Customer revenue and usage API

What should be true before you start?

Before you integrate, understand what makes this different from a reporting feed. Six endpoints can each hand you a number; only Crossdeck hands you the join, because it owns the identity that connects revenue, entitlements, errors, and read-cost. If you read the app revenue API post, this is the layer underneath it — the reason the numbers connect.

Teams that do this well make the data model boring before they make the UI impressive. They decide what the product trusts, how the customer is identified, and which events prove that a premium flow worked. That upfront discipline prevents pricing changes, support escalations, or platform additions from turning into a rewrite later.

• Decide the pivot you need: from a customer, from an error, or from a cost spike.
• Know the customer id you will pass (your own developerUserId).
• Keep the secret key server-side; the cross-match is identity-scoped to one customer.

How should you implement this step by step?

The cross-match is one GET with a customer id. You pass who, and you get back what they pay, what they are entitled to, and what they cost — joined server-side by an identity you do not have to reconstruct. From the other direction, an error tells you how many of the affected users pay you. The join runs both ways, and it is the same join behind read-cost margin.

Implementation should move from trust to explanation. First make the purchase and access state reliable. Then add the events and context that explain whether the path is working for real customers. That order matters because a beautiful funnel built on unreliable access logic will still mislead the team.

• Call GET /v1/crossmatch?userId=... with your cd_sk_live_ secret key.
• Read data.revenue (monthlyCents, paying), data.entitlements.active, and data.readCost.
• From the errors layer, read affected.payingUsers to weigh an issue by revenue at risk.
• Render the joined view in support, ops, or success — instead of three disconnected tabs.

const r = await fetch("https://api.cross-deck.com/v1/crossmatch?userId=" + id, {
  headers: { Authorization: "Bearer " + process.env.CROSSDECK_SECRET_KEY }
});
const { data } = await r.json();
// one customer, joined: revenue x entitlements x read-cost
showCustomer(data.revenue, data.entitlements, data.readCost);

Where do teams make mistakes?

The mistake is treating the cross-match like six separate readouts instead of one joined model.

Most production problems here are not caused by missing one API call; they are caused by model mistakes. Teams mix catalog structure with access logic, treat frontend success states as final truth, or log events without preserving identity. Those shortcuts often feel fine during integration and expensive during the first real support incident.

• Pulling revenue, errors, and cost into three disconnected widgets and never joining them.
• Rebuilding identity yourself across vendors — the join is exactly what you cannot easily stitch.
• Asking for raw rows when the joined counts and amounts are what the decision needs.

How does Crossdeck operationalize the workflow?

Crossdeck owns the identity that joins revenue, entitlements, behaviour, errors, and read-cost. The Reporting API surfaces that join over a secret-key gate: /v1/crossmatch for one customer, affected.payingUsers for one issue. This is the moat made callable — not data, but the connections between data, rendered in your own product.

The sentence a competitor cannot produce: this customer pays $5/mo, holds 2 entitlements, and cost you 4,021 reads. Surfacing that in your own dashboard is surfacing Crossdeck's power, not just its data.

The operating win is not just cleaner instrumentation. It is that product, support, and engineering can all look at the same customer and reason from the same truth. That shortens the loop between insight, bug fixing, and revenue recovery.

What should a healthy rollout let your team do?

After rollout, the team should be able to inspect one customer and answer four basic questions quickly: what they bought, what access they should have, what they did before the key moment, and whether an error or product break interrupted the path. If those answers still live in different systems, the rollout is not finished yet.

A healthy setup should also make pricing, platform, and lifecycle changes cheaper. New SKUs, trial structures, payment rails, or premium features should mostly be mapping and instrumentation updates, not excuses to rewrite the access model from scratch.

• Trace one premium journey from paywall view to verified access.
• Confirm support can explain a paid-user issue without engineering stitching exports together.
• Review whether new products can be attached without changing feature checks.

What should you review after launch?

The first review cycle should happen with real production questions, not a checklist alone. Look at a new conversion, a failed payment or retry, a support ticket, and a customer who used a premium feature successfully. If the workflow is sound, those stories should be easy to reconstruct.

From there, keep reviewing the signal as an operating surface. The point is not only to collect data. It is to make the next pricing change, onboarding improvement, or incident response faster because the evidence is already joined.

• Review the earliest events that predict retained value.
• Check the gap between entitlement state and what the UI showed.
• Use the next support conversation as a live test of the model.

How should the whole team use the workflow?

A workflow like this becomes more valuable when it is not trapped inside engineering. Support should be able to confirm access and recent failure context. Product should be able to connect the path to adoption or conversion quality. Engineering should be able to see which state or step broke first.

When those three views line up, the system starts compounding. Each incident teaches the team something about pricing, onboarding, premium UX, or instrumentation instead of dying as a one-off ticket.

• Support: confirm entitlement state and the last premium action quickly.
• Product: review which steps correlate with value or friction.
• Engineering: prioritize breaks by customer and revenue impact.

Frequently asked questions