Consumer Behavior Analysis by Graph Mining Technique (post print version)

In this paper, we discuss how graph mining system is applied to sales transaction data so as to understand consumer behavior. First, existing research of consumer behavior analysis for sequential purchase pattern is reviewed. Then we propose to represent the complicated customer purchase behavior by a directed graph retaining temporal information in a purchase sequence and apply a graph mining technique to analyze the frequent occurring patterns. In this paper, we demonstrate through the case of healthy cooking oil analysis how graph mining technology helps us understand complex purchase behavior

Market segments based on the dominant movement patterns of tourists

This paper presents an innovative method for tourist market segmentation-based on dominant movement patterns of tourists; that is, the travel sequences or patterns used by tourists most frequently. There were three steps to achieve this goal. In the first step, general log-linear models were adopted to identify the dominant movement patterns, while the second step was to discover the characteristics of the groups of tourists who travelled with these patterns. The Expectation–Maximisation algorithm was then used to partition tourist segments in terms of socio-demographic and travel behavioural variables. The third step was to select target markets based upon the earlier analysis. These methods were applied to a sample of tourists, over the period of a week, on Phillip Island, Victoria, Australia. A significant outcome of this research is that it will assist tourism organisations to identify tourism market segments and develop better tour packages and more efficient marketing strategies aligned to the characteristics of the tourists

Discovering temporal regularities in retail customers’ shopping behavior

In this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customerâ\u80\u99s temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity

Modelling the spatial-temporal movement of tourists

Tourism is one of the most rapidly developing industries in the world. The study of spatio-temporal movement models of tourists are undertaken in variety of disciplines such as tourism, geography, mathematics, economics and artificial intelligence. Knowledge from these different fields has been difficult to integrate because tourist movement research has been conducted at different spatial and temporal scales. This thesis establishes a methodology for modelling the spatial-temporal movement of tourists and defines the spatial-temporal movement of tourists at both the macro and micro level. At the macro level, the sequence of tourist movements is modelled and the trend for tourist movements is predicted based on Markov Chain theory (MC). Log-linear models are then adopted to test the significance of the movement patterns of tourists. Tourism market segmentation based on the significant movement patterns of tourists is implemented using the EM (Expectation-Maximisation) algorithm. At the micro level, this thesis investigates the wayfinding decision-making processes of tourists. Four wayfinding models are developed and the relationships between the roles of landmarks and wayfinding decision-making are also discussed for each type of the wayfinding processes. The transition of a tourist movement between the macro and micro levels was examined based on the spatio-temporal zooming theory. A case study of Phillip Island, Victoria, Australia is undertaken to implement and evaluate the tourist movement models established in this thesis. Two surveys were conducted on Phillip Island to collect the macro and micro level movement data of tourists. As results show particular groups of tourists travelling with the same movement patterns have unique characteristics such as age and travel behaviours such as mode of transport. Effective tour packages can be designed based on significant movement patterns and the corresponding target markets. Tourists with various age groups, residency, gender and different levels of familiarity with physical environment have different wayfinding behaviours. The results of this study have been applied to tourism management on Phillip Island and the novel methods developed in this thesis have proved to be useful in improving park facilities and services provided to tourists, in designing tour packages for tourism market promotion and in understanding tourist wayfinding behaviours

Consumer behavior analysis by graph mining technique

Abstract. In this paper we discuss how graph mining system is applied to sales transaction data so as to understand consumer behavior. First, existing research of consumer behavior analysis for sequential purchase pattern is reviewed. Then we propose to represent the complicated customer purchase behavior by a directed graph retaining temporal information in a purchase sequence and apply a graph mining technique to analyze the frequent occurring patterns. In this paper we demonstrate through the case of healthy cooking oil analysis how graph mining technology helps us understand complex purchase behavior.

CONSUMER BEHAVIOR ANALYSIS BY GRAPH MINING TECHNIQUE

In this paper, we discuss how graph mining system is applied to sales transaction data so as to understand consumer behavior. First, existing research of consumer behavior analysis for sequential purchase pattern is reviewed. Then we propose to represent the complicated customer purchase behavior by a directed graph retaining temporal information in a purchase sequence and apply a graph mining technique to analyze the frequent occurring patterns. In this paper, we demonstrate through the case of healthy cooking oil analysis how graph mining technology helps us understand complex purchase behavior.Graph mining, graph structure, consumer behavior, purchase pattern, cooking oil market