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Identifying New Customers Using Machine Learning: A case study on B2B-sales in the Swedish IT-consulting sector
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Identifiering av nya kunder med hjälp av maskininlärning : En fallstudie om B2B-försäljning i den svenska IT-konsultsektorn (Swedish)
Abstract [en]

In this thesis, we examine machine learning as a tool for predicting new cus- tomers in a B2B-sales context. Using only publicly available information, we try to solve the problem using two different approaches: 1) a naive clustering based classifier built on K-means and 2) PU-learning with a random forests- adapter. We test these models with different sets of features and evaluate them using statistical measures and a discussion of the business implications. Our main findings conclude that the PU-learning could produce results that are satisfactorily for the purpose of improving the sales process, with the best case of being 4.8 times better than a random baseline classifier. However, the clustering based classifier was not good enough, producing only marginally better results than a random classifier in its best case. We also find that us- ing more variables improved the models, even in high-dimensional spaces with over 60 variables.

Place, publisher, year, edition, pages
2017. , p. 39
Keyword [en]
Machine Learning, B2B, Industrial Marketing, PU-learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-210256OAI: oai:DiVA.org:kth-210256DiVA, id: diva2:1118107
External cooperation
Exsitec AB
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2017-10-23 Created: 2017-06-29 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
Permanent link

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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
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