سیستم های هوشمند بازاریابی برای مدل سازی رفتار مصرف کننده با استفاده از یک روش القاء توصیفی بر اساس سیستم های فازی ژنتیکی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|5565||2009||18 صفحه PDF||سفارش دهید||15450 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Industrial Marketing Management, Volume 38, Issue 7, October 2009, Pages 714–731
In its introduction this paper discusses why marketing professionals do not make satisfactory use of the marketing models posed by academics in their studies. The main body of this research is characterised by the proposal of a brand new and complete methodology for knowledge discovery in databases (KDD), to be applied in marketing causal modelling and with utilities to be used as a marketing management decision support tool. Such methodology is based on Genetic Fuzzy Systems, a specific hybridization of artificial intelligence methods, highly suited to the research problem we face. The use of KDD methodologies based on intelligent systems like this can be considered as an avant-garde evolution, exponent nowadays of the so-called knowledge-based Marketing Management Support Systems; we name them as Marketing Intelligent Systems. The most important questions to the KDD process–i.e. pre-processing; machine learning and post-processing–are discussed in depth and solved. After its theoretical presentation, we empirically experiment with it, using a consumer behaviour model of reference. In this part of the paper, we try to offer an overall perspective of how it works. The valuation of its performance and utility is very positive.
Firms operate in markets that are increasingly “turbulent” and “volatile.” How to deal with this turbulence and survive in these hypercompetitive conditions has become a strategic question (Agarwal et al., 2007 and Christopher, 2000). Consequently, the idea of the achievement and support of a sustainable competitive advantage gave rise, in the nineties, to another focused on its continuous development (D'Aveni, 1994), which is more realistic these days. One of the main implications of this reformed strategic approach is a search for new suitable market opportunities. Of course, such opportunities need to be correctly identified and addressed by firms. This premise justifies the transcendental relevance recently given to the creation and management of knowledge about markets (Drejer, 2004). In this vein, the marketing function of companies and, most especially, their Marketing Management Support Systems (MkMSS) plays a notable role in this task, as they must contribute to the reduction of the uncertainty related to the firms' markets of reference. As we know, this question does not only imply having access to good marketing databases. On the contrary, the key question is having the necessary level of knowledge to take the right decisions (Campbell, 2003 and Lin et al., 2006). The analytical capabilities of MkMSS are more critical than ever to provide this support to marketing managers' decision making, in order to give useful and valuable information about market behaviour. Specifically, we highlight the following: models and methods of analysis. It is expected that MkMSS will improve their performance in the near future, taking advantage of the synergies caused by the integration of modelling estimation techniques based on classic econometrics with new methods and systems based on artificial intelligence (Gatignon, 2000 and Van Bruggen and Wierenga, 2000). The adoption of these new methods represents a worthwhile opportunity to improve the efficiency of the marketing managers' decision making and consequently, if well applied, the accuracy of marketing strategies (Li, Kinman, Duan, & Edwards, 2000). The paper we present here focuses on the exploration and analysis of the suitability of certain brand new methods based on knowledge discovery in databases (KDD) to be applied in marketing modelling. Specifically, our main aim is twofold: first, we aim to make a modest contribution to the methods used in consumer behaviour modelling. In any case, this is the marketing field we have focused on to develop and experiment our methodology, though it also applies to marketing causal modelling, in general, as well as to other Science and Social Sciences fields that work with similar causal models. We propose a complete knowledge discovery methodology, whose main questions are shown in this paper, to extract useful patterns of information with a descriptive rule induction approach based on Genetic Fuzzy Systems; this is a novel hybridization of methods belonging to the field of artificial intelligence, highly appropriate for the marketing problem we face. With this purpose, we have had to give solutions, adapted from our academic field, to the diverse questions related to the main stages of the KDD process; i.e. data preparation, data mining, and knowledge interpretation. Moreover, an important characteristic of our methodology is that it has been designed under the base there is a causal model of reference; a consumer behaviour model in our case. In other words, the knowledge discovery process is guided by a prior theoretic structure that defines the elements (variables) of the model. Hence, this machine learning approach is not only interesting for practitioners, but also for academic researchers' purposes. To address these questions, the paper is structured as follows. Section 2 reflects on the suitability of evolving our marketing modelling methods towards a growing importation and use of artificial intelligence methods to support professional and academic marketing problems. Section 3 presents an overview and justification of the artificial intelligence tools employed (fuzzy rules, genetic algorithms, etc.). Section 4 illustrates with some examples the behaviour of the proposed KDD tools. Section 5 shows the methodological proposal in detail. Next, in Section 6 we experiment with the methodology, show some significant results and dedicate a brief closing part to illustrate both the intrinsic and complementary advantages of our fuzzy modelling-based method. Section 7 discusses the main contributions of our research, reflecting on the academic and managerial implications. Finally, in Section 8 we comment on some research limitations and opportunities (our future research agenda).
نتیجه گیری انگلیسی
Among the main limitations we identify here is the real application of this methodology we present in this paper. We would understand those readers, either scholars or practitioners, who after assimilating the method we propose here said something like: “right, but how could I apply this tomorrow to my research (scholar), or to my decisional process (practitioner)?” Obviously, it would be necessary to have specific software. Such software has already been designed and developed by us; we would not have been able to empirically test our method without it. Notwithstanding, it is not developed enough yet for commercialization. In sum, these are some of the questions that encourage us to go on with the main research project underlying this paper. In particular, some of the research opportunities, hence new challenges to tackle, that are occupying our time in the near future are the following: •Improvement of the genetic algorithm used in the machine learning stage, in order to further improve the performance and accuracy of the fuzzy rules discovery process. •Design and application of new metrics/indexes, added to those of support and confidence, to better evaluate the fuzzy rules obtained; for instance, metrics related to the interestingness of the rules. •The current method has been designed to drive the machine learning process by using a marketing causal model to interconnect variables in the space of search; i.e. what is called “supervised learning”. However, sometimes, the manager or the academic may not have full information about any relation structure among the variables containing a particular database. In other words, the marketing expert may know certain relations, though (s)he may not be aware of reasonable relations about others. Moreover, there could not be aprioristic information about the relations of the variables, or even an attempt to search, without any restriction of search imposed by any model, for “covered” structures in the database. In this case, we could develop what is known as semi-supervised and unsupervised learning, respectively. •Finally, we are working on designing user-friendly software to apply this method. Specifically, this software is integrated in a wider research project focused on developing a software package of diverse artificial intelligence tools to be applied in KDD, called KEEL (http://www.keel.es). This project is supported by the Spanish Ministry of Education and Science.