Swarm intelligence goal-oriented approach to data-driven innovation in customer churn management

One type of data-driven innovations in management is data-driven decision making. Confronted with a big amount of data external and internal to their organization's managers strive for predictive data analysis that enables insight into the future, but even more for prescriptive ones that use algorithms to prepare recommendations for current and future actions. Most of the decision-making techniques use deterministic machine learning (ML) techniques but unfortunately, they do not take into account the variety and volatility of decisionmaking situations and do not allow for a more flexible approach, i.e., adjusted to changing environmental conditions or changing management priorities. A way to better adapt ML tools to the needs of decision-makers is to use swarm intelligence ML (SIML) methods that provide a set of alternative solutions that allow matching actions with the current decision-making situation. Thus, applying SIML methods in managerial decision-making is conceptualized as a company capability as it allows for systematic alignment of allocating resources decisions vis-`a -vis changing decision-making conditions. The study focuses on the customer churn management as the area of applying SIML techniques to managerial decision-making. The objectives are twofold: to present the specific features and the role of SIML methods in customer churn management and to test if a modified SIML algorithm may increase the effectiveness of churnrelated segmentation and improve decision-making process. The empirical study uses publicly available customer data related to digital markets to test if and how SIML methods facilitate managerial decision-making with regard to customers potentially leaving the company in the context of changing conditions. The research results are discussed with regard to prior studies on applying ML techniques to decision-making and customer churn management studies. We also discuss the place of presented analytical approach in the literature on dynamic capabilities, especially big data-driven capabilities.