Case Study

Pricing of Merchant Terminal Fees for the Business Bank of a Major Bank

Banking

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Pricing

A major Australian bank came to us to revisit their pricing for existing merchant relationships. The bank wanted to identify customers who were unprofitable from a total relationship perspective, and reprice based on profitable customers with equivalent usage profiles.

Client Challenge

Reprice fee schemes for unprofitable merchant relationships

During a period of steady increases in credit transaction amounts, our client’s net income from their customers using a merchant terminal for processing credit cards had remained flat.

The bank wanted to identify the merchant customers who were unprofitable or had low profitability from a total relationship perspective, and then reprice based on customers with equivalent usage profiles who were profitable.

Once the merchant customers were identified, the challenge was then to recommend an adjusted price which was still competitive, but also took the product mix observed in the market into account.

Our work included:

  • Pricing models
  • Forecasting
  • Cohort analysis
  • Data preparation

The Solution

Reprice both credit and debit transaction fees in a single framework

The solution had to account for the data being highly fragmented. For example a merchant might have only a partial banking relationship with our client and may not settle the transaction amount within an account held with our client. In addition, we needed to look at debit transaction pricing and the fees the merchants pay on debit transactions.

We applied modern statistical techniques that allowed us to reprice every customer irrespective of the data fragmentation, and also to reprice both the credit and debit transaction fees in a single framework so that pricing could account for each merchant’s transaction mix.

Our analysis found that we could reprice the debit and credit transactions and still have one of the most competitive offerings in the market. Furthermore, the repriced fees are also more fair as it also opened up the reduction of pricing customer cohorts who were based on historical pricing models that were unnecessarily high.

Business Impact

Over $5m in incremental profit over 3 years with no attributable attrition

Based on the recommendations, the client repriced some of their merchants and generated an incremental profit of over $5m, even during a time of slowing business activity due to the global pandemic. There was also no attrition attributable to the repricing.

Our client is in the process of testing repricing on other targeted groups of customers with an estimated benefit of $15m in incremental revenue. The exercise opened up the opportunity to reprice larger pools of remaining customers with incremental benefits estimated to be in the hundreds of millions.

All re-pricing exercises included adjustments that accounted for the total business relationship with each customer. This flexible repricing modelling can be integrated with any future change in scheme fees with minimal manual effort.

  • Repricing an initial cohort will generate an incremental profit of over $5m over 3 years
  • A flexible repricing modeling process that can be used to incorporate future changes in scheme fees
  • A fairer outcome for customers who were priced on high historical pricing models, and had their pricing reduced as a result