ESTIMATION OF THE CORRELATION BETWEEN MARKETING AND SPATIAL INDICATORS
В статье рассмотрено одно из методологических направлений концепции маркетинга пространственного взаимодействия- экономическое оценивание взаимосвязи маркетинговых и пространственных показателей. Представлена классификация и ранжирование данных показателей, сформирована метрика показателей.
In this article the author considers one of methodological aspects of the concept of marketing of spatial interaction, namely, economic estimation of the correlation between marketing and spatial indicators. These indicators were classified and ranged on the base of the results of the expert survey. The metric of marketing and spatial indicators is suggested to be used in practice.
Ключевые слова: концепция маркетинга пространственного взаимодействия, маркетинговые показатели, пространственные показатели, метрика маркетинговых и пространственных показателей.
Key words: concept of marketing of spatial interaction, marketing and spatial indicators, metrics of marketing and spatial indicators.
The basis of the new paradigm of spatial economics was developed by Russian academician A.G. Granberg in 2009 . Contemporary market conditions are not as simple, with the appearance of new means of media and development of virtual space as a key area of marketing interaction, and with growing social differentiation in the regions of Russia and of the whole world. The reason is the necessity of taking into consideration the spatial factors to make business in the region prosperous in the long terms.
This is why marketing should be also regarded including spatial aspects. So there should be further evolution of marketing concepts to develop marketing of spatial interaction. The key objective of this evolution is to organize marketing activity effectively in terms of spatial economics. To reach this goal there several tasks should be performed: to form a base of methodology, theory and terminology of the concept of marketing of spatial interaction, to model marketing interactions including spatial aspects, to study and develop the concept of marketing space, to identify its structure and role in functioning of the other spaces, to develop a model of marketing space and to find consistent patterns within it to create the metrics of marketing and spatial indicators.
The author considers that economic space is a space of relations, which appear in the process of social production and reproduction.
Marketing space can be regarded as combination of social and material objects, which relate to each other in either supporting or acting in marketing processes and making a direct or indirect impact on their results. Marketing space is considered to perform an integrating function relative to economic subspaces.
Concerning the consistent patterns within marketing space and its role in the functioning of other spaces Professor of marketing of SPbSUE G.L. Bagiev and the author have suggested the hypothesis that there are interrelation and interaction between key marketing and spatial indicators. To prove this idea the author developed an algorithm of economic estimation of the correlation between marketing and spatial indicators, and then used it. Firstly, the algorithm of this method was designed and the data was collected.
Marketing and spatial indicators were selected in steps. In the first step the author made a theoretical analysis, selected 17 key marketing and spatial indicators. A main criterion of selection was availability of official data. Also it was important to characterize several economic subspaces: financial, innovative, technological, investment, commercial, demographical, price.
Then the author collected the data on all regions of Russia in 2011 from the official web-site of Federal State Statistics Service of the Russian Federation. A criterion for the further selection was 2 principles. The first one is that factors mustn’t have close correlation to each other. The second principle is that the model must contain only significant factors, which form the result.
This is why in the second step the author carried out a quantitative analysis and created a matrix of pair correlation coefficients. The main marketing indicator was development index of commodities category. This indicator was calculated for the durables: personal computers (PC), mobile phones, cars.
As a result the close correlations between the resulting marketing indicator, a development index of commodities in the PC category (Y) and the following spatial factors were identified:
- 1. development index of commodities in the mobile phone category - xb
- 2. average cash income per capita - x2,
- 3. level of subsistence minimum of income per capita - x3,
- 4. share of household consumption expenditure on food and nonalcoholic beverages- x5.
The next step was regression analysis which was carried out using Microsoft Excel and SPSS 17.0. The latter is considered to be more reliable, powerful and effective.
SPSS program excluded two indicators average cash expenditures per capita, retail trade turnover from the model on the first stage of the data processing. As they are not substantial and significant for the resulting indicator. Exception of these two factors reflects that development index of commodities in the PC category depends directly on the average cash income per capita, which reflects the life level in the region. And indicator the average cash expenditures per capita reflects the preferences of the households to save or to spend money, so this indicator makes an indirect impact on the result. The same logic is for the indicator retail trade turnover.
The regression equation was found to be:
Y = 0,564 + 0,469xi + 0,000009197x2 + 0,00004234x3 - 0,017x5
The statistical model of this study and all parameters of the equation can be regarded as reliable and significant because they were checked by calculating multiple regression coefficients, using Fisher’s F-test, the Student's t-test for all parameters, and the Durbin-Watson statistic test.
Thus marketing indicator, development index of commodities in the PC category depends on development index of commodities in the mobile phone category, average cash income per capita, level of subsistence minimum income per capita, share of household consumption expenditure on food and nonalcoholic beverages.
The method of estimation of the correlation between marketing and spatial indicators can be considered as a very useful and effective tool for the concept of marketing of spatial interaction. As without using this method it was impossible to consider the existence of correlations between some indicators, for example, between the development index of commodities in the mobile phone category, the level of subsistence minimum income per capita and the development index of commodities in the PC. Also, without using this instrument it is difficult to identify the degree of these correlations, and to determine which spatial indicators have a direct influence on marketing indicators, and which ones have an indirect influence on them.
After it was proven that there is a correlation between marketing and spatial indicators the expert survey was carried out. Its main aim was to classify the indicators and to range them. As a result of the expert survey the metric of marketing and spatial indicators was formed. This metric can be used to analyze the current and future situation on the consumer goods market, identifying hidden opportunities and risks, and also as an instrument for companies’ upper management to make effective decisions concerning business regional expansion.
This metric includes the following indicators.
- 1. Consumers’ loyalty.
- 2. Consumers’ satisfaction.
- 3. Sales profitability.
- 4. Market share (in terms of money).
- 5. Clients’ profitability.
- 6. Profitability of marketing investments.
- 7. Product sampling intensity.
- 8. Price elasticity of demand.
- 9. Profitability of marketing investments in the impact of mass media.
- 1. Average cash income per capita.
- 2. Structure of consumption expenditure of households.
- 3. Distribution of retail trade turnover by forms of trafficking.
- 4. Innovative activity of organizations.
- 5. The retail trade turnover per capita.
- 6. Commodity structure of export and import.
- 7. The economically active population
- 8. Expenditure on technological innovation.
- 9. Actual final consumption of households per capita.
- 10. Share of households which own a PC and have access to the Internet in the whole quantity of the households in the region.
- 11. E-readiness.
- 12. Level of unemployment.
- 13. Level of subsistence minimum income per capita.
- 14. Share of household consumption expenditure on food and nonalcoholic beverages
Marketing and spatial indicators:
- 1. Development index of trade mark.
- 2. Development index of commodity category.
- 3. Volume of innovative products, services.
Concerning the challenges of the concept of marketing of spatial interaction given above the method of economic estimation of the correlation between marketing and spatial indicators has not only practical significance for business but also a real contribution for the development of this concept.
Thus, the equation given above can be regarded as a useful instrument to predict the volume of the distribution of PCs and to range regions of Russia concerning their attractiveness for business expansion. This equation can be also used by firms selling not only PCs but also mobile phones, as a correlation between the volumes of consumption of these products was identified.
Besides, by analogy with PCs this equation can be adapted for the other durable goods, for example, for refrigerators. And firstly the same indicators can be included in the statistical model. Then other, similar indicators may be applied which reflect the life level in the region, the level of consumption.
So the method of economic estimation of the correlation between marketing and spatial indicators proved its reasonability and can be used in order to find correlations between other marketing and spatial indicators and to identify more consistent patterns within marketing space. But, in fact, the next conclusion which can be made from the equation obtained in this study is that marketing space really performs the integrating function relative to all other economic subspaces. However this thesis requires further analysis and study.
This approach really works as it gives a real result which can be used in business. Identified correlations can be the base of more effective metrics of marketing and spatial indicators.
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