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Machine Learning for Categorizing Companies in Sweden - A Study for Decision Making Support in Customer Relationship Management

Frid, Christian LU (2016) In LU-CS-EX 2016-08 EDA920 20152
Department of Computer Science
Abstract
The number of companies in Sweden has shown a significant increase over the last five years (www.bolagsverket.se). Data about these companies is an important asset for the customer relationship management market. In most business areas, users are in search for new customers. Companies, such as Lundalogik AB, provide a service, where the analysts can look for customers in an interactive environment that provides useful data about other companies.

However, the majority of company names is largely unknown even to analysts. This makes it hard for them to make quick decisions about which companies that could be future customers. It is an ineffective and time consuming activity to scout through large amounts of data in search of interesting... (More)
The number of companies in Sweden has shown a significant increase over the last five years (www.bolagsverket.se). Data about these companies is an important asset for the customer relationship management market. In most business areas, users are in search for new customers. Companies, such as Lundalogik AB, provide a service, where the analysts can look for customers in an interactive environment that provides useful data about other companies.

However, the majority of company names is largely unknown even to analysts. This makes it hard for them to make quick decisions about which companies that could be future customers. It is an ineffective and time consuming activity to scout through large amounts of data in search of interesting companies. This is why there is a need for a tool that compares companies to one another (within the same line of business and company form). This way the user can get quicker insights about companies.

Machine learning techniques work well within customer relationship management (Glas, 2015). This Master's thesis uses techniques in machine learning to categorize companies in regards to size and economy. It also shows how to make predictive models that foretell the category of any previously unknown company.

The results I obtained show that the companies can be clustered and labeled with meaningful descriptions. With a sufficiently large number of instances, these labels can in turn be used to create a supervised learner model with great predictive ability. (Less)
Popular Abstract
Results show that large amounts of company data can be categorized into groups with descriptive labels using k-means clustering and C4.5 decision tree induction. Clients can now search for companies more efficiently.
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author
Frid, Christian LU
supervisor
organization
alternative title
Machine Learning för kategorisering av Företag i Sverige - En studie för beslutsstöd inom Customer Relationship Management
course
EDA920 20152
year
type
H3 - Professional qualifications (4 Years - )
subject
keywords
CRM, Machine Learning, Clustering, Data Mining, Decision Trees, C4.5, K-means, Principal Components Analysis (PCA)
publication/series
LU-CS-EX 2016-08
report number
LU-CS-EX 2016-08
ISSN
1650-2884
language
English
id
8870990
date added to LUP
2016-04-13 13:03:06
date last changed
2016-04-13 13:03:06
@misc{8870990,
  abstract     = {The number of companies in Sweden has shown a significant increase over the last five years (www.bolagsverket.se). Data about these companies is an important asset for the customer relationship management market. In most business areas, users are in search for new customers. Companies, such as Lundalogik AB, provide a service, where the analysts can look for customers in an interactive environment that provides useful data about other companies. 

However, the majority of company names is largely unknown even to analysts. This makes it hard for them to make quick decisions about which companies that could be future customers. It is an ineffective and time consuming activity to scout through large amounts of data in search of interesting companies. This is why there is a need for a tool that compares companies to one another (within the same line of business and company form). This way the user can get quicker insights about companies. 

Machine learning techniques work well within customer relationship management (Glas, 2015). This Master's thesis uses techniques in machine learning to categorize companies in regards to size and economy. It also shows how to make predictive models that foretell the category of any previously unknown company. 

The results I obtained show that the companies can be clustered and labeled with meaningful descriptions. With a sufficiently large number of instances, these labels can in turn be used to create a supervised learner model with great predictive ability.},
  author       = {Frid, Christian},
  issn         = {1650-2884},
  keyword      = {CRM,Machine Learning,Clustering,Data Mining,Decision Trees,C4.5,K-means,Principal Components Analysis (PCA)},
  language     = {eng},
  note         = {Student Paper},
  series       = {LU-CS-EX 2016-08},
  title        = {Machine Learning for Categorizing Companies in Sweden - A Study for Decision Making Support in Customer Relationship Management},
  year         = {2016},
}