literature on Web-mining applied to the context of marketing called for an exploratory ... analytics data, demographics, psychographics and other market- and ...
The Clute Institute International Academic Conferences
Key West, Florida USA 2013
Benefits Of Web-Mining For Profiling Existing Web Customers Myriam Ertz, MSc., ESG-UQAM, Canada Raoul Graf, M.A., Ph.D., ESG-UQAM, Canada
Abstract Web-Mining (WM) techniques have been developed in order to analyze massive quantities of data and information spread through the internet. However, WM remains a mysterious, almost occult field for any neophyte. In the popular imagination it is often perceived as an entanglement of algorithms, artificial intelligence, machine learning and sophisticated statistics from which, one does not always grasp the depth and scope but finds it useful to discover “a diamond in a coal pile”. Paradoxically, in an increasingly rationalized world, technology appears as a blue chip. WM is, therefore, appealing because since it is so complex and cabbalistic it must necessarily be a reliable method to obtain relevant results. Data- Mining (DM) and hence, WM would be a kind of recipe which extracts totally new, ready-to- use knowledge from databases. WM is, however, simultaneously a gold mine and a minefield, depending on how the organization implements it. Surely, Web Mining (WM), remains a relatively unknown technology. However, if used appropriately, it can be of great use to the identification of existing customers’ profiles on the internet. The recent technical advances in the field of Web-Mining enhance tremendously the analytical side of the so-called Customer Relationship Management (CRM), which is still usually related to simple transactional functions. Early on, marketers acknowledged the fact that they were incapable of satisfying all the needs and wants of all the individuals on the market. Instead, they identified commonly shared characteristics among individuals to divide them in smaller homogeneous segments which were more manageable. Web businesses face the same dilemma but the potentialities of the web have much more avenues to offer in that respect. This section examines how WM tools contribute to optimally grasp these web opportunities. This study follows an exploratory approach to assess whether Web-Mining fulfills, alone, the core objectives of CRM related to the segmentation of a business’ current customers. Since web-mining is specifically used in a web context and that the web channel becomes increasingly more important from year to year, the study examines specifically how web-mining could fulfill alone these major CRM objectives, related to customer segmentation. The corollary being that this study thus also seeks to highlight, how Web-mining may be used in conjunction with traditional marketing research and other classic aCRM methods to optimize CRM, and hence marketing, in a web context. WM-derivated knowledge can then be managed within the company in a Knowledge Management (KM) framework, in order to optimize relationships with new and/or existing customers, improve their customer experience and ultimately deliver greater value to customers. Web-mining is still a very recent field of study an although is is widely discussed in the information technology and computer science literature, few articles or white papers exist in marketing on the subject. The lack of relevant literature on Web-mining applied to the context of marketing called for an exploratory study design. The main goal of the research was to explore and discover Web-mining experts opinion about the current and state-of-the-art potentialities of web-mining regarding its capacity to segment existing web customers. Following an exploratory research design type, several in-depth semi-structured interviews were conducted in order to get the insight of several (web) data mining experts. Several professionals and academia were interviewed for that purpose. The principal research question to be answered is to what extent Web-mining methods applied to web data (web log data, etc.) provide accurate profiles of existing web customers. Two research propositions were derivated from this research question. The first being that Web data generated by existing web customers are sufficiently detailed and accurate to provide a strong basis for the creation of precise profiles about those existing web customers. The second research proposition is that Web-mining methods applied to web data (web log data, search results and web © Copyright by author; 2013 The Clute Institute http://www.cluteinstitute.com/index.html
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The Clute Institute International Academic Conferences
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pages, etc.) create homogeneous groups of existing web customers. Both research propositions were validated through the research process. The study revealed that web-mining is very well suited to profile existing web customers. It is actually the CRM objective for which Web-mining is the most suitable. Therefore, Web-mining is a tremendously efficient and predictive tool via the wide array of classification and estimation potentialities that it offers. It is also a formidable descriptive tool since it offers a plethora of clustering/segmenting as well as association potentialities. Nevertheless, Web-Mining does not well in understanding the less obvious and underlying dimensions of customer profile, that is for instance describing how existing or prospective customers develop specific profiles, specifically the tastes, preferences, etc that underlie these profiles (e.g. why do they prefer black to green websites, etc.). In fact, to that end, Web-Mining still needs to be complemented with traditional marketing research, preferably through another medium than the internet since customers mainly dislike surveys on the internet. Eventually, the conclusions of this piece of research are mainly aimed at companies and managers’ intention, because they more than the client have the resources and processes to implement the discussed Web-Mining research projects. The study results revealed that segmentation is depicted as a continuum ranging from overall segmentation with limited differentiation, to personalized segmentation with optimal differentiation adapted to one unique individual. Depending on the level of segmentation one seeks to achieve, input data range from the least to the most detailed and insightful data types, namely: IP (Internet Protocol) address, ISP (Internet Service Provider) information, log files (text files that describe the browsing behaviour of a web user), cookies (same as log files but more detailed) and other navigation files, clickstream data (data recording the click behaviour of web user), web analytics (statistics of website visits) providing advanced website traffic statistics, to transactional data. The latter is the most detailed and accurate data for performing customer profiling because it typically requires the customer to log in. Additional external data can be retrieved or purchased such as census data, third parties’ advanced web analytics data, demographics, psychographics and other market- and consumer-related knowledge. Contingent variables such as trends, holiday periods, special events, weather conditions, etc. should be used in conjunction to internal and external data types during the WM analytical step in order to triangulate the knowledge, pinpoint congruence or contradictions, and get an in-depth picture of existing web customers. Contingencies should also help in interpreting internal and/or external data as well as WM results. The data should also be large, homogeneous regarding the context of the research and include many specifiers. WM tools are useful but the quality and quantity of the data really determine the appropriateness of the output. WM tools have traditionally been used in an iterative fashion. They were built based on the input data and then applied to the new data. A lot of time was incurred during both of these steps. Recent advances in the WM community have made WM tools more actionable on the spot, i.e., directly on the website. They should be propelled directly on the website in order to dynamically feeding and enriching databases that encompass customers’ profiles. Automation also allows classifying the customer in a predefined businessspecific taxonomy which could range from masses, segments, niches, personas to the individual, depending on the segmentation strategy chosen. The most powerful of these strategies, namely the personalization strategy based on the individual allows immediate dynamic customization be it of the web page, products displayed, options proposed, etc. The results of this study strictly focus on the outputs that can be generated by means of WM alone without surveys and/or observations, based on the exploratory findings. The following provides a good estimation of the current capabilities in the field of DM as applied to web data in order to generate useful information and ultimately knowledge and allow businesses to build stronger and more profitable customer relationships, in a relational marketing paradigm. Depending on the level of segmentation sought by the company and, therefore, the strategic marketing orientation resulting from the targeting process, WM allows to determining the profile based on a multitude of attributes as well as the strategic importance of existing web customers (unprofitable vs. profitable). WM enables thus to fulfill the first main objective of aCRM which is segmenting of existing web customers. Knowledge about these 3 elements is useful to develop business- specific profiles ranging on a continuum from low to high, given the segmentation level that is sought. The profile of a given web customer can thus be used to predict that customers’ © Copyright by author; 2013 The Clute Institute http://www.cluteinstitute.com/index.html
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The Clute Institute International Academic Conferences
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future loyalty, but also future profitability to develop dynamic response frameworks in real-time and appropriate to the profile of the customer. Keywords: webmining, aCRM, segmentation
© Copyright by author; 2013 The Clute Institute http://www.cluteinstitute.com/index.html
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