February 22, 2020 Thesis Open Access

Getachew Gemechu

DCAT Export

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