Publication: Pazarlama segmentasyon modellerinin karşılaştırılması üzerine biyoteknoloji sektöründe bir uygulama
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Date
2024
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Abstract
Günümüzde artan rekabet, firmaların müşteri bağlılığını artırmak için daha fazla çaba sarf etmesini gerektirmektedir. Birçok firma, müşteri ilişkileri yönetimi (CRM) uygulamalarına önemli yatırımlar yaparak müşteri ihtiyaçlarındaki değişiklikleri izlemek, kişiselleştirilmiş hizmetler sunmak, rakiplerinden farklılaşmak ve rekabet avantajı elde etmek için çalışmaktadır. Bu amaçla, RFM (Recency, Frequency, Monetary) analizi müşteri verilerini etkili bir şekilde sınıflandırmak için büyük bir öneme sahiptir. RFM, müşterinin ne kadar yakın zamanda alışveriş yaptığı ne sıklıkta alışveriş yaptığı ve ne kadar harcadığı gibi kriterlere dayanarak müşterileri segmentlere ayırır.Geleneksel RFM yöntemi farklı yönlere genişletilerek daha ayrıntılı segmentasyonlar geliştirilmiştir. Bu genişletmeler, müşteri ilişkisinin uzunluğu (Length) ve ürün çeşitliliği (Variety) kriterleri eklenerek LRFM (Length, Recency, Frequency, Monetary) ve RFMV (Recency, Frequency, Monetary, Variety) yöntemleri ortaya çıkmıştır. Geleneksel olarak kabul edilen RFM yöntemi ile genişletilerek oluşturulan her bir yöntem kıyaslanacaktır. Çalışmada bu analizler biyoteknoloji sektörüne ait müşteri verileri üzerinden elde edilecektir. Analizler sonucunda müşterilere uygulanması gereken hangi pazar stratejilerinin daha etkili olabileceği ve sektör için hangi yöntemin daha etkili sonuç vereceğini göstermek hedeflemiştir. Yöntemlerin hepsini bir arada göstererek her bir yöntemin artı eksi yönleriyle firma için değerlendirebilmesi, planlayacağınız stratejilere daha kolay karar verilmesi, firmanın ne tür hizmetler sunması gerektiği, nasıl farklılaşabileceği gibi birçok konuda firmaya yanıtlar sunarak analiz sonuçlarının uygulanabilir planlar haline getirilmesi hedeflenmiştir.
In the contemporary business landscape, intensifying competition necessitates companies to exert more effort in enhancing customer loyalty. Many firms invest significantly in Customer Relationship Management (CRM) applications to monitor changes in customer needs, provide personalized services, differentiate themselves from competitors, and gain a competitive advantage. In this regard, Recency, Frequency, Monetary (RFM) analysis holds paramount importance in effectively classifying customer data. RFM segments customers based on criteria such as how recently they made a purchase, how frequently they make purchases, and how much they spend.The traditional RFM method has been expanded in various directions to develop more detailed segmentation. These extensions include adding criteria such as the length of the customer relationship (Length) and product variety (Variety), giving rise to methods like LRFM (Length, Recency, Frequency, Monetary) and RFMV (Recency, Frequency, Monetary, Variety). Each method, whether traditional RFM or its extended counterparts, will be compared in this study. The analysis will be conducted using customer data from the biotechnology sector. The aim is to determine which market strategies may be more effective for applying to customers and which method yields more effective results for the industry.By presenting and evaluating each method together, the analysis seeks to provide insights for the company to make informed decisions on strategies. This includes assessing the strengths and weaknesses of each method, facilitating easier decision-making for planned strategies, guiding the company on the types of services it should offer, and identifying avenues for differentiation. The ultimate goal is to transform the analysis results into actionable plans that can guide the company in various aspects, such as determining the most effective marketing strategies and services to offer in the biotechnology sector.
In the contemporary business landscape, intensifying competition necessitates companies to exert more effort in enhancing customer loyalty. Many firms invest significantly in Customer Relationship Management (CRM) applications to monitor changes in customer needs, provide personalized services, differentiate themselves from competitors, and gain a competitive advantage. In this regard, Recency, Frequency, Monetary (RFM) analysis holds paramount importance in effectively classifying customer data. RFM segments customers based on criteria such as how recently they made a purchase, how frequently they make purchases, and how much they spend.The traditional RFM method has been expanded in various directions to develop more detailed segmentation. These extensions include adding criteria such as the length of the customer relationship (Length) and product variety (Variety), giving rise to methods like LRFM (Length, Recency, Frequency, Monetary) and RFMV (Recency, Frequency, Monetary, Variety). Each method, whether traditional RFM or its extended counterparts, will be compared in this study. The analysis will be conducted using customer data from the biotechnology sector. The aim is to determine which market strategies may be more effective for applying to customers and which method yields more effective results for the industry.By presenting and evaluating each method together, the analysis seeks to provide insights for the company to make informed decisions on strategies. This includes assessing the strengths and weaknesses of each method, facilitating easier decision-making for planned strategies, guiding the company on the types of services it should offer, and identifying avenues for differentiation. The ultimate goal is to transform the analysis results into actionable plans that can guide the company in various aspects, such as determining the most effective marketing strategies and services to offer in the biotechnology sector.
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pazarlama, sayısal yöntemler, müşteri segmentasyonu, veri madenciliği marketing, customer segmentation, data mining, quantitative methods