The Influence of Customer Movement between Sales Areas on Sales Amount: A Dynamic Bayesian Model of the In-store Customer Movement and Sales Relationship

Kaneko, Yuta; Miyazaki, Shuichi; Yada, Katsutoshi

doi:10.1016/j.procs.2017.08.225

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…Associated research has revealed that in the offline sales scenario, the sale amount and rental are closely related to customer mobility [13,14], which may reflect customers' tendencies. Therefore, a prerequisite for increasing profits is understanding the mobility of customer flows and their influencing factors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Finally, the influencing factors were quantified via a linear multivariable regression model, where customer flow density on the weekend was the dependent variable and the independent variables included locational and social factors. To determine the factors that may influence customer flows to stores, we needed to choose the influencing factor first, which was based on previous research [3,4,13,14,18,50,52] and online comment information (dianping.com). Here, we selected 9 factors, among which the first eight were locational variables and the last one was a social one.…”

Section: Analysis Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

What Influences Customer Flows in Shopping Malls: Perspective from Indoor Positioning Data

Pei

Liu

Shu

et al. 2020

IJGI

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Offline stores are seriously challenged by online shops. To attract more customers to compete with online shops, the patterns of customer flows and their influence factors are important knowledge. To address this issue, we collected indoor positioning data of 534,641 and 59,160 customers in two shopping malls (i.e., Dayuecheng (DYC) in Beijing and Longhu (LH) in Chongqing, China) for one week, respectively. The temporal patterns of the customer flows show that (1) total customer flows are high on weekends and low midweek and (2) peak hourly flow is related to mealtimes for LH and only on weekdays for DYC. The difference in temporal patterns between the two malls may be attributed to the difference in their locations. The customer flows to stores reveal that the customer flows to clothing, food and general stores are the highest; specifically, in DYC, the order is clothing, food and general, while in LH, it is food, clothing and general. To identify the factors influencing customer flow, we applied linear regression to the inflow density of stores (customers per square meter) of two major classes (clothing and food stores), with 10 locational and social factors as independent variables. The results indicate that flow density is significantly influenced by store location, visibility (except for food stores in DYC) and reputation. Besides, the difference between the two store classes is that clothing stores are influenced by more convenience factors, including distance to an elevator and distance to the floor center (only for LH). Overall, the two shopping malls demonstrate similar customer flow patterns and influencing factors with some obvious differences also attributed to their layout, functions and locations.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Analysis Proceduresmentioning

confidence: 99%

What Influences Customer Flows in Shopping Malls: Perspective from Indoor Positioning Data

Pei

Liu

Shu

et al. 2020

IJGI

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“…XiaodanYua presented a presented a sales furcating for magazine and newspaper using the support vector regression [1]. YutaKanekoa, et al presented a dynamic Bayesian model for identifying the relationship between the movement of in-store customer and sales [2]. D. Asir et al presented a forecasting model to forecast the sales data.…”

Section: Related Workmentioning

confidence: 99%

“…Initially, the training dataset is given to the each algorithm and the forecasting model is developed then the test dataset is given to the forecasting model to evaluate the model as shown in Figure 3. The forecasting model is evaluated in terms of mean absolute error (MAE) and root mean squared error (RMSE) as expressed in the Equation 1and (2).…”

Section: Experimental Setup and Proceduresmentioning

confidence: 99%

Machine Learning based Business Forecasting

Singh¹,

Leavline²,

Muthukrishnan³

et al. 2018

IJIEEB

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The business sectors directly contribute to the growth of any nation. Moreover, the business is an activity of producing, buying, and selling the goods and services to generate the money. The business directly involves in the gross domestic product (GDP). The business forecasting is the activity of predicting or estimating the feature position of the sales, expenditures, and profits of any business. However, the business forecasting helps to the business sectors for planning, decision making, resource utilization, business success, etc. Therefore, business forecasting is a pressing need for the growth of any business. In recent past, many researches attempt to carry out the business forecasting using different tools. However, this paper presents the business forecasting for sales data using machine learning technique and the obtained results are presented and discussed..

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“…Precision marketing refers to the marketing targeted at consumers with strong willingness to consume. To realize precision marketing, it is necessary to create a complete consumer database, identify the enthusiastic consumers, and implement consumer-centered marketing activities [13][14][15]. Lin et al [16] mentioned that, during precision marketing, enterprises could establish a precise and controllable mechanism for marketing and communication based on consumption actions and results.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation Strategy for Commercial Precision Marketing Based on Artificial Neural Network

Cheng¹

2020

RIA

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With the proliferation of the Internet and smart mobile terminals, great progress has been made in the precision placement and benefit-sharing mechanism of commercial advertisements. Meanwhile, media marketing has become increasingly in-depth and precise. So far, mature theories have been proposed on consumer value and precision marketing. But further research is needed to mine the value from the big data on commercial precision marketing. To improve the accuracy of commercial precision marketing, this paper presents an evaluation index system (EIS) for commercial precision marketing based on improved attention-interest-desire-memory-action (ADIMA) model, and determines the principal evaluation indices through principal component analysis (PCA). Next, an artificial neural network (ANN) was established to evaluate commercial precision marketing, and optimized through k-means clustering (KMC). Finally, the optimized model was realized on MATLAB. The proposed EIS and ANN were proved scientific and effectiveness through simulations. The research results provide a reference for the application of the ANN in other fields of marketing.

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The Influence of Customer Movement between Sales Areas on Sales Amount: A Dynamic Bayesian Model of the In-store Customer Movement and Sales Relationship

Cited by 8 publications

References 19 publications

What Influences Customer Flows in Shopping Malls: Perspective from Indoor Positioning Data

What Influences Customer Flows in Shopping Malls: Perspective from Indoor Positioning Data

Machine Learning based Business Forecasting

An Evaluation Strategy for Commercial Precision Marketing Based on Artificial Neural Network

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