Business Analytics for Dual-Channel SaaS Pricing under Demand, Supply, and Cybersecurity Risk
Main article
Abstract
Software-as-a-service (SaaS) firms increasingly sell through dual channels: low-friction self-service subscriptions for individual users and negotiated enterprise contracts for organizational clients. This structure improves market reach but also exposes pricing decisions to demand volatility, cloud supply uncertainty, information asymmetry with infrastructure partners, and cybersecurity-related trust losses. This article develops a business analytics framework for dual-channel SaaS pricing under demand, supply, and cybersecurity risk. Instead of extending a purely mathematical service supply chain model, the study translates the problem into an analytics-driven pricing architecture that combines demand segmentation, cloud-capacity planning, risk scoring, and governance-aware scenario analysis. A stylized numerical experiment is designed for a mid-sized SaaS provider operating B2C and B2B channels. Six scenarios are compared: a transparent benchmark, infrastructure information gaps, stochastic demand, cloud supply risk, cybersecurity exposure, and analytics-enabled mitigation. The results show that unmanaged information and risk reduce expected quarterly operating profit from USD 2.86 million to USD 2.31 million, while analytics-enabled mitigation recovers USD 0.28 million and improves the final profit to USD 2.59 million. Channel-level analysis indicates that the B2B segment is less price-sensitive but more vulnerable to security and service-level shocks, whereas the B2C segment is more sensitive to demand volatility and churn. The study contributes to business analytics research by showing how pricing, capacity, and cybersecurity decisions can be evaluated in one managerial framework without relying on excessive formulaic complexity. The findings provide practical guidance for SaaS managers who must align subscription pricing, cloud commitments, and digital trust investment under uncertainty.
