Before you hand over your hard‑earned capital, make sure the numbers in Stripe aren’t just a clever marketing trick. This guide walks you through a data‑driven audit, turns raw invoices into actionable insights, and shows you exactly how to turn that insight into leverage.
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In a SaaS acquisition, revenue is the lifeblood of the business. Stripe is the most common payment processor for subscription services, and its reports are the raw material that fuels valuation models. If you’re new to Stripe verification, check out our free guide at Deal Alert AI. A misread or a missing transaction can skew your entire deal, costing you thousands of dollars.
Unlike traditional accounting, Stripe offers granular transaction data, real‑time reporting, and the ability to drill down by customer segment, plan tier, and billing cycle. That level of detail means you can spot seasonality, churn spikes, and even fraudulent activity before you sign the contract.
When you’re evaluating a SaaS deal, you’ll typically see numbers like Monthly Recurring Revenue (MRR), Annual Recurring Revenue (ARR), and churn rate. Those figures are usually calculated from Stripe exports. If the source is unreliable, every other metric you calculate on top of it will be flawed. Therefore, the first step in any SaaS purchase is a meticulous audit of the Stripe data.
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Stripe doesn’t just give you a flat list of payments; it provides a wealth of fields you can use to validate revenue:
From this raw data, you can calculate the classic SaaS metrics: MRR, ARR, average revenue per user (ARPU), and the net dollar retention (NDR). A trustworthy Stripe export should also include refunds and chargebacks, so you can subtract them and arrive at the net figure.
When the exporter is missing any of these fields, you need to investigate. A missing plan ID could mean the business is using a custom pricing model that isn’t captured; a missing refund column could hide a hidden churn rate.
Start by requesting a full data export from the seller. Ask for a CSV that includes all payments, refunds, and subscription events for the past 12–18 months. The timeframe should cover at least one full fiscal year to capture seasonality.
Once you have the file, load it into a spreadsheet or a database. Use a naming convention that reflects the source and date: Stripe_2025_Q1_export.csv. This keeps versions tidy and prevents you from re‑importing the wrong file.
At this point, you can run a quick sanity check: count the number of unique customers, the total number of transactions, and the sum of the amounts. If the numbers look off by an order of magnitude, the seller might have filtered the data.
Stripe occasionally creates duplicate records if a payment retries or if a webhook fires twice. Identify duplicates by matching Payment ID and timestamp. Drop any that are exact repeats.
Outliers are more subtle. Look for one‑off large amounts that don’t match any recurring plan. Those could be gift card sales or manual invoices that you’re not supposed to include in MRR calculations.
After cleaning, add a calculated column called Net Amount (Amount – Refund – Chargeback). This column will be the foundation for all further analysis.
Now that you have a clean dataset, build a dashboard that pulls out the core SaaS metrics. Here’s a quick formula approach you can replicate in Excel, Google Sheets, or a BI tool:
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