A Framework for the Adoption of Marketing Automation Strategies by Impact Tech Start-ups in South Africa

This study investigates the critical factors influencing the adoption and sustainability of marketing automation tools among Impact Tech Startups (ITS) in South Africa. Amid accelerating digital transformation, marketing automation offers startups a pathway to scale operations, personalize customer engagement, and optimize limited resources. Employing a qualitative methodology, the research draws on in-depth interviews with sixteen ITS founders and managers to explore internal and external factors shaping adoption decisions and long-term sustainability outcomes. Findings highlight internal enablers such as leadership commitment, financial support, and employee capability as foundational to successful adoption. Leadership and funding were key drivers, while employee readiness emerged as both a challenge and an opportunity for capacity building. Externally, customer demands for personalized, real-time interactions and competitive market pressures strongly influenced adoption behavior. Although some participants reported experiencing persistent vendor marketing, reactions varied, ranging from appreciation to resistance, underscoring the importance of trust, timing, and relevance in vendor outreach. Marketing automation is broadly perceived as enhancing the sustainability of ITS by increasing brand visibility, improving operational efficiency, and enabling scalable customer acquisition. For early-stage startups, these tools have become essential to achieving business continuity and growth through cost-effective marketing strategies. This study expands the limited literature on marketing automation in emerging markets and startup contexts. It provides practical insights for startup founders, policymakers, and technology vendors seeking to support digital adoption. Ultimately, the research emphasizes the need for context-sensitive strategies and policy frameworks that align with the dynamic challenges faced by startups in resource-constrained environments.