Prescriptive Analytics

Prescriptive analytics answers the question of what to do about a prediction, not just what will happen. While predictive analytics might forecast that a user has a 70% churn probability, prescriptive analytics recommends the specific intervention, like offering a particular discount or triggering a customer success call, most likely to prevent that churn.

For growth teams, prescriptive analytics closes the loop between insight and action. AI enables prescriptive systems through reinforcement learning that optimizes actions based on outcomes, causal inference that estimates the impact of different interventions, and optimization algorithms that find the best action given constraints. Growth engineers should build prescriptive capabilities on top of their predictive foundation, adding action recommendation and outcome optimization layers. Key applications include next-best-action systems for customer engagement, budget allocation optimization across channels and campaigns, and pricing optimization that recommends optimal prices given demand and competitive conditions. The main challenge is building the feedback loops that allow prescriptive systems to learn from the outcomes of their recommendations, requiring disciplined experimentation and outcome tracking infrastructure.