Market Data Spend Analytics

• Benchmarking current contracts to assess competitiveness against the market, providing transparency into how pricing compares with both the peer group/market.
• Guidance for vendor renewal negotiation to strengthen commercial outcomes and uncover opportunities for cost avoidance, whether through mitigating price increases or leveraging negotiation strategies to secure real savings.
• Insights for internal budgeting and allocation, helping teams make informed, strategic decisions around market data spend.
• Independent validation of pricing and payments for market data products, supporting internal reporting and demonstrating efficient spend management to key stakeholders.
• Contract language benchmarking to evaluate terms against industry standards.
• Market rate “sanity checks” to verify whether product pricing aligns with prevailing market levels.
• Scenario analysis to model pricing or use case changes and understand their commercial and strategic impact.

• To ensure benchmarks are truly comparable, we use a system of Dynamic Peer Groups. These groups are built from clients with closely aligned product usage patterns and are recalibrated for each benchmark to maintain accuracy and comparability. This approach produces benchmark that has a tailored and reliable comparison group.
• The underlying models incorporate key pricing drivers and commercial levers, capturing the factors that most influence each vendor’s pricing structure. Because these drivers differ across products and licenses, each benchmark uses its own peer group and methodology to ensure a true like for like, apples-to-apples comparison.
• Peer groups also adapt to additional variables, including license type (enterprise vs. individual), firm size (where relevant), contract duration and start year, bundling arrangements, and other commercial nuances. This ensures that each benchmark reflects the client’s actual consumption profile and commercial context.

• A BOTTOM-UP APPROACH is used to benchmark each client’s market data setup at a highly granular level. This involves reviewing every agreement line by line, product by product, and license by license to identify the specific pricing drivers associated with each product and vendor.
• As a result, every benchmark is tailored to how the client actually uses each product, rather than relying on broad or generic peer definitions. This ensures that every comparison reflects actual product usage, rather than grouping clients into a broad or arbitrary category of “similar firms”.

• Financial institutions are steadily gaining more leverage and flexibility, even within a market still dominated by powerful incumbent vendors. Through our work with clients, we routinely see meaningful successes, such as avoiding significant costs from initial proposals and deploying alternative negotiation strategies informed by market trends and shared client insights.
• In segments where only a few viable alternatives exist, negotiations naturally become more challenging and the focus shifts on reducing price increases and optimizing each vendor relationship. However, in these instances, our data shows that informed, well managed negotiations still deliver meaningful improvements over time.
• Across other parts of the market data budget, where viable alternatives are available, buyers benefit from far stronger leverage. In these areas, material savings are both realistic and highly achievable.

Most benchmarks are delivered within 2-4 weeks once we have received the data. Deliveries and updates then happen throughout the year, including alerts on important changes in pricing models across the market.

No. We provide the benchmarks and insights that clients can directly leverage in their negotiations. As additional questions inevitably arise after each vendor meeting, the account management team works closely with clients throughout every stage of the negotiation process, diving deeper wherever needed to support informed, confident decision making.

All client data in this analysis is handled in full compliance with data privacy regulations and internal governance standards. Information is anonymised before analysis, access to raw data is restricted to authorised personnel, and outputs are aggregated to prevent identification of any individual client or transaction.