A leading stock exchange was looking optimizing pricing strategy for derivatives trading.
A leading stock exchange wanted to optimize its pricing strategy for derivatives trading. It needed to increase revenue to support inflationary pressures and investment needs. But it was crucial that the new fee model did not alienate market makers and also maintained profitability.
The existing rebate management was also a challenge. It was outdated and had drifted from the actual client value and trading behavior over time. Our client was now looking to re-design the rebate structures, introduce price differentiators for expected uplift per market maker, and establish fallback options. These strategies were expected to significantly enhance the exchange's rebate management practices, offering competitive incentives to market makers while maintaining profitability.
We developed a data driven pricing strategy based on clear guardrails, client behavior, and tailored incentives to balance revenue goals with fairness and market alignment.
1. Define guardrails and pricing principles
We established strategic guardrails and a clear pricing philosophy to implement moderate price increases that minimized client attrition while balancing revenue needs. The new pricing structure was designed to be in line with industry benchmarks, while also reflecting client willingness to pay. So, the price increases were tailored by market segment to ensure fairness and relevance, with fallback options in place to mitigate resistance. The new fee structure was designed to be non-discriminatory, free of retroactive discounts, and predictable for clients.
2. Conduct deep analytics and elasticity assessments
A closer look into its clients’ trading activities revealed pricing disparities across similar commitment levels, largely due to historically established fee schedules and rebate models. To account for the diverse trading behaviors of the exchange’s clients, we introduced volume bands for variable fees by aligning fees with the actual cost-to-serve for each client behavior profile. This volume-based structure reduced revenue leakage and enabled dynamic adjustments based on market conditions or cost changes.
3. Plan cost per trade for the new fee model
We designed a two-part tariff model to separate trading and technical fees as part of the new harmonized fee structure. This separation allowed better planning of commitment levels, catering to both high-volume and low-volume clients with tailored fee structures.
4. Introduce differentiators, calibrate, and validate the model
We developed a comprehensive pricing model with new price differentiators, including listing rebates, group activity rebates, and client loyalty rebates for market makers. Additionally, we conducted extensive simulations using historical client activity and price sensitivity to validate the new pricing model and assess its impact. We also provided a detailed roll-out plan, including a follow-up list for the sales team.
Total revenue increased by 9.9% with minimal loss of clients
Our strategy created a transparent fee model that ensured predictable revenue stream. Overall, total revenue increased by 9.9% with minimal client attrition.
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