Finding the best statistical model to predict customer defection in telecommunication retail setting
Date
2014-07-30
Authors
Ngcongo, Nkululeko
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Abstract
In this study we examine the question of which statistical mod-
els work well in predicting customer defection in the retail mobile
telecommunication industry. For each of the two data sets that were
used (mobile call pattern and billing, and time taken to churn data),
four statistical models were tted and compared namely; arti cial
neural networks, decision trees, logistic regression and support vector
machines. The arti cial neural network model proved to be supe-
rior than the other three models when tted on both data sets. This
model gave the best area under the receiver operating characteristic
curve (0.93 for call pattern data and 0.88 for billing and time taken to
churn data), highest lift at 10 per cent of the population (7.01 for call
pattern data and 2.12 for billing and time taken to churn data) and
lowest misclassi cation rate (0.04 for call pattern data and 0.19 for
billing and time taken to churn data). The logistic regression model
under performed the other models when tted to call pattern data and
came out as third when tted to billing and time taken to churn data
whereby they outperformed the decision tree model. Support vector
machine came out as the second best model for billing and time taken
to churn data and third when tted to call pattern data. Decision
tree model performed well when tted to call pattern data and worst
when tted to billing and time taken to churn data The study showed
that in the retail mobile telecommunication industry, companies can
increase revenue streams and competitive advantage by using data
mining techniques to predict customers that are likely to churn. The
next step for the business is to embark on retention programs to use
these methods to reduce churners.
Description
A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Mathematical statistics. Johannesburg, February 2014.