Commercial Economics
Make margin visible before inefficiency becomes permanent.
Price, cost, discount, and mix decisions are made every day — usually without a shared view of what they do to margin. The data to see it already exists in ERP, CRM, and billing. It has never been modeled as one system.
Margin does not disappear in one decision. It leaks through a thousand ungoverned ones.
Discount approvals, freight exceptions, contract renewals, and inventory positions each look reasonable locally. Without cost-to-serve and profitability modeled on a shared grain, nobody sees the compound effect until the quarter closes.
Price and cost live in different systems
Quotes sit in CRM, costs in ERP, rebates in spreadsheets. True product and customer profitability is a manual reconstruction.
Discounting has no feedback loop
Approvals are workflow events, not data. The organization never learns which exceptions destroy value.
Mix shift is invisible until the close
Revenue holds while margin erodes, and the drivers are discovered weeks after the decisions that caused them.
Outcomes
Profitability on a defensible grain
Product, customer, and channel margin models finance and commercial teams both accept.
Pricing decisions with context
Discount, cost-to-serve, and win-rate history visible at the moment of decision.
Early margin signals
Mix, leakage, and exception trends surfaced weekly instead of at close.
How the architecture works
01
Commercial cost model
ERP costs, logistics, rebates, and service effort aligned to transactions at a stable grain.
02
Gold profitability products
Customer, product, and deal-level margin with explicit allocation rules and owners.
03
Decision surfaces
Databricks SQL and AI/BI views for pricing, sales, and finance — governed by Unity Catalog.
What we implement
- Cost-to-serve modeling
- Product and customer profitability
- Discount and leakage analytics
- Demand and price elasticity datasets
- Inventory and mix analysis
- Executive margin dashboards
Where this shows up
Discount leakage review
Deal-level margin with approval history for one product family, exposing which exceptions repeat.
Cost-to-serve transparency
Customer profitability including freight, service, and returns — not just gross margin.
What should be measured
The business case is built on a baseline, not a promise. These are the numbers this solution is accountable to.
- Margin variance explained by driver
- Discount exception frequency and cost
- Time from margin question to defensible answer
- Mix-adjusted margin trend visibility
The first sensible pilot
Deal-level margin for one product family
Join price, cost, discount, and service data for one commercial domain and put the resulting margin view into a real pricing review cadence.
Questions
Is this pricing software?
No. It is the governed data foundation pricing decisions should stand on. If a pricing tool is warranted later, it inherits trustworthy inputs instead of another extract.
Will you tell us what to charge?
We make the economics visible and model the levers. Pricing strategy remains a business decision — made with far better information.
Related
Databricks SQL & AI/BI
Building Trusted Executive Analytics on Databricks
Executives do not need more dashboards. They need a small number of numbers that survive contact with finance, operations, and the next question.
9 min
Start with the business case
Find the first data or AI opportunity worth proving.
We evaluate the business problem, systems, data, architecture, and economics behind it—then identify the smallest production engagement capable of proving whether the opportunity is real.
Business case first · Architecture-led · Production-focused