Optimizing Merchant Discount Rate (MDR) or one of the Largest Merchant Acquirers in South America

Abstract

Merchant Acquirers (‘MA’) play the essential role of accepting/ capturing, processing and settling the payments made through cards (credit, debit, etc.) for purchases made at merchants/retailers. This MA had a client portfolio of around ~one million merchants with the mass market segment consisting of SMB merchants forming the major portion. The MA was also operating in a highly competitive market with thin operating margins due to a highly competitive pricing environment. The MA was interested in understanding the mass market merchant’s behavior, spending pattern, growth, price elasticity and competitive effect as reasons for lower growth & profitability. The objective sought was to identify the right set of merchants who were less elastic and then adjust their Merchant Discount Rate (MDR) by few basis points to improve revenue & profitability without losing market or wallet share. As a first step in this study, a robust statistical segmentation was developed using unsupervised machine learning technique to reveal the pattern of merchant’s spend, product usage, growth trend(QoQ)and customer risk. A non-linear differential price elasticity function was used to analyze the impact of price and the influence of a number of qualitative factors on revenue. The results of modeling suggested that the most significant factors impacting price besides merchant elasticity were - rental price of POS, regional effect, number of excess POS or PDV machines at the merchant location. For each individual segment, price elasticity along with an optimization algorithm with complex constraints (maximum % change in price, maximum loss of market share etc.) was developed to optimize the MDR price and achieve optimal revenue. An in-market testing of this solution was done for a sample of merchants. A factorial design of experiment was used to design the campaign and build a test & control group. Based on the price change suggested by the pricing model, more than 150% incremental revenue lift was observed for test over control. In 2014, from almost 4 segments, the acquiring bank has realized annualized additional incremental revenue in the range of R$ 10 MM. In next phase, opportunities to crosssell other products and other fee sources will be investigated in parallel to drive more revenue from merchants.