The Problem
The company processed transaction data from billions of payments for thousands of financial institutions. Almost none of it was sold back as insight. The question was whether that data could become a real business, with a strategy, a pricing model, and an operating model that held up.
The market was large and growing, on the order of several billion dollars a year at roughly ten percent annual growth. Adoption was shallow. Only a small fraction of the firm's own clients used analytics beyond basic reporting. A large market with low adoption can mean strong latent demand or a structural barrier. The work had to tell the two apart before the company committed.
The Results
The engagement defined and launched a new data business, built on proprietary analytical methods, with a strategy a board could defend.
Pricing was derived from the economics of the product rather than a markup on cost. A mixed-bundling, multi-tier structure let the firm sell a standard version to most clients and a premium version to the few who would pay for more. The operating model was designed as a factory. Production was standardized, the cloud infrastructure could expand and contract with demand, and the analyst-to-client ratio rose by roughly an order of magnitude. Within two years it was a multimillion-dollar business running ahead of aggressive targets.
Key Techniques
- First-principles economics of information goods: high fixed cost, near-zero marginal cost, experience-good uncertainty, and network effects.
- Pricing designed from microeconomics, using mixed bundling, version tiering, and price discrimination to match willingness to pay.
- Operating-model design for unit economics, with the cost structure built to absorb uncertainty.
- Market sizing and segmentation by asset tier, analytical maturity, and core-system relationship.