From CLV to Action: A Lightweight Framework for Omnichannel Automation and Budget Allocation in SaaS

The increasing use of artificial intelligence (AI) in customer relationship management has enabled firms to automate interactions across multiple channels. However, many software-as-a-service (SaaS) companies struggle to align omnichannel automation and budget allocation with customer value, particularly in emerging market contexts where data and analytical resources are constrained. This study proposes an AI-enabled, yet non–machine-learning, framework that integrates short-horizon customer value estimation with operational risk signals to guide automation governance and resource allocation. Using an 18-month customer–month panel from a B2B SaaS company, the analysis examines cohort-based churn heterogeneity, validates the economic relevance of forward-looking value tiers, and evaluates journey-level outcomes in terms of churn, realized revenue, and automation spend. The results show that churn probabilities vary systematically by customer tenure and subscription tier, that short-horizon value tiers align closely with realized six-month revenue and retention outcomes, and that aggregating value and risk at the journey level provides a transparent basis for allocating automation budgets. Rather than prioritizing predictive complexity, the proposed framework emphasizes interpretability, managerial relevance, and feasibility. The findings demonstrate how explainable AI principles can be operationalized to improve omnichannel automation governance in SaaS firms, offering practical insights for organizations operating under data, budgetary, and capability constraints.