Authors:
Patrick Hosein
;
Shiva Ramoudith
and
Inzamam Rahaman
Affiliation:
Department of Computing and Information Technology, The University of the West Indies, Trinidad and Tobago
Keyword(s):
Resource Allocation, Marketing, Optimization, Data Mining, Machine Learning.
Abstract:
Many companies and institutions, such as banks, typically have a wide range of products that they make available to customers. However, such products must be marketed to their customers, especially when the product is new. Phone calls, emails, postal mail, and online advertisements are among the ways companies can market products to specific customers. However, the cost incurred during marketing increases with every contact made. Phone calls are the most personal means of targeted marketing but also the most costly. In telemarketing, a company can make multiple calls to a single customer with each call incurring a human resource cost. Such calls may or may not be able to persuade a customer to subscribe to the service or product. Some customers might subscribe after the first call. Some customers might require several calls to convince them. Other customers might never be persuaded. In light of limited resources, to maximize return, a company would need to determine which customers t
o contact and how many attempts to make for a customer. In this paper, we present a mathematical model for this problem in which, given a marketing budget of calls, one can determine a policy for selecting customers to target along with the optimal number of calls to use for each selected customer. We illustrate our model using a Portuguese banking dataset and show that our model can achieve significantly higher levels of success performance.
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