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This paper aims to study and compare variables such as location, industry, socio-demographics and business factors of several companies located in Western Macedonia.

Research method is based on primary data the authors collected through questionnaires and secondary data obtained from State Statistical Office. Besides, the research sums up data classification of companies in the aspect of geomarketing and how location determinants and geo-information of certain cities can influence their business development and decision-making. Lastly, the results of data and Structural Equation Modeling integration are presented.

Since Republic of Macedonia is more than 25 years in transition, experts have often foreseen that companies in their country most of the time make intuitive decisions and do not take into account many elements during decision-making process in the business aspect. But on the other hand, results reveal interesting facts about companies involved in their research. For a majority of companies, the influence of geomarketing on business development is positive as long as during decision-making process its determinants have been taken into consideration and reviewed.

The paper helps the entrepreneurs to develop their businesses and decision-making process based on geomarketing determinants.

This is the first paper that investigates and compares variables such as location, industry, socio-demographics and business factors of companies located in Macedonia.

A key pre‐requisite of locational decision‐making in retailing is appropriate sources of data. These have become increasingly available from a number of internal and external sources in recent years. Coupled with concomitant decreases in the cost of technology, locational decision‐making can now, more than ever, be based on more than subjective practices alone. This paper aims to assess how the use of data varies across four named retail and service sectors, an area of research that has hitherto been neglected. A postal questionnaire was distributed to approximately 300 locational planning managers to ascertain the use of data in decision‐making. Analysis of variance (ANOVA) was used to examine how the use of 21 named datasets varied by sector. A number of significant differences in data usage and sharing emerged and are reported here. It is concluded that data use is by no means uniform across different retail and service types, and that future research that addresses inter‐sectoral differences in locational planning practices would be appropriate.

This paper aims to extend a widely used stochastic model of purchase loyalty to include covariates such as demographics, psychographics and geodemographics. Potentially, this allows covariates to explain variations in brand performance measures (BPMs) such as penetration/reach, average purchase frequency, sole buying, share of category requirements, repeat purchase and so forth. The result is to integrate consumer-based segmentation into previously unsegmented stochastic models of brand performance.

This paper describes a model for predicting BPMs. Covariates are then introduced into the model, with discussion of model specification, model estimation, overall model assessment, and the derivation of generalised theoretical BPMs. The outcome is a practical procedure for behavioural loyalty segmentation.

The implications for strategy and management in applying covariates to the BPMs are considerable. Where there are concentrations of consumers with high repeated purchase/consumption, then many aspects of the marketing mix will be affected. An investigation of the role of covariates in understanding BPMs in the laundry detergent market is presented as an example, and ways for market analysts to display results are demonstrated.

Despite the fact that BPMs are the best operationalisation of behavioural loyalty, until now there has not been a model to evaluate the impact of consumer characteristics as covariates on these BPMs. This paper's original contribution includes a model that fits covariates to the BPMs. New statistical and graphical methods are described. Computer software for fitting the model and generating the output is available from the authors.

Sound target marketing leads to winning business strategies. While market segmentation is an intriguing academic concept, most B2B practitioners struggle with the design and implementation of such initiatives. This paper aims to illustrate an effective strategic segmentation process in a high‐technology market context.

Bonoma and Shapiro's nested model – consisting of geodemographics, operating variables, purchasing approaches, situational factors and characteristics of the buyer – is used as a conceptual framework for market segmentation analysis. The model is applied to Citrix Systems as a way of finding new business opportunities in the desktop application streaming market.

In this study, 17 potential segmenting variables within the five major levels are examined with an initial emphasis on firmographics and technology. Census data identified market priorities based on establishment size, key sectors and geographic sales territories.

A four‐stage segmentation plan consisting of corporate commitment, research/refinement, implementation and evaluation/enhancement is proposed and discussed. Strategic planning lessons and research extensions are offered.

While the work on business segmentation has proliferated over the past 25 years, there has been a paucity of practical applications on how to conduct segmentation analysis successfully in technology markets. This paper provides an important roadmap for marketers to enhance segmentation initiatives via a comprehensive application and analysis of a leading global company.

Twenty-first century online retailing has reshaped the retail landscape. Grocery shopping is emerging as the next fastest growing category in online retailing in the UK, having implications for the channels we use to purchase goods. Using Sainsbury’s data, the authors create a bespoke set of grocery click&collect catchments. The resultant catchments allow an investigation of performance within the emerging channel of grocery click&collect. The paper aims to discuss these issues.

The spatial interaction method of “Huff gravity modeling” is applied in a semi-automated approach, used to calculate grocery click&collect catchments for 95 Sainsbury’s stores in England. The catchments allow investigation of the spatial variation and particularly rural-urban differences. Store and catchment characteristics are extracted and explored using ordinary least squares regression applied to investigate “demand per day” (a confidentiality transformed revenue value) as a function of competition, performance and geodemographic factors.

The findings show that rural stores exhibit a larger catchment extent for grocery click&collect when compared with urban stores. Linear regression finds store characteristics as having the greatest impact on demand per day, adhering to wider retail competition literature. Conclusions display a need for further investigation (e.g. quantifying loyalty).

New insights are contributed at a national level for grocery click&collect, as well as e-commerce, multichannel shopping and retail geography. Areas for further investigation are identified, particularly quantitatively capturing brand loyalty. The research has commercial impact as the catchments are being applied by Sainsbury’s to decide the next 100 stores and plan for the next five years of their grocery click&collect offering.

The purpose of this research was to investigate the attendance range of spectators at an English Football Association Premier League club. The numbers of spectators in both percentage and absolute terms were calculated and allocated to three segments (heavy, medium, light attendance). The findings were compared to an earlier reported study in the United States and marketing recommendations based on related studies and industry expertise were proposed for those involved in live spectator sports.

This paper seeks to explore the applicability and implications of Bourdieu's field‐capital theory for marketing using original research with a typical European society. Bourdieu's field‐capital theory proposes that people acquire economic, social and cultural capital which they deploy in social arenas known as “fields” in order to compete for positions of distinction and status. This exploratory study aims to examine how Bourdieu's theory may explain competitive behavior in fields of interest to marketers.

A total of 61 in‐depth interviews were completed with respondents that were representative of each of 61 geodemographic “types” – clusters that enable marketers to segment an entire population.

The findings suggest that examining human behaviour through the lens of field and capital theory highlights the importance of the competition motive in explaining consumers' behaviour. New “fields” were identified which seem to have assumed primary importance, particularly in middle‐class people's lives.

Viewing consumer behaviour as social competition implies that new segmentation approaches may yield successful marketing outcomes, and opens consumer psychology and behaviour itself to new interpretations.

Very few research papers that apply field‐capital theory to marketing are present in the literature. It is hoped that this work addresses an important area, and one that is particularly prevalent in twenty‐first century consumerism.

Focuses on the growth and future direction of direct marketing from the point of view of “user” companies. Describes the most significant findings from a recent empirical study of consumer reactions to being directly targeted. Highlights important consumer concerns with regard to privacy and ethical issues. Given that the long‐term success and prosperity of the industry will be closely related to its image among consumers, guidelines are developed for companies using direct marketing as to how they can positively promote industry growth and reduce consumer concerns in the future. Briefly describes the self‐regulatory bodies and codes of practice relevant to direct marketers within the UK.

A new census was carried out in 1991 and local authorities will be keen to analyse the changes which have occurred since the last census. Their analysis will be helped by the introduction of geographical information systems which have been recommended by the Chorley Report. Examines the findings of ten in‐depth interviews with local authority officers within planning departments.

Using basket analysis, a methodology is presented to investigate the healthiness of particular household diets. The analysis was applied to “Homescan” panel data relating to 11,000 households. This breakdown was then cross‐related to geodemographic classifications using “MOSAIC” groups. The degree or extent of unhealthiness was determined on the basis of percentage swing away from recommended dietary patterns in terms of indicator foods belonging to five groups (dairy, meat, vegetables, fruit and bread and cereals). The consumption of fats was also monitored. It was found that 8 per cent of households had diets that were labelled “unhealthy”, based on standard recommendations for healthy eating. A further 20 per cent of households had “relatively unhealthy” diets. The majority of these households were shown to belong to the “suburban semis” group in the MOSAIC classification.