diplomsko delo
Huso Jusić (Author), Damjan Škulj (Mentor)

Abstract

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.

Keywords

segmentacija;podatkovno rudarjenje;bančništvo;grozdenje;Banke;Potrošniki;Tehnologija;Diplomska dela;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FDV - Faculty of Social Sciences
Publisher: [H. Jusić]
UDC: 336.71:004(043.2)
COBISS: 23654147 Link will open in a new window
Views: 584
Downloads: 184
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Other data

Secondary language: English
Secondary title: Customer segmentation in the banking sector through data mining
Secondary abstract: 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.
Secondary keywords: segmentation;data mining;banking;clustering;Banks;Consumers;Technology;Graduate theses;
Type (COBISS): Bachelor thesis/paper
Study programme: 0
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za družbene vede
Pages: 56 str.
ID: 11884481