GCRIS

Now showing 1 - 3 of 3

Makine Öğrenimi Algoritmalarına Dayalı Çevrimiçi Pazar Yeri Satış Tahmininin Analizi: Türk E-Ticaret Sitesi Örneği
(Abdullah Gül Üniversitesi / Sosyal Bilimler Enstitüsü, 2023) Kaya, Ecem; Sütçü, Muhammed
Internet shopping has grown in popularity as more of our daily requirements have begun to be addressed online. Learning about the preferences and motivations of customers in the Turkish market and guiding e-commerce platforms to adapt their marketing strategies and increase customer satisfaction is important for both resource allocation and cost minimization. The purpose of this paper is to estimate future sales for popular e-commerce sites based on behavioral factors such as discounts, price or free shipping. Therefore, real-time and experiment-independent data are collected from the sales made by one of Turkey's most popular e-commerce sites. In order to produce predictions, we employ Artificial Neural Networks, Support Vector Regression, K-Nearest Neighbors Regressor, OLS regression, and Nu-Support Vector Regressor. The models developed using machine learning algorithms attempt to estimate the number of sales based on independent factors such as price, discount rate, and user ratings. As the result of this research, we calculate and compare the accuracy of the models with root mean squared errors and R².
Geliştirilmiş RFM Modeli ile Müşteri Segmentasyonu: Bir Halı ve Kilim Üretici Firmasında Uygulama
(Abdullah Gül Üniversitesi, Sosyal Bilimleri Enstitüsü, 2022) İmdad, Yağmur Gizem; İmdad, Yağmur Gizem; Yılmaz, Cengiz
Data science has gained enormous importance by contributing to the in-depth understanding and interpretation of information. Especially companies consult on data analysis to make strategic decisions in the competitive market. Much more important than the decisions taken is a determination of the customer or customer groups to which these decisions will be adapted. For that reason, customer segmentation by identifying similarities and differences between customers becomes crucial. In recent times, the RFM model is preferred mostly for customer segmentation. The RFM model is based on the customer's last purchase date, how often they purchase, and how much money contributes to the company. It is an easy model to understand and interpret results in a clear way. Many researchers prefer to apply the RFM method by adding extra variables to the analysis. Thus, customers are evaluated from a broader perspective. This study aims to present a developed RFM model by adding extra variables which are Loyalty, Dependence, and Expectation which are determined by a broad literature review and as a result of a survey relating to 106 dealers. There are some studies that create a segmentation model by using loyalty and the RFM model. However, this study developed a new model by including the dependence and expectation variables, which are not been used previously with the RFM model, besides loyalty. In the study, dealers are analyzed by the K-means clustering method and the optimum number of clusters is indicated as six. Each cluster has its specific customer behavior and this study guides the company to constitute marketing strategies regarding customers' specifications.
COVID-19 Sürecinde Hareketlilikte Eşitsizlik: Küresel ve Yerel Analiz
(Abdullah Gül Üniversitesi, Sosyal Bilimleri Enstitüsü, 2022) Gençaslan, Elif; Gençaslan, Elif; Türk, Umut; Madenoğlu, Fatma Selen
This thesis analyzes mobility patterns during the Covid-19 pandemic from a global and local perspective. The global framework includes 37 European countries and the local framework comprises 81 Turkish cities. The study follows the daily mobility trajectories of people from February 2020 to January 2022. The analyzes are conducted to understand the economic opportunities available in countries -at a macro scale- that facilitate or hinder the 'proper' mobility behavior of individuals while focusing on the captive commuters, i.e., the share of the population who need to commute to the work despite the risk of infection and governmental policies. The results indicate that the workforce in regions with higher GDP per capita, education level, and life expectancy at birth was able to reduce their workplace mobility higher than commuters in areas with low income, education level, and life expectancy at birth. Therefore, unprivileged populations were exposed to higher health risks against rapid Covid-19 transmission in Europe and Turkish cities.

Yüksek Lisans Tezleri

Browse

Filters

Settings

Sort By

Results per page

Search Results