Revenue pivots
Orb’s revenue recognition dataset is meant to be as granular as possible, allowing you to split data by the following axes:
| Pivot dimension | Example use case |
|---|---|
| Customer | A finance team wants to analyze revenue performance per customer to identify high-value clients and potential churn risks. Operationalized by tracking the revenue generated by customer A over the last quarter. |
| Plan | Comparing the revenue from the Basic Monthly plan versus the Pro Annual plan to determine which plan drives more consistent revenue. |
| Item | Assessing the revenue contribution of file processing services versus platform access fees. |
| Billable Metric | Evaluating how revenue differs across different implementations of a single conceptual measurement to understand which one is most effective. |
| Invoice | Auditing the revenue recognized from Invoice #12345 to verify it aligns with the usage and services provided. |
| Prepaid credit block | Tracking the consumption and expiration of prepaid credit blocks to ensure accurate revenue recognition and forecasting. |
Notably, Orb supports pivoting on multiple axes at once. For example, you might want to power the following use cases:
- Customer + Plan: Identify which plans are most popular among various customer segments. For instance, analyze revenue from Customer A across both the Basic Monthly and Pro Annual plans to determine customer preferences.
- Plan + Item: Determine the contribution of different items within each plan. For example, evaluate the revenue from file processing versus platform fees within the Basic Monthly plan to optimize plan offerings.
- Item + Block: Monitor the usage of prepaid credits against specific items. Track how many prepaid credits were used for file processing services versus compute fees.
- Invoice + Billable Metric + Block: Verify that invoicing, pricing, and credit block usage are aligned. Ensure that the pricing applied on Invoice #6789 accurately reflects the consumption of prepaid credits and any discounts given.