At the smallest end of the market, elaborate diligence costs more than the asset. The useful discipline is proportionality: identify the two or three things that could make this a bad purchase, check those properly, and consciously skip the rest rather than performing a scaled-down version of a large-deal process.
TL;DR: At very small scale the question shifts from retention analysis to whether there is enough history to analyse at all. Here is how to run proportionate diligence and what to skip.
Micro-startup marketplace, very small deals, often pre-revenue or early-revenue. That shape determines what a buyer can expect to be given and what has to be requested, which is most of what changes between one acquisition channel and another.
Listings are typically brief, with revenue if any, a product description, and the builder's account of traction. Many listings at this end are early-revenue or pre-revenue, which means there is often genuinely no retention history to examine.
Usually, sufficient history. A product with six months of revenue and twenty customers cannot produce a meaningful churn rate, and pretending otherwise wastes effort. What is also typically missing is clarity about what is actually being transferred: code, customers, domain, accounts, or some subset.
Twenty customers over six months does not support a churn rate. Say so in your own notes rather than computing one. The honest position is that retention is unknown, and the purchase decision has to rest on something else — the code, the domain, the customer list as a list.
At this scale the most common problem is not a bad number, it is ambiguity about what conveys: the domain, the code, the customer relationships, the billing account, the analytics history, third-party accounts and API keys. Get an explicit written asset list, because it is cheap to get and expensive to discover afterwards.
Very early revenue is frequently friends, colleagues or the builder's own audience buying to be supportive. Those accounts behave nothing like arm's-length customers. Ask directly, and ask how many customers the builder had never met before purchase.
Small products often depend on API keys, app-store accounts, OAuth apps and platform listings that may not be transferable at all. This is a practical blocker rather than a valuation question, and it is worth checking before the money moves rather than after.
In order, and stopping early if any step produces a blocker:
Getting a usable export is its own problem, and the request wording that works differs by billing platform. The export guides cover eighteen platforms with the exact wording to send and the status values that mislead on each. Once you have the file, the seller-claims pages give the arithmetic for each specific claim, and the 23-point checklist is the short version of the whole process.
Tiny Acquisitions: https://tinyacquisitions.com/
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Every check on this page can be run by hand in a spreadsheet, and if you have the time you should. If you would rather not: send us the target's subscription export and we run the full human-reviewed analysis — logo churn, revenue churn, customer concentration, annual-plan decay, zombie MRR and an A–F revenue-quality grade. The free Starter tier covers one CSV per month, which is enough to check a single deal.
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Enough to answer the two or three questions that could make it a bad purchase at that price, and no more. Usually that means verifying the revenue exists, confirming what actually transfers, and establishing whether the customers are arm's-length. Retention analysis often cannot be done at all, and saying so is better than estimating it.
Asset boundaries. Which of the domain, code, billing account, customer records, third-party accounts, API keys and marketplace listings actually convey, and whether each can be transferred at all under the provider's terms. It costs almost nothing to establish in writing beforehand and is expensive to discover after.
Not meaningfully, especially with a small customer count. The right output is that retention is unknown, which is a legitimate finding and better than a number with no support behind it. The purchase decision then has to rest on something you can actually assess.