personalized version of Yahoo (myYahoo) provides to its customers personalized content, such as local weather or interesting events in the area where the customer lives. As another example, Amazon.com and Moviecritic.com provide recommendations on what books to read and movies to see respectively. In general, there is a very strong interest in the industry in personalized (one-to-one) marketing applications and in recommender systems that provide personal recommendations to individual users for products and services that might be of interest to them 1. The advantages of these personalized approaches over more traditional segmentation methods are well documented in the literature. While there has been a lot of work done for personalization in e-commerce applications, less attention has been given so far to the benefits and implementation of personalization methods in web-based customer care systems. In this paper I will show why personalization is vital in customer service and describe current personalization techniques and related issues. This paper also presents a solution strategy to implementing personalization in online customer care systems - the explicit-implicit conversational approach in combination with such implementation tool as PIDL - Personalized Information Description Language. 1 Adomavicius, G., Tuzhilin, A. 2001. Using Data Mining Methods to Build Customer Profiles While the explicit-implicit personalization technique can generate holistic customer profiles and provide possibility to resolve various security issues, such as fraud detection; PIDL facilitates personalization of online information by providing enhanced interoperability between personalization applications. PIDL provides a common framework for applications to progressively process original contents and append personalized versions in a compact format. Another benefit of PIDL is that it supports the personalization of different media (e.g. plain text, structured text, graphics, etc), multiple personalization methods (such as filtering, sorting, replacing, etc) and different delivery methods (for example SMTP, HTTP, IP-multicasting, etc). 2. Related Study and Research The issues of personalization and creating customer profiles in e-business have been addressed in many past and present research works. To start with, there has been conducted a field-related research, supported by the European Commission in its ACTS program, that provides a comprehensive overview of techniques for personalized hypermedia presentation, focusing on hypermedia adaptation for improving customer relationship management utilizing the World Wide Web. This research study showed that the WWW could be fruitfully employed to support the entire sales cycle2. In the pre-sales phase, it is used to establish and strengthen corporate and brand identities and to draw customer’s attention to new products and services. In the sales phase, web-based online storefronts with electronic multimedia catalogs and online ordering/purchasing facilities enable a customer to select the desired product and to purchase it directly. In the post-sales phase, the web is used to reassure customers of his or her purchase decision and to deliver additional values through services like product support, support for user groups and loyalty programs, thus creating opportunities for long-term customer retention and eventually cross- and up-selling. The research study suggests that customer relationship software has to support sales and marketing divisions in their tasks to provide individualized customer care and to integrate and utilize information from various sources to create targeted information, services and product offers. Web-based customer relationship software empowers web-sites with this functionality directly, offering a number of key advantages at reasonable cost, compared to traditional channels: It facilitates the collection of information about large number of customers (their interests, online purchase behavior and support needs). It offers the opportunity for rapid changes and updates of content and presentation to quickly react to new opportunities and challenges. It enables a global around-the-clock presence independent of its locality. It offers the opportunity for dynamic creation of content and presentation formats for narrow-targeted and/or personalized information delivery. In this paper I will show how these hypermedia presentation techniques can be enhanced with modern tools and technologies, such as XML/XSL, to realize significantly more far-reaching personalized online applications. Kobsa, A., Koenemann, J., Pohl, W. Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships. The Knowledge Engineering Review 16(2). 2 2 A well-designed personalized customer service system will take into account such information as: User knowledge. The presentation of the hypermedia material should be adjusted in such a way that users become neither bored by unnecessary explanations, nor confused by details they cannot understand. User skills and capabilities. Besides “knowing what” (customer’s knowledge about concepts and relationships between concepts in an application domain), a customer’s “knowing how” also plays an important role in adapting systems to customer needs. User interests and preferences. Interests among users of the same application often vary considerably, and information and product offerings that are targeted to one particular interest group may not only be of no interest to another group, but may even have adverse effects. User goals and plans. Customers usually pursue concrete goals and plans when visiting the web-site. Therefore, it is important to distinguish these goals and to support customers through the system interface in achieving their goals. As I have already mentioned, virtually, all marketing strategies consider customer care as a cornerstone of success. One startling fact is that customer acquisition costs per customer are typically higher than single purchases. The average online retailer requires three purchases to break even on the acquisition cost for each new customer. Internet companies are spending an average of $250 on marketing and advertising just to acquire one customer. Current studies show that even when they know this information, most sites will not shift their focus from customer acquisition to customer retention and service. A recent McKinsey & Co. study shows that European firms achieve higher levels of e-business success earlier than U.S-based sites do. Europeans competing in the marketplace are much better at turning new customers into repeat customers. In an environment with enforced business competition, price and quality are no longer the only differentiation criterions for a potential customer. He or she cares for the grade of service he or she will receive as a customer. The Swiss National Science Foundation has appointed a profound pre-study on customer care, accounting, charging, billing and pricing for Information and Communication Structures. This pre-study determined new customer care concepts that concentrate on three parts of the customer-business relationship. Firstly, the potential customer’s attention must be attracted by special “shepherding”. Different marketing strategies are employed. By using customer preference profiles, providers, sellers, and retailers provide information on specific products to a selected group or individuals. Actual Business-to-business transactions exploit this information. However, some sophisticated Internet shops like Amazon.com use customer profiles for providing users with targeted e-mail notifications on new products. Software agents, e.g., Firefly, represent another marketing strategy facility. Such agent group user preferences based on their similarities. Companies may use this information to suggest buying opportunities to specific customers. Broadcasting facilities allow different enterprises to offer bids fitted for the customer’s desired product requirements. For example, BidnAsk accepts 3 postings for computer related equipment from potential customers and forwards bids from vendors to the related questioner. Currently, commerce becomes more information intensive. Companies use more and more information technology to deal with the growing information and to better match products to customer’s tastes so as to enhance the service relationship. The targeted marketing strategies towards old customers create a learning relationship with them and are generally believed less costly than acquiring new customers. Secondly, the customer must be cared for during the sales transaction. Besides providing him optimal product information, several value-added services are offered to him. Virtual bookstores have the possibility to purchase out-of-print books due to their alliances with second-hand bookshops. Trust is an important part of the customer-business relationship. If the customer does not trust the provided security mechanisms of the virtual enterprise, he will not buy the product. A number of trust enhancements have been proposed. These technologies help to increase the level of confidence that people have in e-commerce. Thirdly, the traditional after sales support experiences an information technologybased enhancement. Typically, customers contact a call center to troubleshoot problems or to get additional information. An early definition describes a call center as a place of doing business by phone that combined a centralized database with an Automatic Call Distribution system (ACD). But nowadays call centers are more than that. They integrate automation and telephony into businesses processes to both optimize business processes and provide better service to customers. Typical business processes supported include sales, marketing, customer service, telemarketing, technical support or other specialized business activity3. As a result of deregulation and the subsequent advent of new carriers, the communications’ marketplace has become an arena for fierce competition. Providers with flexible operational support systems have a distinct competitive advantage by being able to introduce rapidly product bundles combining multiple services as well as cross-product discounts and promotions. The carrier’s ability to provide superior customer service plays an important role in increased customer loyalty and retention. As a result, customer care is the key to success. Even more research has been done on collaborative features of customer care. IBM T.J. Watson Research Center has conducted a case study that presented the concept and business solution for web-based collaborative customer care. The collaborative customer care technology (known as CLIVE) allows a customer connected to the Web to speak and share data with the bank’s customer service representative 4. The case study pointed out three sets of requirements for the system, which are also relevant to my research project: 1. The object for collaboration is the content of the Web browser. 2. System constraints must be transparent to users. 3. System has to be designed for casual use and errors. The research proved a strong support for these requirements and hinted other interesting issues to pursue in the future (e.g., limiting collaboration to segments of a web-site, sharing applets, etc.) Stiller, B., Fankhauser, G., Plattner, B., Weiler, N. 1998. Pre-Study on Customer Care, Accounting, Billing and Pricing. Wolf, C. G., Lee, A., Touma, M., Daijavad, S. A Case Study in the Development of Collaborative Customer Care: Concept and Solution. IBM T.J. Watson Research Center. 3 4 4 Jack Hafeli, director of consultants at ThinkFast Consulting has written a review that suggests that the next generation of proactive technology-enabled interaction with customers will be based on web analytics. “In terms of using Web analytics to reach customers, information needs and capabilities have become much more than counting the number of visitors to a Website. A more thorough understanding of customer acquisition, conversion and retention are key drivers for online business. Understanding buyer behavior on your site, the effectiveness of advertising and promotions, how to best leverage cross-sell and up-sell opportunities are all critical components to driving online performance. Furthermore, the delivery of analytical solutions can extend far beyond interaction with current customers. Using data stored in the warehouse, an organization can initiate proactive information exchanges with financial analysts, shareholders, the media, even the general public”5. There has been a lot of research done in the area of creating customer profiles. For example, the profiling problem was studied in the data mining academic community by Fawcett and Provost , Aggarwal, Adomavicius and Tuzhilin, and Chan. In particular, Fawcett and Provost studied this problem within the context of fraud detection in the cellular phone industry 6. This was done by learning rules pertaining to individual customers from the cellular phone usage data using the rule learning system RL. However, these discovered rules were used not for the purpose of understanding the personal behavior of individual customers, but rather to instantiate generalized profilers that are applicable to several customer accounts for the purpose of learning fraud conditions. Aggarwal studied the problem of on-line mining of customer profiles specified with association rules, where the body of a rule refers to the demographic information of a user, such as age and salary, and the head of a rule refers to transactional information, such as purchasing characteristics. Moreover, Aggarwal presented a multidimensional indexing structure for mining such rules. The proposed method provides a new approach to deriving association rules that segment users based on their transactional characteristics. However, it does not derive behavior of an individual user in a one-to-one fashion. Chan presented still another approach to the profiling problem in the context of providing personalized Web search. In this approach the user profile consists of a Web Access Graph summarizing Web access patterns by the user, and a Page Interest Estimator characterizing interests of the user in various Web pages. Although the approach presented by Chan goes beyond building simple factual profiles, these profiles are specialized to be used in specific Web-related applications, i.e., to provide personalized Web search. This means that they do not attempt to capture all aspects of the on-line behavior of individual users. One specific consequence of this specialization is that Chan does not use behavioral rules as a part of a user profile. Adomavicius and Tuzhilin presented a framework for building behavioral profiles of individual users7. These behavioral profiles contain not only factual information about Hafeli, J., ThinkFast Consulting, Inc. 2000. Web Analytics: The Next Generation of Interaction with Employees, Supply Chain Partners and Customers. 6 Fawcett, T., Provost, F. 1997. Adaptive Fraud Detection. NYNEX Science and Technology, New York. 7 Adomavicius, G., Tuzhilin, A. 2000. Expert-Driven Validation of Rule-Based User Models in Personalization Applications. 5 5 the users, but also capture more comprehensive behavioral information using conjunctive rules that are learned from user transactional histories using various data mining methods. However, there are caveats to this approach due to the nature of personalization applications. In particular, as will be explained in the paper, the behavioral rules learned about individual users can be unreliable, irrelevant, or obvious. Therefore, post-analysis, including rule validation, becomes an important issue for building accurate personalized profiles of users. The second contribution of this paper lies in developing a new approach to validating the discovered rules during the post-analysis stage of the data mining process. The domain expert who can iteratively apply various rule validation operators performs this validation process. In particular, we describe different validation operators and demonstrate how these operators are integrated into a unifying framework. Development of specific validation operators, in particular, rule grouping method based on attribute hierarchies, constitutes the third contribution of this paper. Finally, the paper describes a case study of testing the proposed validation method on a marketing application. Adomavicius and Tuzhilin defined the quality of rules stored in user profiles in several ways. Rules can be “good" because they are (1) statistically valid, (2) acceptable to a human expert in a given application, (3) “effective" in the sense that they result in certain benefits obtained in an application. In their paper, Adomavicius and Tuzhilin focused on the first two aspects, i.e., statistical validity and acceptability to an expert. Although a lot of research papers on personalization solutions has been published in the academic literature, most of the work has been done in the industry so far. In the next section I will describe current personalization issues and existing solutions. 3. Problem Statement. General approach. As with most business tools, a customer care web site's value to an organization is less intrinsic and more a function of how it is used or applied. More than ever, the ability to acquire and retain these shrewd customers now depends on how much you know about them, and how well you can serve them based on their specific needs. Increasingly, when customers can get a product or service from multiple sources, they base their decisions less on the service and more on the personalization of information delivery and content. To achieve the goals of e-marketing, personalization must be customer-centric, not product- or service-centric. A true customer-centric enterprise does not stop with just this form of personal interaction. A true customer relationship management (CRM) enterprise will trace every interaction with the customer including complaints, call center transactions, in-store purchases, types of services preferred, etc. – not just Web visits. And this applies not only to e-commerce solutions, but also for web customer care systems. Certainly, one approach to customer centricity is to create a detailed profile of each customer's behavioral habits. The more data can be captured about the customer, the better the e-business applications can create personal interactions, provide better service and, ultimately, increase customer loyalty. Many enterprises have arrived at the same conclusion: To more effectively engage and retain customers, it is vital to personalize customer communications during customer Web visits. One challenge with personalization is that it requires large amounts of data to be collected every day for every customer for every Web visit. With increased traffic, this data is growing exponentially. Traditionally, this data was used to monitor Web traffic, determine future bandwidth requirements and assure trouble-free web-site operations. Now it is being used to tweak campaigns, up-sell or cross-sell customers 6 and redirect site visits. These new uses of Web data have dramatically increased the technological complexity and difficulty of capturing and analyzing the data. Three significant problems must be overcome before useful analysis can occur: Volumes of data. Popular Web sites create several gigabytes to terabytes of data per day; however, these sites do not mistake volumes of data for volumes of information. Synchronizing, combining and integrating server logs, sorting and then processing the data is a time-consuming and complex set of tasks that must be accomplished under severe time constraints. Quality of data. Prospects and customers do not always complete forms or enter sufficient data. They may start an order and then cancel the transaction, or they may only have a cookie with no other information. Sometimes, prospects and customers may even give false or misleading information. Filtering and sorting out extraneous and unnecessary data and then integrating the remaining data with existing data is difficult at best and may be impossible. Integration of data with other systems. An e-business Web site does not exist in a vacuum. It is simply one part of the customer's experience. The Web site's effectiveness must be examined in relation to all other customer touchpoints (other sales channels, CSR interactions, distributors, VARs, etc.). Many web visits are brief. Given processing constraints and the limited time frame to recognize and interact with the customer, minimal analysis occurs at this point. The primitive analysis that does occur during Web visits is limited to determining if the visitor is a known customer, if that customer already has a profile established and, if so, what content should be displayed. Web data is used as a basis for immediate analysis where every keystroke or combination of navigational keystrokes may trigger an activity. The triggered activity might be a banner change presenting a new product at a special price or it might be an e-mail coupon sent to the customer after he or she has logged off the Web site. Additionally, the web site may welcome the visitor by name, inform the visitor of new products similar to those he or she purchased or give recommendations or suggestions for new available services. There are two types of responses – solicited and unsolicited: Solicited responses. For solicited responses in which the customer specifically requests something, it is crucial to ensure that every customer interaction is closed-loop. That is, there should not be any one-way communications. This means that every query is fully answered, problems are promptly handled and the customer is constantly informed of problems and transaction status. In addition, the technical architecture must support the Web visit. A customer performing a transaction over the Internet expects the same level of service he or she would receive from a live customer service representative. For example, once an order is placed, the customer should be informed as to whether the item is in stock, when to expect the item and how to track its progress to the customer's door. This implies that the Web site is fully integrated with the operational systems fulfilling the order. Unsolicited responses. For unsolicited responses customer information is utilized to take an action that a company assumes will be of use or of benefit 7 to the customer. If the assumption is incorrect, the company may be perceived to have violated the customer's trust or misused personalization information in terms of the customer's privacy. This is the one area that threatens e-business more than any other. Personalization requires the gathering of as much information about the customer as possible. Most people are still very wary of how and where they provide confidential or personal information. Statistically, you are far safer flying in an airplane at 30,000 feet than you are driving your car, yet no amount of statistics will convince some people that they are safer in the air. It is not always possible to make people feel secure. Customers will gravitate to those enterprises that make them feel secure. It is important to define and implement policies and procedures that guarantee the maximum amount of security for customers and monitor these policies and procedures to ensure they are fulfilling their promise. The assimilation of customer profile information brings the Web information together with current customer information from operational systems, external customer demographic information and the results from strategic analyses. The customer operational data store (ODS) is the component of the Corporate Information Factory that houses the customer profile information. It enables quick response time for queries about the current customer's situation and supports reporting capabilities for the business community8. Another way to solve the problem of the volume of Web data is to determine the level of aggregation; summarization or data grouping that is needed. Data reduction in the customer ODS can be substantial and make the ODS much more usable and informative. For example, a company may determine that customer segmentation is an important objective for them, on the other hand; perhaps it is the customer's motivation for buying that is important. The company then aggregates or summarizes data based on these objectives to get the fundamental nature of the customer's activities, but not the extraneous clutter. Regardless of the method of aggregation, summarization or grouping that is used; it is essential to maintain the consistency in the measurements. For example, if an enterprise captures revenues for each individual customer, then it must capture the costs for each individual customer as well. A personalization engine, along with other input, is used to create a customer profile. The customer's profile consists of a description of behavioral habits, products likely to be purchased, services likely to be utilized and some demographic information. The goal of most e-businesses is to understand their customers' habits and behaviors as close to real time as they possibly can. Analysis that is less than immediate – occurring 15 minutes to one hour after the Web logs have been collected – uses the defined aggregation and summarization schemes. Time-based analytics are used to gain additional knowledge about the Web site customer. Information gathered during this analysis will be used to generate the content (recommendations, banner ads, welcoming messages, etc.) during the customer's next visit to the Web site and is posted in the customer operational data store as soon as technologically feasible. Detailed Web log information is propagated into the customer ODS multiple times a day. This can be accomplished using an extract, transform and load (ETL) tool. The profile record found in the customer ODS is updated in the traditional online The Corporate Information Factory, 2nd Edition, by William H. Inmon, Claudia Imhoff and Ryan Sousa, published by John Wiley & Sons, 2000 8 8 transaction processing (OLTP) manner. The update to the customer ODS will record information about a customer including any items purchased since the last update. For instance, products that a customer may have purchased throughout the day can be updated in the customer ODS with an ETL process and may eventually be passed on to the data warehouse. On a time-based interval, the web logs are processed into higher summarization tables. The roll-ups or summarizations may take place within the customer ODS and are recreated to hold only a day's worth of history. The summarized roll-ups are used for reporting as well as tactical analysis. These summarizations could be placed in a cube or an online analytical processing (OLAP) "oper-mart" for tactical analysis. An ETL process can be used to create the summarized data as well. With an application that evaluates the customer profiles, specific customers can be targeted for a structured marketing campaign based on the integrated information. The sales channels can quickly become aware of the opportunities in the ODS via mobile or Internet access within the corporation. From the discussion above we can see that applying personalization in e-business applications in not a trivial process and there are many related issues that have to be solved. In the next section I will describe a personalization approach that I believe has the potential to resolve major personalization issues and provide the expected benefits in e-business systems. 3. A Proposed Personalization Approach In order to show how personalization techniques and tools can be effectively applied to web-based customer care systems, I will first describe the existing types of personalization modeling, their advantages and drawbacks. As of today, personalization is usually classified with two types of customer modeling: Implicit personalization that uses behavior-based modeling Explicit personalization that uses data provided by the user in forms-based dialogue Implicit personalization Implicit personalization is based on a behavioral modeling system. These systems have the ability to draw on past customer behaviors to aid in the anticipation and response of a customer’s service or content needs in the present. With implicit personalization, customers can be sorted and analyzed by which services they choose, where they click, buy and spend their time. This is very different from today’s current methods of television-based demographic profiling and audience segmentation. With implicit personalization, you are what you do. These technologies rely extensively on developing user profiles and maintaining constant user tracking throughout customer visits. Here are the known technologies of implicit personalization: 1.Collaborative filtering In customer care systems, collaborative filtering can use customer behaviors to collect and process information according to the frequency and type of obtained 9 services from a site. This technology can then be employed to dynamically update their user profiles so they can target marketing promotions and suggestions. For example, if I make online bill payment with a credit card, my profile and future recommendations might from then contain advertisement for credit cards and different online credit services. 2. Clickstream analyzers Clickstream analyzers shape user profiles by the places customers visit and the time they spend within a site. The real-time tracking tools analyze where customers click and how long they stay there. Then, in many cases, automated predefined scoring systems apply point systems and rankings to the different actions and paths the user takes. Customers can be sorted into communities of other users who have established similar surfing patterns. The more a customer shops and surfs, the more likely the system’s business rule-guided algorithms will produce effective results. Though this technique is more used in e-commerce solutions, it can also be effectively applied to online customer care systems. Analyzing clickstreams can help to group customers according to the types of services they utilize - click through most frequently. 3. Rules-based systems Rules-based technology requires that business logic and merchandizing rules be embedded in conditional “if/then” statements to create content display. So, for example, if a customer purchases a printer, the system will offer ink at a 25% discount. Business rules are also used to filter out inappropriate offers. For example, don't offer a discount on a CD player to a customer who has already bought one. The problem with this approach is that it is not trivial to estimate how effective and good the generated rules are. 4. Data-mining methods Finally, data mining technology uses complex algorithms to select, explore and model large amounts of data to uncover previously unknown patterns for business advantage. Real-time mining scores and rates the probability that an offer will be accepted. For example, if a customer just checked out the customer service FAQ portion of the Web site, a window offering the opportunity to "chat with a customer service representative" could be served. This technology has a lot of potential, though the efficient personalization algorithms are yet to emerge. Explicit personalization Explicit personalization bases its customer dialogue on forms and questionnaires that allow users to "tell" the customer care site what they are interested in or what they would like to see. With explicit personalization, profiles are based on who customers tell you they are. Customers usually will receive customized data based on what they choose from predefined lists. However, choosing from these lists is time consuming, and extracting relevant data from them, when they are based on typical choices, can be difficult. Among the standard current examples of explicit personalization are the pervasive "my" pages. Most successful personalized sites allow customers to select from categories of information, which in turn populate specialized information to their personalized pages. 10 My Yahoo forms contain 70 separate customized items to select from, and msn.com contains more than 90. These customizations are mostly binary, allowing the customer to turn specified information on or off. This method is very time consuming to set up, as it is not always evident to the customer what is being turned off or on. Explicit-implicit personalization Explicit-implicit personalization is a new way to think about combining two levels of user control, and has the potential to become the future of online marketing. This type of technology creates explicit forms or questionnaires that are implicitly generated based on current or previous behavior. These new interactive dialoguebased forms nurture a conversation between the site and the customer. Customers and users of sites like to know how things work. Customer-service sections using implicit-explicit personalization models can allow users to see what information and behaviors are being tracked. By simply asking the question, "Does this profile match you?", sites open the possibility for meaningful electronic conversation with their customers. For example, one of the leaders in personalization, Amazon.com, is currently allowing customers to update implicitly collected personalization profile information in explicit forms. A link titled "Rate or exclude your past purchases" allows customers to rank their purchases on a scale of 1 to 5, or to completely exclude previous purchases from their customer profiles. A feature in the same section allows customers to directly input titles of favorite authors or movies, or names of artists of interest. Another feature, termed a "preference wizard," allows customers to select from categories of store items that best represent their interests, and, more importantly, to preview the results of these personalized selections on their personal homepages. I see this conversational approach to distributing information and advice to the customer as an important and innovative strategy that can be used for online personalization and would be especially effective in online customer care applications, since it ensures generation of adequate customer profiles. The intent of conversations with customers is getting and giving information, and opening up dialogues to establish relationships. As companies expand their contact points or channels of interaction (face-to-face, mail, telephone, internet), most fail to develop strategies to consolidate these customer service databases, and this failure alone will encourage a fragmented conversation. No matter how small or large the scale of these initiatives, giving customers the ability to correct data and converse about their preferences will allow online applications to create and manage a holistic customer image. The new conversations that result will keep customers from being improperly profiled by a clickstream analyzer when they are lost or confused on a site, or from being misread by a collaborative filter while purchasing gifts. Besides providing a whole range of marketing and merchandizing benefits, a holistic customer profile data can have even more applications in web-based customer care systems. Since every customer will have a unique, constantly updated behavioral profile, it gives a possibility to establish fraud detection mechanisms and address a number of security issues. The actual value from a personalization feature can be direct, which is an easily measured customer action from a newly implemented site feature, or indirect, which 11 is not so easily measured and is closely related to customer satisfaction as a result of CRM (customer relationship management) efforts. The explicit-implicit conversational approach to personalization achieves both direct and indirect values for e-business in general, and web-based customer care systems in particular. Direct value is measured by evaluating features recently added to the site and gauging the results of a customer acting on that feature. One main measurement of value is the ability of a newly implemented site feature to convert browsers to customers, to convert customers to repeat customers, and to increase the number and size of average customer orders. Indirect value is not so easily measured and is closely related to customer satisfaction with CRM efforts. Customer service, satisfaction, and retention features, which are the focus of most personalization systems, are hard to compare with hard conversion-to-sales figures. Even so, sites that integrated personalization as a feature set reported a visitor-to-buyer conversion rate two to three times greater than that of their competition. There are a lot of tools on the market that struggle to realize existing personalization techniques. Most of them though concentrate on a particular approach. In this paper I will analyze Personalized Information Description Language - a tool that I believe to be generic and ideal for implementing explicit-implicit conversational personalization approach in web-based customer care systems. Personalized Information Description Language 9 In almost all current personalization applications, a central site (service) first collects some form of personal data relevant to the personalization (i.e. gender, age, interests, etc), obtains the raw data that should be personalized (i.e. newsfeed, product information, etc), personalizes it according to the user's preferences and finally makes it available to the user via download (using HTTP or FTP) or delivery (via email, Web push, etc). As personalization applications continue to grow on the Web, the above issues (user data solicitation, personalization of raw data, dissemination of personalized data) have become increasingly important for the Web community. In order to personalize both safe and effectively, we have to move away from ad-hoc implementations and create general frameworks that provide efficiency and interoperability. User's privacy has been a dominant topic in recent months and efforts such as the W3C's Platform for Privacy Preferences Project (P3P) provide architecture to solicitate, transmit and store user data in an informed and secure way. However, even after the industry wide acceptance of P3P or similar frameworks, the actual personalization and dissemination of personalized data still remain isolated solutions that vary from service to service and application to application. With XML being a well-established system for defining, validating, and sharing document formats, it has a lot of potential for implementing the above-mentioned personalization techniques. In particular, XML provides syntax for the Personalized Information Description Language (PIDL) that has especially been developed to facilitate personalization of online information by providing enhanced interoperability between personalization applications. PIDL provides a common framework for applications to progressively process original contents and append personalized 9 Koike, Y., Kamba, T., Langheinrich, M. 1999. PIDL - Personalized Information Description Language. W3C Note. 12 versions in a compact format. PIDL supports the personalization of different media (e.g. plain text, structured text, graphics, etc), multiple personalization methods (such as filtering, sorting, replacing, etc) and different delivery methods (for example SMTP, HTTP, IP-multicasting, etc). The Personalized Information Description Language (PIDL) aims at creating a unified framework for services to both personalize and disseminate information. Using PIDL, services can describe the content and personalization methods used for customizing the information and use a single format for all available access methods. PIDL addresses the following three requirements for a general personalization language: It can be applied to describe content that is composed of various media: plain text, structured text, graphics, etc. It can be used to describe the effects of various personalization methods and their combinations: filtering, sorting, replacing, etc. It supports the description of contents that is delivered using various methods including pull- and push-type delivery such as SMTP, HTTP, IP-multicasting, and others. PIDL contributes making personalization applications simple by realizing the interoperability among such applications. Once applications support reading and writing PIDL documents, processed contents of one application can be incrementally processed by other applications. Changing the information delivery method of an application can be done with little effort if the personalized contents are expressed in PIDL. I will briefly stop here on some PIDL features that I believe to be essential to support the requirements listed above. Namely, PIDL: Encapsulates both the original contents and the progressively processed personalizations in a single XML document. Can contain personalized contents for multiple user in a single XML document, allowing effective distribution of personalized content over 1-to-many connections such as IP-multicasting. To protect sensitive information about each users personalization preferences from being disclosed, such personalized content can optionally be encrypted with the user's public key. Supports incremental storage of personalization results in order to keep the overall document size small, even including personalization for several hundreds of users. Traditional personalization systems apply a single personalization process to a set of original content in order to produce a personalized version of the content that is then delivered to the user. In order to allow effective distribution of such processes, PIDL documents contain not only the result of a personalization step, but also the original content this personalization was based upon. Having the raw, non-customized content included in the document even after personalization has finished allows later, independent processes to continue or alter the initial personalization. PIDL uses a method called "Progressive storage of processed content" when describing the effects of personalization steps. In other words, the original contents and the results after being processed by a particular personalization process are described separately but are encapsulated in a single XML document. 13 By allowing multiple blocks of such processed content to be included in a single document, multiple personalization processes can independently and/or progressively, that is building upon the results of a previous process, customize the document. Each personalization process operating on a PIDL document is not allowed to change the original contents included in the file, but instead adds its results, its processed content, at the end of the file, just after the original contents and any other personalized content that might already exist. After several such personalization methods have been applied to the original contents, the results are accumulated progressively as shown in Figure 1. XML document original contents processed contents by function "A" processed contents by function "B" processed contents by function "C" Figure 1: Progressive storage of processed content Another feature of PIDL is its "Compact storage of processed content". As I mentioned above, personalized contents for multiple users are stored in a single PIDL document. However, simply storing the full content of each personalization step for each subsribing user would easily result in a huge document containing hundreds of copies of almost identical content. To solve this problem, PIDL documents do not store the full content of each personalization step in its processed content sections, but instead stores only the processing method used and the personalization data used for the processing. For example, if a personalization process selects "newspaper articles that will match user preferences", the PIDL document will only store a set of flags for each user indicating whether a particular article from the original content is relevant to the user (See Figure 2). In order to create a full personalized document out of such a compact representation, a client-side PIDL document reader would parse the document and display the listed articles for each user according to the included processed content. 14 XML document original contents (article X, Y, Z) Selects the articles that will match each user's preference User "A"={X, Y, Z} User "B"={X, Y} User "C"={Y, Z} Figure 2: Compact storage of processed contents From this short PIDL overview we can see that the features inbuilt in this tool make it very efficient for implementing personalization techniques in e-business applications. PIDL is a relatively new tool that has yet to be completely defined and developed; therefore there hardly exists any real-life implementation example. However, to demonstrate efficiency of XML technologies, I will provide here an implementation example of better customer service that uses wireless markup language (WML) for personalization. Case study example10 The state of California uses wireless technology to extend its citizen information portal to wireless devices, as well as alert citizens to freeway traffic snags and impending power outages. According to Arun Baheti, director of e-government for the state of California, customer care was primary focus for rolling out a wireless component. “What we're trying to do primarily is offer better customer service and give citizens access to the information they need”, he said. The wireless component of the portal is based on software from Redwood City, Calif.based BroadVision Inc. This allows the State to deliver Web content dynamically, eliminating the need to create different code for each device, Baheti said. BroadVision supports extensible markup language (XML), which is the foundation for the wireless medium, according to Drew Bartkiewicz, vice president of industry solutions at BroadVision. California uses BroadVision for the Web site and Kana for outbound e-mail text paging, which is used to send alerts to pagers and wireless phones. BroadVision software allows for the wireless application protocol (WAP) and PDA templates so that the portal can be browsed on a handheld device. The database is enabled for The source of this example is an article by Campbell, C. 2001 Think customer satisfaction, not cost savings, when going wireless. (www.searchCRM.com) 10 15 XML, and the data is presented via wireless markup language (WML) on wireless devices and HTML on standard Web browsers. From this example we can see that XML technology can be successfully used to implement a better customer service. However, implementing personalization with PIDL still remains a subject for future research. 4. Conclusion In this paper I have shown that one of the ways e-business and web-based customer care systems can create viable, sustainable business models to offer a better customer experience is through implementation of robust personalization systems. Customers are beginning to expect and demand well-orchestrated personalization features, and studies have shown that companies that want to survive will need to make increasing commitments to offering them. Personalization is also a web design movement toward including customer data in decision-making about utilization of web products and marketing efforts. The key is to understand the main principles of personalization, as outlined above, and to find opportunities to fit them into the web environment wherever they produce value. Personalization systems can offer both direct value, through increased customer conversion and retention rates, and indirect value, through increased customer satisfaction. If sites can determine the specifics of their customers’ needs, these systems will help in developing continuous relationship-based marketing with each individual customer or segment. As a solution strategy to implementing personalization techniques in online customer care systems I proposed the explicit-implicit conversational approach in combination with such implementation tool as PIDL. While the explicit-implicit personalization technique can generate holistic customer profiles and provide possibility to resolve various security issues, such as fraud detection; PIDL facilitates personalization of online information by providing enhanced interoperability between personalization applications. PIDL provides a common framework for applications to progressively process original contents and append personalized versions in a compact format. Another benefit of PIDL is that it supports the personalization of different media, multiple personalization methods and different delivery methods. 5. References 1. Adomavicius, G., Tuzhilin, A. 2001. Using Data Mining Methods to Build Customer Profiles. Stern School of Business, New York University. 2. Adomavicius, G., Tuzhilin, A. 2000. Expert-Driven Validation of Rule-Based User Models in Personalization Applications. (Kluwer Academic Publishers). 3. Adomavicius, G., Tuzhilin, A. 1997. Discovery of actionable patterns in databases: The action hierarchy approach. Working Paper IS-97-8, Stern School of Business, New York University. 4. Campbell, C. 2001 Think customer satisfaction, not cost savings, when going wireless. (www.searchCRM.com) 5. Fawcett, T., Provost, F. 1997. Adaptive Fraud Detection. NYNEX Science and Technology, New York. 16 6. Kobsa, A., Koenemann, J., Pohl, W. Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships. The Knowledge Engineering Review 16(2), pp. 111-155 (Cambridge University Press). 7. Koike, Y., Kamba, T., Langheinrich, M. 1999. PIDL - Personalized Information Description Language. W3C Note. 8. Stiller, B., Fankhauser, G., Plattner, B., Weiler, N. 1998. Pre-Study on Customer Care, Accounting, Billing and Pricing. 9. Wolf, C. G., Lee, A., Touma, M., Daijavad, S. A Case Study in the Development of Collaborative Customer Care: Concept and Solution. IBM T.J. Watson Research Center. 17