Before you can raise UA financing, your data needs to be able to tell the story on its own.
Mar 16, 2026 - 6 min read
Most studios fail the UA financing eligibility test because their cohort unit economics are not ready or suitable for such a structure. UA financing follows demonstrated payback. If cohorts do not show a credible path to recovering acquisition cost, no level of reporting discipline will change that.
But there is a second filter – one that becomes decisive when economics are promising.
When payback appears viable, the next question is simple: Can the performance be independently reconstructed and verified?
This is where many otherwise financeable studios encounter friction. Not because their game is weak – but because their historical data cannot withstand scrutiny.
If there is a realistic possibility that you will explore UA financing in the next six months, these are the structural foundations you should already have in place.
This is a practical, experience-based guide for mobile studios whose data setup isn’t finance-ready yet – and who want to fix it before the clock runs out.
Future-you will thank you.
The foundation of underwriting is raw, reconstructable revenue data. At minimum, you should maintain a clean, append-only record of:
If this data exists and has been preserved consistently and with the same methodology in defining each metric since launch, cohorts can be rebuilt and LTV can be re-modeled independently.
Attribution platforms and dashboards can provide helpful structure, but they are not the core requirement. What matters is whether historical performance can be reconstructed from first principles - not whether it looks clean in a presentation.
Action today:
Run a simple audit. Confirm that you can export a complete historical log containing user ID, install date, transaction timestamp, gross revenue, and net revenue for every transaction since launch.
If this does not exist in one clean, append-only format, prioritize building that pipeline now - before scale makes reconstruction impossible.
Dashboards are useful operational tools. They are not substitutes for underlying data integrity. A simple internal test can reveal whether your reporting is finance-ready:
If someone rebuilt your cohorts from raw transaction data, would the results match what you currently report? Think about the following:
If the answer is yes, diligence tends to be straightforward.
If the answer is no – because definitions evolved, logic changed midstream, or exports no longer reconcile – confidence erodes quickly.
This is not about BI sophistication, it is about consistency: performance metrics must be reproducible from preserved historical data.
Action today:
Take one historical cohort – ideally from three to six months ago – and rebuild its retention and ROAS manually from raw transaction data.
Compare the results to what your dashboard reports. If there is a discrepancy, document why – then standardize the logic going forward.
Underwriting depends on reliable acquisition cost per cohort.
Spend should be:
Real-time numbers do not need to be perfect, but historical spend must converge to a stable record. If past acquisition costs continue to shift without documentation, it becomes difficult to establish true payback dynamics. At that point, the conversation shifts from growth potential to reporting controls.
Modest spend with clean historical reconciliation is far more financeable than scaled spend with structural gaps.
Action today:
Choose a closed historical month and reconcile total reported spend by channel against invoices and platform exports. Adjust for fraud and clawbacks.
Once finalized, freeze that month’s acquisition cost in your reporting. Going forward, establish a monthly “crystallisation” process so historical CAC does not drift.
This is often the most underestimated step. Your:
Should be within a 5% variance, with differences that are explainable - whether due to FX, timing, platform reporting delays, or accounting treatment. The operational data should ideally form the basis for the financial reporting.
The same applies to marketing spend and the P&L.
When operational data reconciles to financial statements, the focus remains where it belongs: on unit economics and scalability.
When it does not, discussions drift toward process, controls, and reliability – areas that introduce friction, but rarely create value.
Action today:
Pick one completed quarter and tie:
Do the same for marketing spend. If differences exist, document the drivers (FX timing, accounting treatment, reporting delays). Make reconciliation a recurring discipline, not a one-time exercise.
Clean, well-structured data alone does not make a game financeable.
If cohorts do not demonstrate credible payback, capital will not follow. Many studios that explore UA financing simply have not yet reached that inflection point in their economics.
However, when unit economics are close – or already working – the ability to independently reconstruct performance becomes decisive.
Financiers will re-model your LTV.
They will rebuild your cohorts.
They will test sensitivity across retention and monetization assumptions.
If your underlying data supports that process, underwriting can move efficiently. If it does not, even promising economics become difficult to validate.
Data infrastructure is rarely built with financing in mind. It is built for operations, iteration, and learning.
But once capital enters the equation, historical integrity becomes a structural asset.
You cannot retroactively fix missing transaction logs.
You cannot reconstruct cohorts that were never preserved.
You cannot reconcile numbers that were never tied to financial statements.
You only get one version of your data history.
If there is even a possibility that UA financing will become part of your roadmap in the next six months, start treating your data as permanent infrastructure rather than temporary reporting.
When the time comes to raise capital, you want the conversation to center on your unit economics – not on whether they can be trusted.
And when the data can stand on its own, the underwriting conversation becomes what it should have been all along: a discussion about scale.
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