Based on increasing user demands, domestic e-commerce transactions are encouraged across international borders. A product/service demand forecast is necessary to improve the transaction rate for stagnancy-less returns. Though different methods have been employed to forecast user demands, this article introduces a different proposal using fuzzy C-means clustering. It is named as Iterative C-means Clustering Method (ICCM), in which the returns based on transaction and goods stagnancy are separated. The clustering process is iterated throughout the transactions to differentiate the above and identify the actual demand across borders. Using this differentiation, the demand that defaces the returns is classified through minimum degree of stagnancy. Such identified process retards the cross-border transaction for false user demand forecasts, ensuring the goodwill of the products/services. The fuzzy-based derivative groups are disintegrated post the transaction completeness to improve the prediction efficacy.

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