Investigating Effective Indicators in Locating Banks Using a New Geomarketing Model Based on Spatial Data Infrastructure (Case Study: Semnan City)

In today 's world, optimizing a successful business depends on using all the resources that make it superior to its competitors. Location-based marketing or Geomarketing leads to critical and effective decisions by analyzing different geographical areas. Spatial information systems marketing is more agile in strategic decision making. In this age where data dynamics are so important, the use of spatial data infrastructure (SDI) can create a platform for spatial data sharing. Spatial Data Infrastructure (SDI) with instant sharing of spatial data can provide a dynamic platform. SDI-based Geomarketing fixes the flaws and shortcomings of spatial information layers in GIS-based Geomarketing. The main advantage of this model compared to previous models, in addition to information dynamics, is that there is no need for an operator to record and store information and produce layers of location-based information in alternating time periods. This is an applied research in terms of purpose and is based on a descriptive method that includes a set of methods that aim to describe the conditions or phenomena under study. In terms of implementation, part of this research is collected in the form of libraries and documents using the theoretical foundations and background of previous research, and the other part is done experimentally and by collecting information from the base statistical reference authorities.Accordingly, in this research, a new model of location-based marketing is presented, which uses spatial data infrastructure for the first time. In this article, we seek to answer the questions of whether the use of Geomarketing based on spatial data infrastructure has an advantage over GIS-based location-based marketing? Is it possible to prioritize the optimal areas by sharing important indicators from different databases of executive agencies in the field of marketing of Semnan banks? In this regard, using this model and based on the data available in 4 databases of related executive agencies, the city of Semnan is divided into 139 urban areas or statistical areas. Afterwards, using the geoportal infrastructure of Semnan province spatial data located in the Management and Planning Organization of Semnan province, the desired registration information layers were shared and model’s maps were extracted. Subsequently, by examining 150 demographic and economic indicators and examining their correlation coefficient with the number of bank branches, it was found that the indicators of literacy rate, household size, population density, distance from the city center, number of important businesses, income decile, number of apartments and number of schools are most relevant with the number of bank branches in each region. Then a model was estimated using multivariate regression. In this model after estimating the model coefficients, the number of businesses index with a coefficient of 0.598 has the greatest impact on the number of bank branches. According to the results of this model, area No. 62 of Semnan city has the most favorable conditions in terms of banking marketing indicators. So the main advantage of this model compared to previous models, is that there is no need for an operator to record and store information and produce layers of location-based information in alternating time periods in addition to information dynamics. In this model, a dynamic model can be achieved by using dynamic information by sharing layers of spatial information in the context of spatial data infrastructure, in addition to maintaining the intellectual property of information. This research is supported by the GIS unit of the Management and Planning Organization of Semnan Province in Iran.