Optimization Algorithm for Creating Price Tiers in The Customer Migration Process Between Companies

This research study reviews the application of optimization algorithms for a case of customer migration between two telecommunications companies. In this scenario, a group of customers was migrated from one company to another, with some changes in their subscription fees. The goal was to determine the values that would minimize the revenue loss for the company receiving these customers. This analysis applied heuristic optimization methods in a hybrid form, utilizing a Genetic Algorithm [GA] combined with a Particle Swarm Optimization Algorithm [PSO] to find an optimal solution. This hybrid method, called Swarm Genetic Algorithm [SGA] aimed to leverage the high exploration capacity of the GA and the high exploitation capacity of the PSO to identify local minima. The study also established a predictive model to assess whether the final pricing would impact customer satisfaction, assisting the business area in deciding how many price points should be created. After the execution of the final algorithm, an optimal solution was found, resulting in a 48% reduction in revenue loss after the migration process and a 1.7% increase in monthly gross revenue post-optimization.