Bayesian Prioritization in Product Strategy: Embedding Predictive Analytics into Agile Decision-Making

Abstract

In dynamic product environments, widely used methods for prioritization often don’t take into account uncertainty and earlier context. A Bayesian predictive analytics model is proposed in this paper for sorting backlog tasks, making use of feature usage data, customer division and rates of installation success. With probabilistic reasoning, the model improves objectivity and follows the company’s strategic direction. We explain how Bayesian inference helps us make decisions using available data and reduces bias when planning sprints. Trials with agile teams have confirmed that they see better organizational planning, stakeholder collaboration and final product results. The results support using data and context in decision-making by product teams today.