Dynamically Tie the Right Offer to the Right Customer in Telecommunications Industry

TL;DR

This paper introduces a dynamic approach to customer segmentation and targeting in telecommunications, integrating customer lifetime value models with genetic algorithms and data structures to optimize marketing strategies.

Contribution

It presents a novel integrated model linking customer segmentation and targeting using genetic algorithms and data structures, enhancing relevance for marketers.

Findings

Optimized marketing strategies using genetic algorithms.

Dynamic customer targeting with LOSSYCOUNTING and Bloom Filters.

Improved relevance of customer segmentation results.

Abstract

For a successful business, engaging in an effective campaign is a key task for marketers. Most previous studies used various mathematical models to segment customers without considering the correlation between customer segmentation and a campaign. This work presents a conceptual model by studying the significant campaign-dependent variables of customer targeting in customer segmentation context. In this way, the processes of customer segmentation and targeting thus can be linked and solved together. The outcomes of customer segmentation of this study could be more meaningful and relevant for marketers. This investigation applies a customer life time value (LTV) model to assess the fitness between targeted customer groups and marketing strategies. To integrate customer segmentation and customer targeting, this work uses the genetic algorithm (GA) to determine the optimized marketing strategy. Later, we suggest using C&RT (Classification and Regression Tree) in SPSS PASW Modeler as the replacement to Genetic Algorithm technique to accomplish these results. We also suggest using LOSSYCOUNTING and Counting Bloom Filter to dynamically design the right and up-to-date offer to the right customer.