Designing Growth Incentives for Platforms: A Causal Evidence Synthesis on Referrals and Cohort Profitability

Digital platforms increasingly depend on referral programs and incentive mechanisms to accelerate user acquisition and improve profitability trajectories. While prior research has examined referral marketing effectiveness, evidence on its causal impact across user cohorts and long-term platform profitability remains fragmented. This paper synthesizes causal evidence from multidisciplinary studies spanning economics, marketing, data science, and platform management on how referral incentives shape user behavior, engagement persistence, and cohort-based profitability. We develop a comprehensive framework situating referral programs within platform growth strategies, emphasizing design trade-offs, incentive calibration, and externalities across network participants. Empirical studies reviewed highlight heterogeneous treatment effects of referral incentives depending on market maturity, platform category, and user demographics. Using a structured causal evidence synthesis, we identify conditions under which referrals drive sustainable profitability versus scenarios that trigger adverse selection, churn, or inflated acquisition costs. Our findings contribute to theory by integrating causal inference methods with platform strategy scholarship and to practice by offering evidence-based guidelines for designing scalable, profitable referral systems. The paper concludes with strategic implications for policymakers and managers regarding sustainable growth incentives in digital platform ecosystems.