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2020, vol. 5, br. 2, str. 60-76
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Predviđanje prodaje na platformi e-trgovine, korišćenjem data mining modela
Sales prediction on e-commerce platform, by using data mining model
Keywords: Cluster analysis; PCA; Market Basket analysis; Vector Distance model; marketplace segmentation
Sažetak
U ovom radu primenjen je algoritam za prodaju twinning proizvoda sa e-commerce Web platforme. Kako bi se utvrdili relativno homogene grupe proizvoda u trgovinskom lancu na internetu tokom prethodne godine potrebno je bilo formirati prediktivni matematički model. Nakon izdvajanja objekata i odre enih atributa iz MySQL baze podataka, odre ivanja skupa relevantnih varijabli koje će reprezentovati obeležja grupe, primenjeni su K-means algoritam u Python programskom okruženju, Market Basket model i Vector Distance model. Na osnovu analize izvornih i veštačkih promenljivih, predložen je broj klastera, koji je tokom izvršavanja algoritma fiksan, a u cilju detekcije razdvojenosti i kompaktnosti klastera, korišćen je Silhouette indeks. Na osnovu podele po klasterima, ura eni su modeli koji predvi aju slične proizvode i analizirala se verovatnoća kupovine. Dobijeni rezultati mogu se koristiti u smislu planiranja prodajnih kampanja, optimizacije troškova marketinga, predlaganja novih programa lojalnosti, kao i boljeg razumevanja ponašanja potrošača sa ciljem zadržavanja postojećih i povećanja broja novih kupaca. Rezultati su izdvojeni u dve grupe - predloge koje treba ponuditi kupcima i predloge koje treba ponuditi prodavcima.
Abstract
In this paper we applied twinning algorithm for product that are sold via e-commerce platform. To establish relatively homogenous product groups that were on sale on this e-commerce platform during the last year, it was necessary to form predictive mathematical model. We determined set of relevant variables that will represent group attributes, and we applied K-means algorithm, Market Basket model and Vector Distance model. Based on analysis of basic and derived variables, fixed number of clusters was introduced. Silhouette index was used for the purposes of detecting whether these clusters are compact. Using these cluster separations, we created models that detect similar products, and try to analyze probability of sales for each product. Analysis results can be used for planning future sales campaigns, marketing expenses optimization, creation of new loyalty programs, and better understanding customer behavior in general.
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