Retail Site Selection Research Articles

Local Indicator of Spatial Agglomeration between Newly Opened Outlets and Existing Competitors on a Street Network

Distance from competitors is a key factor in retail site selection and profitability. To understand the locational tendency that each newly opened outlet locates close to or far from existing competitors in a target area, a specific method is needed. Hence, this study aims first to develop a statistical method to discover the local spatial associations between newly opened and existing point‐like outlets on a street network. We achieve this objective by extending the network local cross K function. The second objective is to evaluate the practicality of the proposed method by applying it to restaurants in a trendy district in Tokyo. Specifically, this study focuses on answering two questions: first, whether each newly opened restaurant is closely located to existing ones or not and, second, whether each existing restaurant attracts newly opened restaurants or not. The results show that the method is useful for revealing the location tendencies of retail outlets toward competitors.

Site Selection Improvement of Retailers Based on Spatial Competition Strategy and a Double-Channel Convolutional Neural Network

The issue of site selection has become a critical challenge in the development of the retail industry with the growth of the Chinese economy and the improvement in the level of household consumption. Previous studies have considered the area of stores as the main factor of retail competition; however, the actual business performance of different stores in these studies was ignored. In addition, few studies have considered the differences in the spatial distribution of the factors of site selection. In this study, we discuss the improvement of site selection of small retail shops. A spatial competition index model was proposed as one of the features in estimating region market potential, and a market demand regression model of a double-channel convolutional neural network (CNN) was constructed based on the spatial correlation range of features. The study area was Guiyang, China. The experiments were based on the monthly sales data of fast-moving consumer goods retail stores in Guiyang. On the basis of the estimated results of the model, 18 sites with high potential for market demand were recommended. The performance of the proposed model was the best among well-known regression methods. Moreover, in comparison with a single-channel CNN, the proposed model decreased the root mean square error by 22.61%. Evaluation results showed that the proposed method could provide effective decision support for the issue of retail site selection.

Spatial Models of Consumer Choice for Retail Outlets: Theory and Practice in Physics of Marketing

A theoretical review and an assessment of applications of the spatial models of consumer choice for retail outlets are presented here. As they are also called gravitational models in marketing, they remain relevant in today’s retail world despite the explosion of e-commerce. The major objective here is to emphasize the theoretical significance of these models, analyze their practicality in the market, and refer to their validity today. The focus would be on the original Reilly’s model of the early thirties of the last century, and the two most significant improvements over it, Converse’s model of the late forties, and Huff’s model of the early sixties. After a detailed exposition of these models and their applications, a critical analysis is introduced to address the retail site selection, radius of the trading area, limitations of the models, and some criticism.

Human elicited features in retail site analytics

Location selection is indispensable for a company or industry to survive for a long term. A strategically positioned retail location can increase the profitability and draw more customers for a company. Conventionally, a good location decision is associated with the relevant and significant location factors. However, integrating human subjective opinion as part of feature engineering process can be a challenge; there is no guarantee that these features can be optimum. In this light, this paper aims to investigate the impact of additional human elicitation features on the retail site selection model. This research focuses on retail site analytics to predict the sale of a telecommunication company in Malaysia. Apart from features such as geographical information, demographics and economics, this paper also includes the features determined by domain experts to investigate whether the human elicited features could improve the accuracy of sales estimation given a specific location. The findings of current work show that the additional of human elicited features successfully increase the model accuracy by 18.22%.

Geospatial Analytics in Retail Site Selection and Sales Prediction

Studies have shown that certain features from geography, demography, trade area, and environment can play a vital role in retail site selection, largely due to the impact they asserted on retail performance. Although the relevant features could be elicited by domain experts, determining the optimal feature set can be intractable and labor-intensive exercise. The challenges center around (1) how to determine features that are important to a particular retail business and (2) how to estimate retail sales performance given a new location? The challenges become apparent when the features vary across time. In this light, this study proposed a nonintervening approach by employing feature selection algorithms and subsequently sales prediction through similarity-based methods. The results of prediction were validated by domain experts. In this study, data sets from different sources were transformed and aggregated before an analytics data set that is ready for analysis purpose could be obtained. The data sets included data about feature location, population count, property type, education status, and monthly sales from 96 branches of a telecommunication company in Malaysia. The finding suggested that (1) optimal retail performance can only be achieved through fulfillment of specific location features together with the surrounding trade area characteristics and (2) similarity-based method can provide solution to retail sales prediction.

Exploring the use of managerial intuition in retail site selection

ABSTRACTAlthough many researchers have noted the widespread adoption of managerial intuition in choosing retail sites, few empirical studies consider the construct and those that do tend to examine it in isolation. Here, intuition is investigated in conjunction with the more quantitative variables typically used to model site selection and it is found to make a significant contribution to the choice process. Data collected from 253 individuals responsible for leasing/real estate decisions in the U.S. retail sector were analyzed. Findings confirm the widespread use of intuition in making site selection decisions, but further indicate that decision-makers may not be fully attuned to their decision making process. Counter to prior research, the use of intuition in this study does not differ based on the size of the retailer or the experience level of the decision-maker. The theoretical and managerial implications of these findings are discussed and ideas for future research are explicated.

Perceptions and Attitudes toward Online Mapping Services

ABSTRACTOnline mapping services, such as Google Maps and Bing Maps, have become increasingly popular. In addition to providing map, navigation, and directory information, such services provide third-party applications with a technology framework including geospatial-visualization capabilities. For instance, consumers often use location-based services and spatial decision support systems (SDSS) to locate the nearest restaurants, search for ideal homes, navigate specific routes, and effectively participate in car and bike sharing programs. Organizations utilize SDSS to perform retail site selection, manage global assets, and optimize supply chains. While geospatial-visualization is a vital capability of online mapping services, little is understood about how it impacts the acceptance of technology. Through a partial least squares analysis of 577 subject responses, this paper demonstrates that the user-acceptance of geospatial-visualization is influenced by utilitarian, hedonic, and cognitive measures. This paper concludes with a discussion of the implications of these results to research and practice.

Retail Store Location Selection Problem with Multiple Analytical Hierarchy Process of Decision Making an Application in Turkey

Retailers are the most powerful actors of the distribution channels with proximity to consumers and potential to create market. Retail store site selection is a strategic decision, both in terms of customer satisfaction and profitability of the company to changing market conditions and intense competition. Retail site selection decision is a long-term investment. Long-term investment decisions are difficult and costly to change. Retail store site selection is a process that must be followed carefully in order to pass ahead of the competition. In this process, election-related data collection and scientific analysis, and decisions must be made in the light of these data. Therefore, in the process of taking decisions managers use scientific methods to provide significant benefits in decision making. Administrators must also consider the combination of several criteria in the selection of retail store location. This problem is complex and has multi-criteria decision-making structure. Such a decision can be modelled analytically.In this study, selection of retail store concept in the literature has been identified, the studies that has been dealt with the problem of the selection of retail store, were examined. A retail store location selection criteria was defined. Selection model was established with the AHP method. The study was applied in the Aygın Milk Inc. The aim of the study, for the managers of the company to decide to use both qualitative and quantitative approach. The goal is to choose the most appropriate location of retail stores in real business life.

Using a Gis in Market Analysis for a Tourism‐Dependent Retailer in the Pocono Mountains

The role of Geographic Information System (GIS) technology in retail market area analysis and site selection is well established. Though approaches range from simple rings to complex multivariate models of customer loyalty patterns, they share the common understanding that market areas for retailers are relatively compact geographically. This assumption is much less appropriate for retail operations in tourist destinations, where stores rely heavily on visitors to the area who spend most of the year far removed from the retailer's location. Though location is still an important factor for such retailers, market area analysis based on geographic proximity is not as useful. However, GIS tools can still play a major role in market analysis for tourism‐dependent retailers. This study uses Pocono Mountain Treasures, a hypothetical retailer in Pennsylvania, to describe how a GIS application, Environmental Systems Research Institute's (ESRI) ArcGIS 9.2 system, can be used to help tourist‐dependent retailers serve existing customers more effectively and to target prospective new customers more precisely.

Standard segments for retail brands

Anyone responsible for managing and marketing a retail brand can learn a lot simply by classifying the retail chain's outlets into types (segments). The paper describes the development of a set of standard classifications covering all the branches of all major multiple retailers in Great Britain. Segmentz, as the system is called, can classify any retail outlet with a GB postcode on four dimensions. These are the catchment demographics, workplace context (working population and employment mix), local retailing environment and strength of competition nearby. Analyses are then presented on three dimensions (competition being difficult to standardise). Applications include retail site selection, merchandising, monitoring trends in sales and customer preferences, brand positioning and competitor analysis. Standard segments are offered as a simple but effective tool for coordinating the wide range of activities encompassed in modern branded retailing.

International Real Estate Review

This paper describes and applies the weighted least squares (WLS) technique that corrects for spatial autocorrelation in the residuals of hedonic regressions. Most empirical studies to date have focused on spatial autocorrelation in the housing market, i.e., single family home valuation. This study focuses on mall stores within shopping centers, with an emphasis on retail site selection within the mall.

Multi-unit retail site selection processes: incorporating opening delays and unidentified competition

Multiunit site selection models have long ignored the impact of time delays in store openings. To determine the effect of this limitation and to identify a solution, we compare three multiunit site selection procedures. The first is a sequential procedure that selects sites and then opens them one at a time. The second is a global approach that selects all future sites in a single decision process. Third, is a proposed anticipated-delay procedure that incorporates opening delays, firm planning horizons and discount rates in determining appropriate sites for multiunit retail systems. We further compare the results of these site-selection procedures to similar procedures that incorporate leakage to unidentified competitors when seeking locations for new outlets. We make the comparisons based upon discounted revenues by using a two-party, game-like simulation. The simulation estimates revenue results under the various alternatives while allowing for reactive and preemptive competitive store openings. Our results indicate that the anticipated-delay procedure incorporating leakage produces a significantly better revenue stream than either the standard sequential or global approach procedures.

An Efficient Search Strategy for Site‐Selection Decisions in an Expert System

This paper describes an algorithm for spatial search, which is used in an expert system for site selection. The algorithm, named ProfMat, is able to find the best site in the area of interest even when the number of possible sites is large and many decision criteria are involved. Compared to commonly used search procedures, ProfMat improves the efficiency of spatial search in two ways. First, the best site is identified through an iterative rather than a linear process of selection and evaluation of optional sites. Second, an area is searched by narrowing down the focus to increasingly smaller areas and, thus, sites are evaluated as much as possible groupwise. The ProfMat procedure is illustrated by analyzing the problem of retail site selection. A comparison with alternative search procedures shows that ProfMat considerably reduces the evaluation costs needed to find the best site. The implementation of the algorithm in an expert system shows how ProfMat can be used in combination with specialist's knowledge to solve site‐selection problems. The efficiency of the procedure allows considering large sets of optional sites, so that it may improve the quality of the outcome.

Book Review: New Developments in Retail Trading Area Analysis and Site Selection

Book Review: New Developments in Retail Trading Area Analysis and Site Selection

New Developments in Retail Trading Area Analysis and Site Selection

New Developments in Retail Trading Area Analysis and Site Selection

The Utility of Retail Site Selection for the Public Library. William C. Robinson

Previous articleNext article No AccessReviewsThe Utility of Retail Site Selection for the Public Library. William C. Robinson Guy GarrisonGuy Garrison Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Library Quarterly Volume 47, Number 1Jan., 1977 Article DOIhttps://doi.org/10.1086/620639 Views: 1Total views on this site Copyright 1977 The University of ChicagoPDF download Crossref reports no articles citing this article.

Geography and the Business of Retail Site Evaluation and Selection

works in regard to choice of retail locations, the best of which is the work of Cohen and Applebaum [1]. Model builders, both academic and business, have attempted to predict consumer behavior and thereby to provide a framework for evaluating retail locations and predicting retail sales. Theoretical and applied location studies are good only if they are useable and used. If they are to be used, they must obtain a physical expression of the activity recommended in the form of a retail facility on a site. The site is the critical element in implementation of location studies, and site deserves the attention of the theorist as well as the

The Problem of Retail Site Selection.

The Problem of Retail Site Selection.

Book Review: The Problem of Retail Site Selection

Book Review: The Problem of Retail Site Selection

The Problem of Retail Site Selection

The Problem of Retail Site Selection