This work proposes a novel customer segment-based pricing scheme, CuPric, tailored for Mobile Sensors-as-a-Service (mSe-aaS) architecture for IoT Applications. mSe-aaS offers flexible and scalable mobile sensor services for different IoT applications. However, multiple actors involved in an mSe-aaS architecture participate in order to earn profit or access services. Therefore, such financial transactions among the actors lead to the need to develop an optimal pricing scheme. On the other hand, Customer segmentation is necessary to classify users based on their specific usage patterns and resource requirements, ensuring customized service offerings and optimized pricing strategies. CuPric utilizes customer segmentation techniques, including Recency-Frequency-Monetary (RFM) analysis and K-Means clustering. We modify the RFM technique by replacing the monetary component with computation resources fitting into the context of mSe-aaS. Further, we model the proposed pricing problem as an optimization problem and apply the Lagrangian multipliers while ensuring the infrastructure is allocated efficiently, service provider expenses are covered, and the Sensor-Cloud Service Provider (SCSP) profit is maximized. The proposed scheme, CuPric, adapts to varying resource demands and provides insights into user behavior, leading to more strategic infrastructure allocation and enhanced overall service efficiency. Through extensive simulations, we demonstrate that introducing customer segmentation earns at least 37% better profit. Additionally, 95% of users are charged less than half the maximum price charged to any user in the CuPric.