بازاريابي مكان محور بانك ها با استفاده از مدل جديد ژئوماركتينگ مبتني بر زيرساخت داده مكاني(SDI) (مطالعه موردي: شهر سمنان)

Location-Based marketing of banks by using a new Geomarketing model based on Spatial Data Infrastructure (Case study: Semnan city)

در دﻧﯿﺎي اﻣﺮوز ﮐﻪ ﭘﻮﯾﺎﯾﯽ دادهﻫﺎ از اﻫﻤﯿﺖ ﺑﺴﯿﺎري ﺑﺮﺧﻮردار اﺳﺖ، اﺳﺘﻔﺎده از زﯾﺮﺳﺎﺧﺖ دادهﻣﮑﺎﻧﯽ (SDI)ﻣﯽﺗﻮاﻧﺪ ﺑﺴﺘﺮي را ﺑﺮاي ﺑﻪ اﺷﺘﺮاك ﮔﺬاري دادهﻫـﺎي ﻣﮑـﺎﻧﯽ ﺑـﻪوﺟﻮد آورد. ﺟـﺎﯾﮕﺰﯾﻦ ﻧﻤـﻮدن SDIﺑﻪﺟﺎي GIS در ژﺋﻮﻣﺎرﮐﺘﯿﻨﮓ ﻓﺼﻞ ﺟﺪﯾﺪي از ﺑﺎزارﯾﺎﺑﯽ اﻟﮑﺘﺮوﻧﯿﮑﯽ ﺑﺮ ﻣﺒﻨﺎي اﻃﻼﻋﺎت ﻣﮑـﺎﻧﯽ را اﯾﺠﺎد ﻣﯽﻧﻤﺎﯾﺪ. ژﺋﻮﻣﺎرﮐﺘﯿﻨﮓ ﻣﺒﺘﻨﯽ ﺑﺮ SDIﻧﻘﺎﯾﺺ و ﺿﻌﻒﻫﺎي ﻋﺪم ﺑﻪﻫﻨﮕﺎم ﺑﻮدن ﻻﯾﻪﻫﺎي اﻃﻼﻋـﺎت ﻣﮑﺎﻧﯽ در ژﺋﻮﻣﺎرﮐﺘﯿﻨﮓ ﻣﺒﺘﻨﯽ ﺑﺮ GISرا ﺑﺮﻃﺮف ﻣﯽﺳﺎزد. در اﯾﻦ ﭘﮋوﻫﺶ ﻣﺪل ﺟﺪﯾـﺪي از ﺑﺎزارﯾـﺎﺑﯽ ﻣﮑﺎنﻣﺤﻮر اراﺋﻪ ﺷﺪه ﮐﻪ ﺑﺮاي اوﻟﯿﻦ ﺑﺎر از زﯾﺮﺳﺎﺧﺖ دادهﻫﺎي ﻣﮑﺎﻧﯽ ﺑﻬﺮه ﻣﯽﮔﯿﺮد. در اﯾﻦ راﺳـﺘﺎ ﺑـﺮ اﺳﺎس دادهﻫﺎي ﻣﮑﺎﻧﯽ ﻣﻮﺟﻮد اﺑﺘﺪا ﺷﻬﺮ ﺳﻤﻨﺎن ﺑﻪ 139ﺣﻮزه آﻣﺎري ﯾـﺎ ﻣﻨﻄﻘـﻪ ﺷـﻬﺮي ﺗﻘﺴـﯿﻢ ﺷـﺪه اﺳﺖ. ﺑﺎ ﺑﻪ اﺷﺘﺮاك ﮔﺬاري ﭘﺎﯾﮕﺎهﻫﺎي داده دﺳﺘﮕﺎهﻫﺎي اﺟﺮاﯾﯽ ﻣﺘﻮﻟﯽ در ﺑﺴـﺘﺮ زﯾﺮﺳـﺎﺧﺖ دادهﻣﮑـﺎﻧﯽ ﺷﺎﺧﺺﻫﺎي اﺻﻠﯽ ﺟﻤﻌﯿﺘﯽ و اﻗﺘﺼﺎدي ﻣﺮﺗﺒﻂ ﺑﺎ ﺑﺎزارﯾﺎﺑﯽ ﺑﺎﻧﮏﻫﺎ اﻧﺘﺨﺎب ﮔﺮدﯾﺪ. از ﻃﺮﯾﻖ روشﻫـﺎي آﻣﺎري ﺿﺮﯾﺐ ﻫﻤﺒﺴﺘﮕﯽ و رﮔﺮﺳﯿﻮن ﭼﻨﺪ ﻣﺘﻐﯿﺮه ﺷﺎﺧﺺﻫﺎي ﻣﺆﺛﺮ ﺑـﺮ ﺗﻌـﺪاد ﺷـﻌﺐ ﺑـﺎﻧﮑﯽ ﻣﺤﺎﺳـﺒﻪ ﺷﺪه اﺳﺖ. ﺷﺎﺧﺺ ﺗﻌﺪاد ﮐﺴﺐ و ﮐﺎر ﺑﺎ ﺿﺮﯾﺐ 0.598 ﺑﯿﺸﺘﺮﯾﻦ ﺗﺄﺛﯿﺮ را در ﺗﻌﺪاد ﺷﻌﺐ ﺑﺎﻧﮏﻫﺎ در ﻫﺮ ﻣﻨﻄﻘﻪ داﺷﺘﻪ اﺳﺖ. در ﻧﻬﺎﯾﺖ ﺑﺎ اﺳﺘﻔﺎده از ﺷﺎﺧﺺﻫﺎي ﺟﻤﻌﯿﺘﯽ، اﻗﺘﺼﺎدي و ﺟﻐﺮاﻓﯿﺎﯾﯽ ﻣﻮرد اﺷﺎره و ﺑﻬﺮهﮔﯿﺮي از روش ﺗﺎﭘﺴﯿﺲ، ﻣﻨﺎﻃﻖ ﺷﻬﺮ ﺳﻤﻨﺎن از ﻟﺤﺎظ ﺷﺎﺧﺺﻫﺎي ﺑﺎزارﯾﺎﺑﯽ ﺑﺎﻧﮏﻫـﺎ اوﻟﻮﯾﺖﺑﻨـﺪي ﺷﺪ ﮐﻪ ﻣﻨﻄﻘﻪ 70داراي اوﻟﻮﯾﺖ ﻧﺨﺴﺖ ﻣﯽﺑﺎﺷﺪ. ﻧﺘﺎﯾﺞ ﻣـﺪل ﺗﺼـﻤﯿﻢﮔﯿﺮي ﭼﻨﺪﺷﺎﺧﺼـﻪ ﺑـﻪﻋﻨﻮان ﯾـﮏ ﻣﺘﻐﯿﺮ ﻣﮑﺎﻧﯽ واﺑﺴﺘﻪ در ﻣﺪل ژﺋﻮﻣﺎرﮐﺘﯿﻨﮓ ﻣﺒﺘﻨﯽ ﺑﺮ SDI ﻟﺤﺎظ ﮔﺮدﯾﺪه اﺳﺖ.

In today's world where dynamics of data are so important, using of Spatial Data Infrastructure (SDI) can provide a platform for spatial data sharing. Replacing SDI with GIS in Geomarketing Creates new chapter on Spatial Information-Based e-marketing. SDI-based Geomarketing fixes the flaws and shortcomings of spatial information layers in GIS-based Geomarketing. In this research, a new model of location-based marketing is presented, which uses spatial data infrastructure for the first time. In this regard, based on the available spatial data, Semnan city is divided into 139 statistical areas or urban areas. By sharing the databases of the related executive agencies in the context of spatial data infrastructure, the main demographic and economic indicators related to bank marketing were selected. Through statistical methods of correlation coefficient and multivariate regression, the effective indicators on the number of bank branches have been calculated. The number of businesses index with a coefficient of 0.598 had the greatest impact on the number of bank branches in each region. Finally, using the mentioned demographic, economic and geographical indicators and using the TOPSIS method, the regions of Semnan city were prioritized in terms of banks' marketing indicators, and region 70 has the first priority. The results of the Multiple Attribute Decision Model are considered as a dependent spatial variable in the SDI-based Geomarketing model.