یک روش مجموعه خشن مبتنی بر سلطه برای رفتار مشتری در بازار خطوط هوایی

Market segmentation is a crucial activity in the present business environment. Data mining is a useful tool for identifying customer behavior patterns in large amounts of data. This information can then be used to help with decision-making in areas such as the airline market. In this study, we use the Dominance-based Rough Set Approach (DRSA) to provide a set of rules for determining customer attitudes and loyalties, which can help managers develop strategies to acquire new customers and retain highly valued ones. A set of rules is derived from a large sample of international airline customers, and its predictive ability is evaluated. The results, as compared with those of multiple discriminate analyses, are very encouraging. They prove the usefulness of the proposed method in predicting the behavior of airline customers. This study demonstrates that the DRSA model helps to identify customers, determine their characteristics, and facilitate the development of a marketing strategy.

In the face of a highly competitive and fast-changing airline market, managers must not only provide high-quality service but also react appropriately to changes in customer needs. However, it would be helpful if, instead of targeting all customers equally or offering the same incentives to all customers, enterprises could target only those customers who meet certain profitability criteria based on their individual needs or purchasing behaviors [5]. Customer behavior is the result of complex interactions between a number of factors, which can include the level of marketing activity, the competitiveness of the environment, brand perception, the influence of new technologies, and individual needs [34]. The characteristics and behaviors of airline customers are even more complex, and customer perception and behavior are affected by many factors, such as safety, service, technology, environment, price and many others. Hence, it is crucial that management determine the most important factors that affect the attitude and loyalty of airline customers. In the past, researchers have generally made use of statistical surveys to determine customer behavior. In such surveys, natural language or linguistic variables (e.g., “although the airline service is satisfactory, the price of the product being offered is high, the individual’s decision is not to purchase”) are used to describe customer patterns. Unfortunately, this can create an environment of imprecision, uncertainty, and partiality with regard to knowledge. These linguistic variables are then transformed into quantitative values, after which factor, cluster, and discriminant analyses are conducted. However, the semantic imprecision of natural languages leads to problems of computation, especially when the information described in a natural language is beyond the reach of existing bivalent logic and probability theory techniques [41]. Recently, data mining techniques have been adopted to predict customer behavior [6] and [30]. Data mining is one stage in Knowledge Discovery in Databases (KDD), involving the application of specific algorithms for pattern extraction [21]. Marketing managers can develop strategies to attract new customers and retain highly valued ones based on this mined knowledge. The Dominance-based Rough Set Approach (DRSA), originally developed by Greco et al. [8] and [9], is a relatively new approach in data mining that is very useful for data reduction in qualitative analysis. The rough set theory, a kind of natural language computation, is particularly useful for dealing with imprecise or vague concepts [23]. Basically, natural language computation is a system in which the objects of computation are simply predicates and propositions drawn from a natural language. A set of decision rules is generated by applying the rough set approach to analyze the classification data. These decision rules are in the form of logic statements of the type “if conditions, then decision”. The set of decision rules represents a preference model for the decision-maker that is expressed in a natural and understandable language. According to Zhu et al. [40], the rough set method does not require additional information about the data; it can work with imprecise values or uncertain data, is capable of discovering important facts hidden in that data, and has the capacity to express them in natural language. The rough set theory has been successfully applied in a variety of fields, including medical diagnosis, engineering reliability, expert systems, empirical studies of material data [15], evaluation of bankruptcy risk [29], machine diagnosis [39], business failure prediction [1] and [4], network intrusion detection [40], travel demand analysis [7], mining stock price [35], the insurance market [28], and accident prevention [36]. Although the Classical Rough Set Approach (CRSA) is a powerful tool for handling many problems, it is not able to deal with inconsistencies originating from the criteria, e.g., attributes with preference-ordered domains (scale) like product quality, market share, and debt ratio [10]. However, the DRSA has an advantage over the CRSA in that it has access to an information table that displays comprehensive dominance relations. It is able to deal with inconsistencies where decisive classes are not consistent with their criteria. The aim of this study is to mine data regarding airline customer behavior using the DRSA. The derived knowledge can help airlines identify valuable customers, predict future behavior, and enable firms to make proactive, knowledge-driven decisions.

This study illustrates the usefulness of the DRSA approach as an operational tool for the prediction of customer behavior in the air transport market. The proposed prediction model takes the form of decision rules. Since the derived rules are supported by real examples, they describe only the most relevant attributes/criteria. The classical rough set theory handles attributes without preferences, a technique that does not always accurately represent the real world. The DRSA is constructed by extending the classical rough set theory to include qualitative reasoning for preference-based customer behavior analysis. This is done by replacing the indiscernibility relation with the dominance relation. In this way we can represent the conflicting preference relations that affect customer behavior more objectively without introducing the equivalence class concept of the classical rough set theory. Compared with those of the traditional statistical method, the results indicate that the DRSA has better prediction ability. Moreover, the derived decision rules are in natural language form, which makes their meaning easier to understand than with traditional methods.