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Customer Segmentation and Customer Churn Prediction for Babil.com
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Çakar, Berk; Evren Güney
In the past decade, a lot of players have joined into e-commerce market and competition in the market has been increasing lately. The e-commerce companies want to use their resources more efficiently to stay ahead in the competition. Personal communication with customers, increasing customer loyalty, acquiring new customers and preventing customer churn are some of the ways to achieve this goal.Babil.com is an e-retailer that sells books online and it is one of the companies which wants to stay ahead in the competition. It is founded in 2013 and now it is a 8 years old mature company. So, instead of spending much resources on acquiring new customers, trying to keep existing customers and increasing retention rate is a more ideal goal for the company. Also, personal communication with customers and reaching them with the right product in the right time is crucial.In this project, a customer segmentation with two levels is implemented to help Babil.com. For the first level of segmentation, customers’ value to company is identified by RFM segmentation and in the second level of segmentation customers’ behaviors are identified by K-Means clustering. To prevent customer churn, a machine learning algorithm which predicts customers who will churn in the next 6 months. With this algorithm, it will be easy to take an action for the right customers in the right time.
Yelp Review Dataset Sentiment Analysis Using Machine Learning Techniques
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Atik, Anılcan; Evren Güney
Today, internet review sites are becoming a significant criterion for users’ consumption habits on products and services, while being vital source of feedback for businesses. This project aims to present quick feedback on whether consumers are satisfied with businesses’ product and services, lessen the allocation of resources on information extraction towards these reviews, and provide a more agile environment for businesses, by automatizing the extraction of the information “whether the sentiment towards the business service or product is positive or negative” from textual data. The problem, binary classification out of textual data, is addressed through Yelp Company reviews dataset. Yelp is an internet review website, it enables users to review products, services, and businesses. Alongside with the text formatted restaurant reviews, star-rating is converted to 1 (positive) and 0 (negative). These values are obtained to provide the target column to predict the sentiment of the review text. 100,000 restaurant review records are used in 4 different machine learning algorithms to predict the binary classification problem of predicting whether the review sentiment is positive or negative. 2 neural networks one with pre-trained GloVe, SVM, and Logistic Regression models are used, and the success of these models is compared using F1-Score as a performance metric. These results are presented in the paper.

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