Customer Movement Research Articles

Fractal Dimension of Shopping Path: Influence on Purchase Behavior in a Supermarket

This paper aims to use fractal dimensions to quantify the complexity of customer in-store movements, and proposes a purchase model factoring in the effects of complex customer movements on purchase behavior. We used the box-counting method to calculate the fractal dimension of shopping paths and investigated its relationships with basket size and sales, which are viewed as important for marketing. We found that the customer group with high fractal dimensions had mean values for the number of sales floor zones visited, basket size, stay time in store, and sales amount statistically higher than those of the customer group with lower fractal dimensions. We analyzed a binomial logit model to identify positive effects that the fractal dimension has on purchases in the food ingredient categories of vegetable, fish, and meat.

Analysis of queueing networks with batch movements of customers and control of flows among clusters

The paper proposes two methods for calculating the stationary distribution of the queueing networks with batch movements of customers and control of flows among clusters. The methods differ in their computing efficiency. The constraints related to practical use of the first method are determined mostly by the dependence of the required amount of computation on the cardinality of the set of states of networks under study. The second method is approximate and analyzes queueing networks lying in the subclass of almost completely decomposable networks. The second method is based on the decomposition of set of states of the queueing network under study into subsets and proves much more computationally efficient compared to the first method.

Dynamic Redeployment to Counter Congestion or Starvation in Vehicle Sharing Systems

Extensive usage of private vehicles has led to increased traffic congestion, carbon emissions, and usage of non-renewable resources. These concerns have led to the wide adoption of vehicle sharing (ex: bike sharing, car sharing) systems in many cities of the world. In vehicle-sharing systems, base stations (ex: docking stations for bikes) are strategically placed throughout a city and each of the base stations contain a pre-determined number of vehicles at the beginning of each day. Due to the stochastic and individualistic movement of customers,there is typically either congestion (more than required)or starvation (fewer than required) of vehicles at certain base stations. As demonstrated in our experimental results, this happens often and can cause a significant loss in demand. We propose to dynamically redeploy idle vehicles using carriers so as to minimize lost de-mand or alternatively maximize revenue for the vehicle sharing company. To that end, we contribute an optimization formulation to jointly address the redeploy-ment (of vehicles) and routing (of carriers) problemsand provide two approaches that rely on decomposability and abstraction of problem domains to reduce the computation time significantly. Finally, we demonstrate the utility of our approaches on two real world data sets of bike-sharing companies.

A Pervasive Promotion Model for Personalized Promotion Systems on Using WLAN Localization and NFC Techniques

In this paper, we propose a novelpervasive business modelfor sales promotion in retail chain stores utilizing WLAN localization andnear field communication (NFC)technologies. The objectives of the model are to increase the customers’flowof the stores and theirincentivesin purchasing. In the proposed model, the NFC technology is used as the first mean to motivate customers to come to the stores. Then, with the use of WLAN, the movements of the customers, who are carrying smartphones, within the stores are captured and maintained in themovement database. By interpreting the movements of customers as indicators of their interests to the displayed items,personalizedpromotion strategies can be formulated to increase their incentives for purchasing future items. Various issues in the application of the adopted localization scheme for locating customers in a store are discussed. To facilitate the item management and space utilization in displaying the items, we propose anenhanced R-treefor indexing the data items maintained in the movement database. Experimental results have demonstrated the effectiveness of the adopted localization scheme in supporting the proposed model.

User Intent Estimation from Access logs with Topic Model

Abstract As the Internet is widespread and there are many online shops in the Internet, many persons buy products in the online shops. Customer's behavior in the online shops is a sequence of customer driven activities intrinsically because his/her movement in an online shop occurs according to only his/her decision. Hence, to achieve satisfactory purchase experiments it is important how the shop supports them. Online shops have to predict visitors’ intents correctly to support them effectively. One of information resources the shops can use is an access log including information on customer's movement in the online shop. If they are histories of customer's behaviors in online shops and the behaviors depend on customer's intents, we can extract new knowledge on them from the access logs. Speaking concretely, we can predict customers’ intents from the access logs since their internal intents affect their activities. We can realized more appropriate recommendation service by changing recommendation strategy depending on customer's intents. In this paper, we propose a method to predict customer's intents from access logs in a real online shop. We adopt a Topic Tracking Model (TTM) to analyze the access logs.

20709 セルオートマトンによる店舗内購買行動シミュレーション

Simulation of customer behavior in a store was conducted by Cellular Automata. Customer moved around in a store depending on the layout of items, so the layouts of a store have large effect on amount of sales. But in fact, it is difficult for retailers to design the best layout, because there are large amount of items hi a store. In such a case, computer simulation is a powerful tool. We modeled the customer behavior in a store by using Cellular Automata (CA). CA is one of discrete modeling methods. In CA algorithm, local neighbor rules are defined as interaction of elements which compose the phenomena. By using CA, we modeled the interactions of customers hi a store based on the personal space, and also interactions between customers and items. As a result, customers walked around hi a store depending on the layout of goods and the movement of other customers.

Mining of customer walking path sequence from RFID supermarket data

A fundamental problem with huge potential advantages for object tracking, product procurement processes and customer movement is the storage and extraction of information from RFID datasets. In this paper, we have designed an efficient technique for tracking the customers' walking path sequences using RFID equipped data. The frequent walking path sequences of the customers' movement have been extracted by exposing the most visited areas and walks across the warehouse and the typical products selected along the way. We make use of synthetic RFID datasets to experiment the proposed technique. From the analysis, we showed that the run time and memory usage is outperformed likely by 50% than the previous method. The applications such as analysing the sequential behaviour in telecommunication, market basket analysis, medical data analysis and electronic government used the sequential pattern mining methods.

The Architecture of a Churn Prediction System Based on Stream Mining.

Churning is the movement of customers from a company to another. For any company, being able to predict with some time which of their customers will churn is essential to take actions in order to retain them, and for this reason most sectors invest substantial effort in techniques for (semi)automatically predicting churning, and data mining and machine learning are among the techniques successfully used to this effect. In this paper we describe a prototype for churn prediction using stream mining methods, which offer the additional promise of detecting new patterns of churn in real-time streams of high-speed data, and adapting quickly to a changing reality. The prototype is implemented on top of the MOA (Massive Online Analysis) framework for stream mining. The application implicit in the prototype is the telecommunication operator (mobile phone) sector. A shorter version of this paper, omitting Section 5, was presented at CCIA’13 (http://mon.uvic.cat/ccia2013/en/).

Analyzing consumers’ shopping behavior using RFID data and pattern mining

The development of sensor networks has enabled detailed tracking of customer behavior in stores. Shopping path data which records each customer’s position and time information is attracting attention as new marketing data. However, there are no proposed marketing models which can identify good customers from huge amounts of time series data on customer movement in the store. This research aims to use shopping path data resulting from tracking customer behavior in the store, using information on the sequence of visiting each product zone in the store and staying time at each product zone, to find how they affect purchasing. To discover useful knowledge for store management, shopping paths data has been transformed into sequence data including information on visit sequence and staying times in the store, and LCMseq has been applied to them to extract frequent sequence patterns. In this paper, we find characteristic in-store behavior patterns of good customers by using actual data of a Japanese supermarket.

Nonparametric inference for stochastic feedforward networks based on cross-spectral analysis of point processes

In this paper we study a nonparametric estimation problem for stochastic feedforward systems with nodes of type G/G/$\infty.$ We assume that we have observations of the external arrival and external departure processes of customers of the system, but no information about the movements of the indistinguishable customers in the network. Our aim is the construction of estimators for the characteristic functions and the densities of the service time distributions at the nodes as well as for the routing probabilities. Since the system is only partly observed we have to clarify first if the parameters are identifiable from the given data. The crucial point in our approach is the observation that in our stochastic networks under study the influence of the arrival processes on the departure processes can be described in a linear and time-invariant model. This makes it possible to apply cross-spectral techniques for multivariate point processes. The construction of the estimators is then based on smoothed periodograms. We prove asymptotic normality for the estimators. We present the statistical analysis for a tandem system of two nodes in full details and show afterwards how the results can be generalized to feedforward systems of three or more nodes and to systems with positive feedback probabilities at the nodes.

공간적 패턴클러스터링을 위한 새로운 접근방법의 제안 : 슈퍼마켓고객의 동선분석

Identifying the major moving patterns of shoppers' movements in the selling floor has been a longstanding issue in the retailing industry. With the advent of RFID technology, it has been easier to collect the moving data for a individual shopper's movement. Most of the previous studies used the traditional clustering technique to identify the major moving pattern of customers. However, in using clustering technique, due to the spatial constraint (aisle layout or other physical obstructions in the store), standard clustering methods are not feasible for moving data like shopping path should be adjusted for the analysis in advance, which is time-consuming and causes data distortion. To alleviate this problems, we propose a new approach to spatial pattern clustering based on longest common subsequence (LCSS). Experimental results using the real data obtained from a grocery in Seoul show that the proposed method performs well in finding the hot spot and dead spot as well as in finding the major path patterns of customer movements.

An application of queuing theory to modelling of melange yarns. Part II: A method of estimating the fibre migration probabilities and a yarn structure simulation algorithm

A queuing model of staple yarn structure was presented in Part I of this work, where the migration behaviours of fibres were simulated by the movement of customers in a Markovian network of queuing systems. Part II of the series proposes (i) a multi-dimensional minimization method for estimating the migration probabilities of fibres based on the analysis of the distribution of number of fibres in yarn cross-sections; (ii) a yarn structure simulation algorithm which uses the migration probabilities of fibres, yarn technical specifications (e.g. linear density and twist) and colour composition to produce a realistic three-dimensional image of a melange yarn. The result is a high quality three-dimensional image of a melange yarn showing its fibrous structure as well as the formation of hairiness. Due to the generic nature of the model and the simulation algorithm, this approach can be applied to a wide range of yarns including ring-spun or open-end spun yarns of solid shade or produced from melange blends. Further improvements to the proposed model are discussed including the application of inhomogeneous Poisson process and the analysis of energy relationships which govern the migration behaviour of fibres.

Network routing control with G-networks

The aim of this paper is to detail a control scheme for packet computer networks whose purpose is to minimise a quality-of-service oriented performance metric by re-routing the traffic. The model is based on G-networks with triggered customer movement to represent traffic re-routing, and on a gradient descent based optimisation algorithm. The model and the algorithm are presented and we show that the gradient descent algorithm is of computational complexity O ( N 3 ) where N is the number of nodes in the packet network. Via the use of multiple classes of normal traffic and multiple classes of triggers, our approach allows one not only to evaluate the effect of the control, but also to incorporate the overhead that the control traffic will induce, and the consequences of the delays or possible losses of the control traffic. Similarly, these effects will naturally be incorporated when one considers both the impact of the control traffic on the cost function, and the details of this control traffic in the control algorithm itself.

A Facility Design Model for 1300 Capacity School Foodservice with Adjacency and Bubble Diagrams

This study aimed to suggest a 1300 scale of a middle school foodservice facility floor plan which was compliant to the principle of HACCP, as well as ensuring food and work safety, and the flow of personnel and food materials. which consisted of 46 nutrition teachers and 6 experts, responded with a questionnaire on the relationship of functional area and space. Using their opinions, key principles for the design of the facility were single direction movement of food materials, customers and workers; minimization of the cross-contamination through the separation of functional space; and securement of customer-focused efficiency; staff-centered convenience and efficiency; and work and food safety. After the completion of an adjacency diagram, bubble diagram and program statement, the functional areas of a 1300 scale middle school food-service facility were allocated as follows: 9.9 m 2 for the receiving area, 56.1 m 2 for the pre-preparation area, 10.5 m 2 for the food storage area, 6.0 m 2 for the supplies storage area, 97.8 m 2 for the cooking area, 33.6 m 2 for the service area, 52.5 m 2 for dish washing area, cafeteria 410.5 m 2 , 4.5 m 2 for the front room, for a total of 725.8 m 2 . Expert groups have pointed to limitations within this model as there are no windows in the office for the influx of fresh outside air and a need for the straight line installation of steam-jacket and frying kettles on the sides of windows. This study can be useful as the guidelines for estimating the investment cost of the facility and placing the placement of functional areas and equipment in the renovation of the facility. It can be also useful data for a methodology of foodservice facility design. (Korean J Community Nutr 16(1) : 98~112, 2011)

Multiple Class Symmetric G-networks with Phase Type Service Times

We consider a queueing network of symmetric G-queues with customers and signals. Since the seminal papers by Gelenbe in the early 1990s (Gelenbe, E. (1991) Product-form queueing networks with negative and positive customers. J. Appl. Probab., 28, 656–663; Gelenbe, E. (1993); G-networks with instantaneous customer movement. J. Appl. Probab., 30, 742–748; Gelenbe, E. (1993) G-Networks with signals and batch removal. Probab. Eng. Inform. Sci., 7, 335–342), generalized networks of queues have received considerable attention. But most papers assume to obtain a product form such that the service times follow exponential distributions. Here, we propose a new generalization of this model with phase-type service times. We also assume a new type of signal. When a signal enters a queue, it changes the phase of the customer in service if there is any. As usual, after completion of its service, a customer moves to another queue and may become a signal. We prove that the steady-state distribution for such a network of queues has a product form solution.

Bibliography on G-networks, negative customers and applications

The idea of G-networks with negative arrivals, as well as of the relevant product form solution including non-linear traffic equations, was first published by Erol Gelenbe in 1989. In contrast to classical queues and queueing networks, the arrivals of negative customers which remove customers from a non-empty queue upon their arrival are possible in G-networks. Negative customers with appropriate killing discipline can be used to model breakdowns and to model packet losses, etc., while triggered customer movement can represent control processes in networks. This work presents a bibliography 1 1 Similar bibliographies on retrial queues can be found in [J.R. Artalejo, A classified bibliography of research on retrial queues: progress in 1990–1999. Top 7 (1999) 187–211; J.R. Artalejo, Accessible bibliography on retrial queues, Mathematical and Computer Modelling 30 (34) (1999) 1–6; A. Gomez-Corral, A bibliographical guide to the analysis of retrial queues through matrix analytic techniques, Annals of Operations Research 141 (2006) 163–191; J.R. Artalejo, Accessible bibliography on retrial queues: progress in 2000–2009, Mathematical and Computer Modelling 51 (9–10) (2010) 1071–1081]. on G-networks, negative customers and the use of G-networks, negative customers and triggers to various performance analysis problems. We hope that we can include a majority of publications on G-networks. This bibliography in the BibTex format and a grouping by various themes is available online from http://www.hit.bme.hu/~do/G-networks/. We would encourage readers and researchers to send information to the author in order to make this bibliography as complete as possible.

Developing A Strategic Advertisement Method VUCMIN To Enhance The Desire Of Customers For Visiting Dealers

In this paper, the authors develop VUCMIN as a new strategic advertisement method designed to enhance the desire to visit dealers in the automobile industry. The proposed method uses video advertisement, and was developed based on scientific approaches and analyses that focus on the standard behavioral movements of customers who visit dealers when choosing an automobile. This method, which is based on the different approaches identified in target customer profiles, aims to increase the desire of customers to visit dealers. After creating this video advertisement, customers were verified as having a positive opinion towards visiting dealers with a plan to purchase the vehicle featured in the video.

The other side of enjoyment: short-circuiting marketing and creativity in the experience economy

The notion of experience economy asserts that staging the most mundane consumption practise as individualized entertainment adds value to the producer and consumer alike (Pine and Gilmore, 1999).This article questions this assumption of mere added value. Probing into an interplay between a marketing fantasy and a customer movement passionately engaging with its promise, it draws attention to a structural inability to enjoy the pleasures that the experience economy revolves around. Invoking the theoretical apparatus outlined by Lacan in The Other Side of Psychoanalysis (2007), the analysis sheds light on a social dynamics regulating this impossibility. This brings to the fore a range of ethical, political and economic consequences left largely untended by the literature, and dwelling on the other side of the lustrous enjoyment that so easily captures our imagination.

Dynamic Marketing Mix Allocation for Long-Term Profitability

To optimally allocate its marketing mix across customers, a firm needs to consider the evolution of its customers over time. Changes in the marketing environment, as well as intrinsic changes in preferences or needs, may discretely shift customers into different buying-behavior states. The ability to identify the dynamics in customer behavior and its drivers presents an opportunity for the firm to influence the movement of customers to more favorable states of buying behavior. Accordingly, we address the following managerial questions in this research: (1) how can the firm dynamically segment its customer base? (2) what are the short- and long-term effects of marketing activities? and (3) how should the firm allocate and target its marketing resources to maximize long-term profitability? To address these questions we propose a non-homogeneous hidden Markov model that accounts for dynamics in customer behavior, the long-term impact of marketing actions, and customer heterogeneity. We capture dynamics in customer behavior by allowing customers to transition over time among a set of latent states of buying behavior. We develop a unique and flexible approach to capture the enduring effect of marketing actions by incorporating a non-stationary transition matrix that is dynamically affected by these actions. To optimally allocate marketing activities, we formulate a dynamic programming approach which takes into account the evolution of customers' behavior. We apply the model in the context of direct-to-physicians marketing in a major pharmaceutical company. The results suggest that physicians transition among three behavioral states over time, showing a high degree of dynamics. Furthermore, the direct-to-physician marketing activities have varying degrees of short- and long-term effects that depend on the physician's prescription-behavior state. Specifically, we find that (i) both detailing and sampling have mostly long-term effects; (ii) detailing and sampling have a total duration impact of approximately 10 and 5 months, respectively; (iii) detailing is most effective as an acquisition marketing tool, whereas sampling is most effective as a retention tool. Using a counterfactual analysis, the optimization results show that by applying our dynamic marketing allocation approach, the firm could increase the number of prescriptions and its profits by as much as 51% and 80%, respectively. Moreover, our analysis suggests that the pharmaceutical firm should decrease its current detailing and sampling efforts by 30% and 20%, respectively. The integrative framework we propose provides important marketing implications for managing customers and maximizing long-run profitability.

An approach to robust network design in telecommunications

In telecommunications network design, one of the most frequent problems is to adjust the capacity on the links of the network in order to satisfy a set of requirements. In the past, these requirements were demands based on historical data and/or demographic predictions. Nowadays, because of new technology development and customer movement due to competitiveness, the demands present considerable variability. Thus, network robustness w.r.t demand uncertainty is now regarded as a major consideration. In this work, we propose a min-max-min formulation and a methodology to cope with this uncertainty. We model the uncertainty as the convex hull of certain scenarios and show that cutting plane methods can be applied to solve the underlying problems. We will compare Kelley, Elzinga-Moore and bundle methods.