Model segmentacije potrošača zasnovan na datumu poslednje kupovine, učestalosti i novčanom iznosu kupovine
Customer segment model with the purchase recency, frequency and monetary amount
Apstrakt
Ovaj rad koristi podatke o transakcijama potrošača u cilju njihove segmentacije na osnovu datuma poslednje kupovine, učestalosti kupovine i novčanog iznosa. Korišćenjem empirijskog pristupa, predlaže se stohastički model za predviđanje segmentacije potrošača u kategorije kao što su novi, aktivni, potencijalni i izgubljeni potrošači. Model takođe konstruiše indekse vrednosti i lojalnosti potrošača. Ovaj pristup omogućava kompanijama da praktično analiziraju i kategorišu svoje potrošače, uz izračunavanje verovatnoće karakteristika segmenata. Studija ističe značaj segmentacije potrošača u strateškom planiranju, naglašavajući ulogu RFM (eng. recency, frequency, monetary amount) analize u identifikaciji potrošača visoke vrednosti i optimizaciji marketing strategija. Predloženi model integriše metrike vrednosti i lojalnosti potrošača, pružajući sveobuhvatan okvir za unapređenje upravljanja odnosima sa potrošačima i ciljanih marketing aktivnosti. Empirijski podaci iz baze korisnika kreditnih kartica na Tajvanu pokazuju efikasnost modela u segmentaciji potrošača i predviđanju njihovog ponašanja, nudeći značajne uvide za strateško raspoređivanje resursa i maksimizaciju potencijalnog prihoda. Ovo istraživanje doprinosi prethodnoj literaturi predstavljanjem robusne metode za dinamičku segmentaciju potrošača, koja se može primeniti u različitim delatnostima.
Abstract
This paper utilizes customer transaction data to segment customers based on their purchase recency, frequency, and monetary amount. By employing an empirical approach, a stochastic model is proposed to predict customer segmentation into categories such as new, active, potential, and lost. The model also constructs indices for customer equity and loyalty. This approach allows companies to practically analyze and categorize their customers, calculating the probability of segment characteristics. The study highlights the importance of customer segmentation in strategic planning, emphasizing the role of RFM (recency, frequency, monetary amount) analysis in identifying high-value customers and optimizing marketing strategies. The proposed model integrates customer equity and loyalty metrics, providing a comprehensive framework for businesses to enhance customer relationship management and targeted marketing efforts. The empirical data from a credit card customer database in Taiwan demonstrates the model‘s effectiveness in segmenting customers and predicting their behavior, offering valuable insights for businesses to allocate resources strategically and maximize revenue potential. This research contributes to the field by presenting a robust method for dynamic customer segmentation, applicable across various industries.
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