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Maskininlärning för att förutspå churn baserat på diskontinuerlig beteendedata
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2017 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Machine learning to predict churn based on discontinuous behavioral data (English)
Abstract [en]

This report is about examining the fields of machine learning and digital marketing, using machine learning as a tool to predict churn in a new domain of companies that do not track their customers extensively, i.e where behaviour data is discontinuous. 

To predict churn relatively simple out of the box models, such as support vector machines and random forests, are used to achieve an acceptable outcome. To be on par with the models used for churn prediction in subscription based services, this report concludes that more research has to be done using more effective evaluation metrics.

Finally it is presented how these discoveries can be commercialized and the business related benefits of using churn prediction for the employer Sellpy.

Abstract [sv]

Denna rapport handlar om att utforska fälten maskininlärning och digital marknadsföring, genom att använda maskininlärning som ett redskap för att förutspå churn i en typ av företag med diskontinuerlig beteendedata.

För att förutspå churn finns relativt simpla "out of the box"-modeller, som support vector machines och random forests, som används för att nå acceptabla resultat. För att nå liknande resultat som i arbeten där churn utförs på kontinuerlig beteendedata konstaterar denna rapport att framtida arbeten forska på vilka utvärderingsmetriker som är mest lämpade.

I rapporten presenteras också hur dessa upptäckter kan kommersialiseras och hur företaget Sellpy kan tjäna på att förutspå churn.

Place, publisher, year, edition, pages
2017. , 25 p.
Keyword [en]
Churn, churning, prediction, machine learning, discontinuous, behavioral data, random forests, support vector machines
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-210546OAI: oai:DiVA.org:kth-210546DiVA: diva2:1118564
External cooperation
Sellpy
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2017-10-27 Created: 2017-06-30 Last updated: 2017-10-27Bibliographically approved

Open Access in DiVA

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CiteExportLink to record
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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
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