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Identifying Early Usage Patterns That Increase User Retention Rates In A Mobile Web Browser
Linköping University, Department of Computer and Information Science, Database and information techniques.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Att identifiera tidiga användarmönster som ökar användares återvändningsfrekvens (Swedish)
Abstract [en]

One of the major challenges for modern technology companies is user retentionmanagement. This work focuses on identifying early usage patterns that signifyincreased retention rates in a mobile web browser.This is done using a targetedparallel implementation of the association rule mining algorithm FP-Growth.Different item subset selection techniques including clustering and otherstatistical methods have been used in order to reduce the mining time and allowfor lower support thresholds.A lot of interesting rules have been mined. The best retention-wise ruleimplies a retention rate of 99.5%. The majority of the rules analyzed in thiswork implies a retention rate increase between 150% and 200%.

Place, publisher, year, edition, pages
2017.
Keyword [en]
data mining, retention, churn, association analysis, association rule, clustering, DBSCAN
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-137793ISRN: LIU-IDA/LITH-EX-A--17/012--SEOAI: oai:DiVA.org:liu-137793DiVA: diva2:1102717
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Available from: 2017-06-08 Created: 2017-05-30 Last updated: 2017-06-08Bibliographically approved

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persson2017.pdf(460 kB)19 downloads
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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
  • rtf