20.500.12556/RUL-117315 Segmentacija strank v bančnem sektorju s pomočjo podatkovnega rudarjenja diplomsko delo Customer segmentation in the banking sector through data mining Diplomsko delo obravnava segmentacijo strank s pomočjo podatkovnega rudarjenja ter opredeljuje relevantne metode in podatke, ki so pomembni za segmentacijo v bančnem sektorju. Obdobje hitrega razvoja tehnologije in naprednih analitičnih orodij zahteva nenehno prilagajanje ponudbe potrošnikom ter analize njihovih potreb in navad. Poleg številnih prednosti, ki smo jih zahvaljujoč napredku tehnologije deležni, se soočamo tudi s številnimi izzivi. V bančništvu je to vidno predvsem v pogostih zlorabah in goljufijah, ki se jim banke vsakodnevno skušajo izogniti ter jih preprečiti. Velikega števila strank ne moremo obravnavati individualno, zato je segmentacija v bančništvu izjemno pomembna in bankam omogoča prilagajanje ponudbe strankam na podlagi njihovih skupnih karakteristik. V empiričnem delu diplomskega dela sem prikazal primer segmentacije strank na podlagi demografskih značilnosti. Uporabil sem k-means metodo grozdenja, ki je ena izmed najbolj pogosto uporabljenih metod za segmentacijo strank. Dobljene grozde sem opisal in opredelil ključne karakteristike posameznikov, ki določenemu grozdu pripadajo ter kakšne storitve lahko banka posameznikom znotraj teh grozdov ponudi. The thesis deals with customer segmentation with the help of data mining and identifies relevant methods and data that are important for segmentation in the banking sector. The period of rapid development of technology and advanced analytical tools requires constant adjustment of the offer to consumers and analysis of their needs and habits. In addition to the many benefits we receive from the advancement of technology, we also face many challenges. In banking, this is especially evident in the frequent abuses and frauds that banks try to avoid and prevent on a daily basis. A large number of customers cannot be addressed individually, so segmentation in banking is extremely important and allows banks to adjust their offer to customers based on their common characteristics. In the empirical part of the thesis, I presented an example of customer segmentation based on demographic characteristics. I used the k-means clustering method, which is one of the most commonly used methods for customer segmentation. I described the obtained clusters and defined the key characteristics of individuals who belong to a certain cluster and what services the bank can offer to individuals within these clusters. segmentacija podatkovno rudarjenje bančništvo grozdenje segmentation data mining banking clustering true true false [H. Jusić] Slovenski jezik Angleški jezik Diplomsko delo/naloga 2020-07-05 08:30:02 2020-07-05 08:30:07 2022-08-29 03:42:34 0000-00-00 00:00:00 2020 0 Ljubljana 0 Ljubljana 56 str. 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 336.71:004(043.2) 116587 23654147 3358.pdf 3358.pdf 1 B301E79BB9B8AA385940CE7FE9359C48 49cc45a4f3e0418345efa105f8974bcfb5043ccf4365da03044db6456b6d9993 2682a143-a1b9-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=131144 Fakulteta za družbene vede 0 0 0