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2019, vol. 7, iss. 1, pp. 61-72
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Combined application of hierarchical and non-hierarchical clustering methods in order to segment the customers in one trade chain
Kombinovana primena hijerahijskih i nehijerarhijskih metoda klasterizacije u cilju segmentacije kupaca u jednom trgovinskom lancu
Abstract
In this paper K-means clustering algorithm is applied in order to classify customers into several groups showing the similarity within a group is better than among groups. After determining the relevant client's attributes in a SQL Server database, K-means is applied in MATLAB programming environment, using fixed number of clusters. Each centroid defines one of the clusters, while each data point is assigned to the nearest centroid, based on the squared Euclidean distance. In this research, centroids are randomly generated, while the separation distance between the resulting clusters is analyzed and illustrated using the Silhouette index. The analysis and results presented in this paper could determine a similarity in purchasing or using the services by a population cluster in a luxury goods company, to develop market segments, to identify repetitive behavior or trends in order to evaluate clients'actions and to create some new customer loyalty campaigns.
Sažetak
U ovom radu primenjene su hijerarhijske i nehijerarhijske metode klaster analize, u cilju utvrđivanja relativno homogenih grupa kupaca u jednom trgovinskom lancu tokom prethodnih pet godina. Nakon izdvajanja objekata i određenih atributa iz SQL Server baze podataka i određivanja skupa relevantnih varijabli koje će reprezentovati obeležja grupe, primenjen je K-means algoritam u MATLAB programskom okruženju, a kao mera sličnosti korišćen je kvadrat Euklidskog rastojanja. Na osnovu analize ukupnog broja kupljenih artikala, frekvencije kupovina, ukupnog prometa po kupcu i broja kontaktiranja kompanije prema kupcu, predložen je broj klastera, koji je tokom izvršavanja algoritma fiksan, a u cilju detekcije razdvojenosti i kompaktnosti klastera, korišćen je Silhouette indeks. Korišćeno je fiksno pet klastera, a takođe je primenjen i hijerarhijski metod, da bi se postavljanjem granice na dendrogramu validirao pretpostavljeni broj klastera, a čime se takođe može eliminisati i problem uticaja slučajnog izbora početnog položaja centroida pri izvršavanju K-means algoritma. Dobijeni rezultati mogu se koristiti u smislu planiranja prodajnih kampanja, optimizacije troškova marketinga, predlaganja novih programa lojalnosti, u cilju boljeg razumevanja potrošačkog ponašanja, kao i pravljenja posebnih planova poslovnih aktivnosti za svaki klaster pojedinačno.
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