The Effect of Customer Behavior on Conversion Rates in Digital Marketing Campaigns: Data Analytics and Random Forest Model Approach

Abstract

This study was conducted by analyzing the data of customers participating in a digital marketing campaign and examining the factors affecting conversion rates. As a result of the calculated Exploratory Data Analysis (EDA), it was determined that age, income, website interactions and advertising expenditure variables had significant effects on conversion. In the modeling phase, the Random Forest classification algorithm was used to estimate conversion rates. The overall accuracy score of the model was calculated as 88.12%. However, it was observed that the model was weak in predicting the “No Conversion” class, but on the contrary, it showed high performance in the “Conversion” class. This situation, which arises from the unbalanced data distribution, shows that class balance strategies can be used to improve the model. The results of the analysis show that advertising expenditures, website visits and click-through rates have significant effects on conversion. These findings emphasize that digital marketing strategies should be optimized with personalized approaches.

Keywords

• Digital Marketing
• Customer Conversion
• Data Analytics
• Random Forest
• Campaign Performance

Dijital Pazarlama Kampanyalarında Müşteri Davranışlarının Dönüşüm Oranları Üzerindeki Etkisi: Veri Analitiği ve Random Forest Modeli Yaklaşımı

Abstract

Bu çalışma, e ticaret siteleri aracılığı ile alışverişlerini gerçekleştiren ve bir dijital pazarlama kampanyasına katılan müşterilerin verilerinin analiz edilerek, dönüşüm (conversion) oranlarını etkileyen faktörlerin incelenmesi ile gerçekleştirilmişti. Hesaplanan Keşifsel Veri Analizi (EDA) sonucunda, yaş, gelir, web sitesi etkileşimleri ve reklam harcamaları değişkenlerinin, dönüşüm üzerinde önemli etkiler yarattığı tespit edilmiştir. Modelleme aşamasında, dönüşüm oranlarını tahmin etmek için Random Forest sınıflandırma algoritması kullanılmıştır. Modelin genel doğruluk skoru %88.12 olarak hesaplanmıştır. Ancak, modelin “Dönüşüm Yok” sınıfını tahmin etmede zayıf kaldığı, bunun aksine “Dönüşüm” sınıfında yüksek bir performans sergilediği görülmüştür. Dengesiz veri dağılımından kaynaklanan bu durum, modelin iyileştirilmesi için sınıf dengesi stratejilerinin kullanılabileceğini göstermektedir. Analiz sonuçları, reklam harcamaları, web sitesi ziyaretleri ve tıklama oranlarının dönüşüm üzerinde önemli etkileri olduğunu göstermektedir. Bu bulgular, dijital pazarlama stratejilerinin kişiselleştirilmiş yaklaşımlarla optimize edilmesi gerektiğini vurgulamaktadır.

Keywords

• Dijital Pazarlama
• Müşteri Dönüşümü
• Veri Analitiği
• Random Forest
• Kampanya Performansı

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