Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Customer churn prediction using machine learning: A study in the B2B subscription based service context
Blekinge Institute of Technology.
Blekinge Institute of Technology.
2021 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The rapid growth of technological infrastructure has changed the way companies do business. Subscription based services are one of the outcomes of the ongoing digitalization, and with more and more products and services to choose from, customer churning has become a major problem and a threat to all firms. We propose a machine learning based churn prediction model for a subscription based service provider, within the domain of financial administration in the business-to-business (B2B) context. The aim of our study is to contribute knowledge within the field of churn prediction. For the proposed model, we compare two ensemble learners, XGBoost and Random Forest, with a single base learner, Naïve Bayes. The study follows the guidelines of the design science methodology, where we used the machine learning process to iteratively build and evaluate the generated model, using the metrics, accuracy, precision, recall, and F1- score. The data has been collected from a subscription-based service provider, within the financial administration sector. Since the used dataset is imbalanced with a majority of non- churners, we evaluated three different sampling methods, that is, SMOTE, SMOTEENN and RandomUnderSampler, in order to balance the dataset. From the results of our study, we conclude that machine learning is a useful approach for prediction of customer churning. In addition, our results show that ensemble learners perform better than single base learners and that a balanced training dataset is expected to improve the performance of the classifiers. 

Place, publisher, year, edition, pages
2021. , p. 49
Keywords [en]
Customer churning, Machine Learning, business-to-business, subscription-based companies.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-21872OAI: oai:DiVA.org:bth-21872DiVA, id: diva2:1574424
External cooperation
Fortnox AB
Subject / course
Degree Project in Master of Science in Engineering 30,0 hp
Educational program
IEACI Master of Science in Industrial Management and Engineering
Presentation
2021-05-24, 15:00 (English)
Supervisors
Examiners
Available from: 2021-06-29 Created: 2021-06-28 Last updated: 2022-05-12Bibliographically approved

Open Access in DiVA

fulltext(907 kB)5137 downloads
File information
File name FULLTEXT01.pdfFile size 907 kBChecksum SHA-512
9f4bc788a3ffa68261dde3a90b7025a60e064479fa8d33e9e06844ee6a53268c4b833da12b2342de286eead29ed01d8f8e59906ed07a0474db51594296c3fcd2
Type fulltextMimetype application/pdf

By organisation
Blekinge Institute of Technology
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 5137 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 2618 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf