Article: Simulation enhanced early warning and intervention model for customer success in startups using value alignment Journal: International Journal of Simulation and Process Modelling (IJSPM) 2026 Vol.23 No.7 pp.1 - 16 Abstract: Subscription startups face high churn and resource limits, demanding efficient customer success solutions. We propose a simulation-driven, intelligent framework that models the customer value alignment process. This framework converts business goals into value intent vectors and leverages a process model to simulate customer behaviour and intervention outcomes. Using graph neural networks, we measure value alignment between behaviour and goals, integrating a counterfactual intervention generator with combinatorial optimisation for dynamic action planning. Evaluated on 12,850 real customers, our model achieves 0.904 AUC and 78.4% 30-day retention, outperforming gradient boosting and LSTM baselines. Simulation results also demonstrate a reduction in calibration error and a cut in customer success manager intervention rates by 18%. This framework enhances early-warning timeliness and intervention efficacy, enabling a rapid shift from behavioural alerts to preemptive actions. Ultimately, it delivers a high-ROI, intelligent solution tailored for resource-constrained startups, balancing predictive accuracy with actionable effectiveness to mitigate churn effectively. Inderscience Publishers - linking academia, business and industry through research

Title: Simulation enhanced early warning and intervention model for customer success in startups using value alignment

Authors: Hong Chen; Zuwei Yu; Shangui Hu

Addresses: College of Business Administration, Ningbo University of Finance and Economics, Ningbo 315175, Zhejiang, China; Research Center for the Regional Comprehensive Economic Partnership (RCEP) Member States, Ningbo 315175, Zhejiang, China ' Department of Economics and Trade, Guangdong Eco-Engineering Polytechnic, Guangzhou 510520, Guangdong, China ' College of Adult and Continuing Education, Ningbo University of Finance and Economics, Ningbo 315175, Zhejiang, China

Abstract: Subscription startups face high churn and resource limits, demanding efficient customer success solutions. We propose a simulation-driven, intelligent framework that models the customer value alignment process. This framework converts business goals into value intent vectors and leverages a process model to simulate customer behaviour and intervention outcomes. Using graph neural networks, we measure value alignment between behaviour and goals, integrating a counterfactual intervention generator with combinatorial optimisation for dynamic action planning. Evaluated on 12,850 real customers, our model achieves 0.904 AUC and 78.4% 30-day retention, outperforming gradient boosting and LSTM baselines. Simulation results also demonstrate a reduction in calibration error and a cut in customer success manager intervention rates by 18%. This framework enhances early-warning timeliness and intervention efficacy, enabling a rapid shift from behavioural alerts to preemptive actions. Ultimately, it delivers a high-ROI, intelligent solution tailored for resource-constrained startups, balancing predictive accuracy with actionable effectiveness to mitigate churn effectively.

Keywords: customer success; value alignment; graph neural network; counterfactual intervention; early warning system.

DOI: 10.1504/IJSPM.2026.155812

International Journal of Simulation and Process Modelling, 2026 Vol.23 No.7, pp.1 - 16

Received: 04 Jan 2026
Accepted: 26 May 2026
Published online: 14 Aug 2026 *

Title: Simulation enhanced early warning and intervention model for customer success in startups using value alignment

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