20.500.12556/RUL-162923 Optimizacija strategije upravljanja odnosov s strankami s pomočjo metod podatkovnega rudarjenja diplomsko delo Optimization of customer relationship management strategy using data mining methods Upravljanje odnosov s strankami (CRM) je kljub pomanjkanju enotne definicije opredeljen kot integriran pristop k pridobivanju in ohranjanju strank, ki sloni na obdelavi podatkov o strankah in informacijski tehnologiji. Ključne stopnje upravljanja odnosov s strankami vključujejo prepoznavanje, pridobivanje, zadrževanje in razvoj strank. V nalogi smo sprva obrazložili ključne pojme in opisali modele, kasneje pa smo se osredotočili na uporabo metod podatkovnega rudarjenja za podporo CRM. Opravili smo segmentacijo strank z uporabo dveh modelov, RFM (Recency, Frequency, Monetary) ter metode razvrščanja z voditelji (K-means). Na koncu smo še napovedali življenjsko vrednost stranke (CLV) z modeloma BG/NBD in Gamma-Gamma, kjer prvi napove število prihodnih transakcij strank, drug pa oceni njihovo denarno vrednost. S pomočjo teh analiz lahko podjetje bolje razume svoje stranke, prilagodi marketinške strategije in poveča svojo konkurenčnost. Z opravljeno analizo smo potrdili hipoteze, da implementacija strategije upravljanja odnosov s strankami prispeva k večji konkurenčni prednosti podjetja in omogoča boljšo segmentacijo strank, kar prispeva k optimizaciji marketinške strategije ter povečanju dobičkonosnost za podjetje. Despite the lack of a unified definition customer relationship management (CRM) is defined as an integrated approach to acquiring and retaining customers, based on the processing of customer data and information technology. The key stages of CRM include identifying, acquiring, retaining, and developing customers. In this work, we define key concepts and described models in the first part and later focus on the use of data mining methods to support CRM. We performed customer segmentation using two models: RFM (Recency, Frequency, Monetary) and the K-means clustering method. Finally, we predicted customer lifetime value (CLV) using the BG/NBD and Gamma-Gamma models, where the former predicts the number of future customer transactions, and the latter predicts their monetary value. These analyses help the company to better understand its customers, adjust marketing strategies, and increase its competitiveness. Through the analysis, we confirmed the hypotheses that implementing a CRM strategy contributes to a greater competitive advantage for the company and enables better customer segmentation, which in turn contributes to optimisation of the marketing strategy and increased profitability for the company. upravljanje odnosov s strankami podatkovno rudarjenje segmentacija strank življenjska vrednost stranke customer relationship management data mining customer segmentation customer lifetime value true true false J. Vošnjak Slovenski jezik Angleški jezik Diplomsko delo/naloga 2024-09-29 08:30:18 2024-09-29 08:30:28 2024-11-13 15:07:58 0000-00-00 00:00:00 2024 0 Ljubljana 0 Ljubljana 1 spletni vir (1 datoteka PDF (57 str.)) 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 658.818:004.8(043.2) 168354 214872067 16014.pdf 16014.pdf 1 4D67EDAD0F7128E706F5FC73E04AD4A4 a896c6d860818e297064a499d98d9ce4714120a7de8ec7ef27eef1ca2f812df2 2e4cbcd1-7e2c-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=192367 Fakulteta za družbene vede 0 0 0