February 22, 2020 Thesis Open Access

Getachew Gemechu

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  <dc:creator>Getachew Gemechu</dc:creator>
  <dc:date>2020-02-22</dc:date>
  <dc:description>Direct marketing is a form of communicating an offer directly to a targeted group of
customers through a variety of media. It plays a major role in customer retention and
service provisioning tasks. Retaining customers by providing products and services that
meet their need is one of the main objectives of customer relationship management. Identifying
prospective customers for direct marketing enables a company to reach specific
audiences which will more effectively respond to promotions. Moreover, direct marketing
helps businesses to optimize their marketing budget, keeps current customers loyal
to them, and makes businesses capable of measuring the result obtained from promotions.
Ethio telecom promotes service packages to its customers through SMS and mass communication
channels. However, promotions should target customers based on the specific
services they use, and customer over-touching should be reduced especially during
SMS advertisement. In the current practice, no scientific methodology is implemented
to estimate the potential respondents to cross-selling market promotion. Promotions are
communicated to both potential buyers and non-buyers without distinguishing the two
groups. Direct marketing approaches help the company to effectively allocate resources
and give services based on the interests of customers.
The aim of this thesis is to identify prospective customers in ethio telecom mobile valueadded
service market. To achieve this goal, five classifiers namely Naive Bayes, Neural
network, SVM, K-nearest neighbour, and Decision tree (J48) tested with customers
service usage historical data. In this process, 900,000 customers’ actual CDRs from
ethio telecom were gathered and raw data aggregated with the aim of representing users’
behaviour. The representation was based on users’ responses towards service fee and
time preference to use services. Sixteen feature variables and one predictor variable
are constructed from the raw CDR collected. Data cleaning and class balancing done,
and the selected classifiers tested for their accuracy in identifying prospective buyers of
service packages</dc:description>
  <dc:identifier>https://zenodo.org/record/4920</dc:identifier>
  <dc:identifier>10.20372/nadre:4920</dc:identifier>
  <dc:identifier>oai:zenodo.org:4920</dc:identifier>
  <dc:relation>doi:10.20372/nadre:4919</dc:relation>
  <dc:relation>url:https://nadre.ethernet.edu.et/communities/aau</dc:relation>
  <dc:relation>url:https://nadre.ethernet.edu.et/communities/zenodo</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:title>Performance Comparison of Classifiers for Prospective Buyers Identification in ethio telecom Mobile Cross-Selling Market</dc:title>
  <dc:type>info:eu-repo/semantics/doctoralThesis</dc:type>
  <dc:type>publication-thesis</dc:type>
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