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Lead management optimization using data mining: A case in the telecommunications sector
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DOI：10.1016/j.cie.2021.107122.png)
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Abstract
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The growing competitiveness of the market has put pressure on companies to improve their customer relationship management strategies. In an era where mass marketing techniques are inadequate, lead management is at the forefront to provide a customized approach to customer acquisition. For this, lead management depends on the correct selection of leads and decision making on what type of approach to take to satisfy the requirements of customers. However, currently, firms are faced with massive quantity of data regarding customers and prospects. Data mining is a solution to cope with this problem, providing a robust approach to massive quantity of data and its complexity. In literature, there is a lack of documented applications of these techniques in lead management. In this sense, we propose a methodology that aims to improve efficiency on the distinct maturity stages of leads management. Also, the methodology aids in support the decision-making regarding the segmentation of leads. This research suggests the application of data mining techniques in the optimization of leads management processes, from capture to conversion, with the objective of improving customer conversion effectiveness. A case study was conducted in a telecommunications company. It was possible to implement the proposed method to estimate the probability of conversion for each lead. With this, was possible to segment the offer to each type of lead.
Keywords:
Lead Management
Data Mining
Predictive Models
Machine Learning
Telecommunications
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