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Predicting Customer Lifetime ValueUsing machine learning algorithms
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Spending money to acquire new customers can be a risk since new players never immediately pay off. In this thesis three machine learning algorithms, neural network, bayesian network and regression, is used to try to early find out if it is possible to determine how much a user will spend in the game in order to minimize the risk.

The result showed that neural network performed badly mostly because there might not be a strong correlation between how a player plays, or where he comes from, and how much he will spend.

Because of how bayesian network works, it was hard to answer the question, but it still gave a good indication at what kind of players spends money in the game.

Regression showed that a player should have paid off around 50% of the advertisement cost around day six or seven, or it will most likely never pay off.

Place, publisher, year, edition, pages
2017. , 46 p.
Series
UMNAD, 1091
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-130652OAI: oai:DiVA.org:umu-130652DiVA: diva2:1069107
External cooperation
Turbolilla AB
Educational program
Master of Science Programme in Computing Science and Engineering
Supervisors
Examiners
Available from: 2017-01-27 Created: 2017-01-27Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
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Output format
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