Publication: Location-based performance management of sales teams
dc.contributor.coauthor | Kastan, Hande | |
dc.contributor.coauthor | Kasikci, Kerem | |
dc.contributor.coauthor | Gunes, Remziye | |
dc.contributor.coauthor | Guven, Melih | |
dc.contributor.coauthor | Koras, Murat | |
dc.contributor.department | Department of Electrical and Electronics Engineering | |
dc.contributor.department | Department of Industrial Engineering | |
dc.contributor.schoolcollegeinstitute | College of Engineering | |
dc.date.accessioned | 2025-03-06T20:57:14Z | |
dc.date.issued | 2024 | |
dc.description.abstract | Direct sales are among the most valuable channels that bring banks together with customers in product and cross-product sales. Direct sales representatives, who communicate one-on-one with the customer, use tablet computers to process customer information that banks must receive or to sell products. Sales information flowing through these tablets constitute the target of the project. This project aims to identify regions with high potential for credit card sales carried out by direct sales personnel;it aims to move the workforce from regions with low potential to regions with high potential. Location optimization will be achieved using machine learning methods. In order for the algorithm to be used to learn the characteristics of each region and the factors affecting its performance, Turkey was placed in a grid view consisting of lines passing through hundredths of the latitude and longitude degrees. For each quadrangle, the characteristics of the customers living or travelling there, the area and the points of interest around it were taken into account. The value of each grid was calculated to decide on areas with high potential. A heat map is projected onto Turkey in order to visualize the study's results. | |
dc.description.indexedby | WOS | |
dc.description.indexedby | Scopus | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | N/A | |
dc.identifier.doi | 10.1109/SIU61531.2024.10601033 | |
dc.identifier.isbn | 9798350388978 | |
dc.identifier.isbn | 9798350388961 | |
dc.identifier.issn | 2165-0608 | |
dc.identifier.quartile | N/A | |
dc.identifier.scopus | 2-s2.0-85200904333 | |
dc.identifier.uri | https://doi.org/10.1109/SIU61531.2024.10601033 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/27167 | |
dc.identifier.wos | 1297894700247 | |
dc.keywords | Sales prediction | |
dc.keywords | Machine learning | |
dc.keywords | Geographical information systems | |
dc.keywords | Spatial modelling | |
dc.language.iso | tur | |
dc.publisher | IEEE | |
dc.relation.ispartof | 32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024 | |
dc.subject | Computer science | |
dc.subject | Electrical and electronic | |
dc.subject | Telecommunications | |
dc.title | Location-based performance management of sales teams | |
dc.title.alternative | Satış ekiplerinin konum bazlı performans yönetimi | |
dc.type | Conference Proceeding | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Akgün,Barış | |
local.contributor.kuauthor | Gönen Mehmet | |
local.publication.orgunit1 | College of Engineering | |
local.publication.orgunit2 | Department of Electrical and Electronics Engineering | |
local.publication.orgunit2 | Department of Industrial Engineering | |
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