Kunal’s letter in Q2 referenced experimenting with consumption-based models. What is the impetus for this? Is the seat-based model no longer providing revenue commensurate with value? How do you distinguish between value-based and consumption-based pricing, and how will that distinction shape your approach? For example, one user may consume data infrequently yet derive significant value, while another may pull large volumes of more replicable data – how do you reconcile the two?

Kunal’s letter in Q2 referenced experimenting with consumption-based models. What is the impetus for this? Is the seat-based model no longer providing revenue commensurate with value? How do you distinguish between value-based and consumption-based pricing, and how will that distinction shape your approach? For example, one user may consume data infrequently yet derive significant value, while another may pull large volumes of more replicable data – how do you reconcile the two? Historically, we have used seat growth to measure incremental client value for products like the PitchBook platform and Morningstar Direct, although a large portion of the Direct Platform business is not seat-based. Value-based pricing establishes a fee level (typically an annual license fee) that reflects business impact, expected buyer return on investment, and/or realized outcomes without considering the number of seats. Consumption-based pricing links client fees to data consumption (e.g., application programming interface (API) calls) and customers pay for the amount of data they consume, so these fees inherently vary from period to period. As customers adopt tools and technology to execute more work with the same (or smaller) workforce, consumption-based models may better capture the value of our data and intellectual property (IP) in certain businesses and customer segments. For example, we see increased adoption of AI-powered tools and technology for deal sourcing among general partners and M&A diligence workflows at investment banks. This adoption coincides with increased use of our data and IP in these tools. As a result, our approach to commercialization may evolve to more appropriately monetize our content. We are working with our clients to match price to the value delivered from our data, research, and scope of our enterprise relationships.