Return to search

Customer segmentation using machine learning

In this thesis, the process of developing an application for segmenting customers with the use of machine learning is described. The project was carried out at a company which provides a booking platform for beauty and health services. Data about customers were analyzed and processed in order to train two classification models able to segment customers into three different customer groups. The performance of the two models, a Logistic Regression model and a Support Vector Classifier, were evaluated with different numbers of features and compared to classifications made by human experts working at the company. The results shows that the logistic regression model achieved an accuracy of 71% when classifying users into the three groups, which was more accurate than the experts manual classification. A web API where the model is provided has been developed and presented to the company. The results of the study showed that machine learning is a useful technique for performing customer segmentation based on behavioral data. Even in the case where the classes are not naturally divisible, the application provides valuable insights on user behaviour that can help the company become more data-driven.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-443868
Date January 2021
CreatorsJohansson, Axel, Wikström, Jonas
PublisherUppsala universitet, Avdelningen för systemteknik, Uppsala universitet, Avdelningen för systemteknik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationUPTEC STS, 1650-8319 ; 21019

Page generated in 0.0024 seconds