In the payment industry context, a merchant acquirer is a firm that facilitates communication between a retailer (online or brick–and–mortar store) and the issuing banks. For an acquirer, it is crucial to determine the transactional potential of each retailer to guide proper pricing and risk management strategies. In this work, we propose a framework to properly assess the transactional potential of any retailer using the transactions of its peers. The proposed framework is based on the construction of a probabilistic counterfactual that uses non-parametric Nadaraya-Watson kernel regression to model differing seasonal patterns, trends and business cycles. We propose an integrated data processing methodology to separate and validate the data not affected by interventions to construct our non-parametric probabilistic counterfactual model. The proposed framework is a powerful decision support system for a merchant acquirer revenue management, with direct applications to pricing, churn detection and, more generally, revenue management. Empirical results corroborate the effectiveness of the method against relevant benchmarks.