Micro-SaaS diligence has a distinct statistical problem: with fifty or a hundred customers, a churn rate computed over one month is dominated by noise. Two cancellations instead of one doubles the rate. The methods that work at scale mislead here, and the analysis has to change accordingly.
TL;DR: At micro scale, small absolute numbers make every rate statistically noisy and founder dependency is usually the whole risk. Here is how to run diligence when the sample size is tiny.
Micro-SaaS marketplace, micro deals, often single-founder products. 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 typically carry revenue, customer count, a product description and the founder's account of how the business runs. At this scale the founder usually knows every customer, so qualitative information about the customer base is often unusually good and worth asking for at length.
Statistical reliability, mostly. With small customer counts there is no stable monthly churn rate to report, and cohort analysis has cohorts of five. Renewal timing, concentration and founder dependency all matter more than at scale and are rarely quantified. The recurring-versus-one-time split is frequently unclear because micro products often sell lifetime deals.
With a hundred customers, monthly churn moves by a full percentage point on a single cancellation. Do not compute monthly rates. Use twelve-month windows, absolute counts alongside percentages, and survival analysis on the customer base as a whole rather than cohort-by-cohort.
With fifty customers, the largest is likely to be several percent of revenue by arithmetic alone. The right question is not whether concentration exists but whether the top few accounts are contractually secured and whether you could survive losing the largest one in month two.
Micro-SaaS is where lifetime deals are most common, and they are one-time revenue with a permanent support obligation. A business with a large lifetime cohort has both less recurring revenue and more ongoing cost than its headline suggests.
At this scale founder dependency is usually the dominant risk rather than one risk among several. Acquisition is often entirely the founder's audience or a single directory placement, and support is entirely the founder. Assume it unless the data shows otherwise.
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.
Microns: https://microns.io/
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Not with monthly rates, which move a full point on a single cancellation. Use twelve-month windows, report absolute counts alongside percentages, and look at survival across the whole customer base rather than cohort-by-cohort. At this size you can also simply review every account individually, which beats any rate.
They are one-time revenue with a permanent support and hosting obligation, so they should sit outside MRR and outside the subscription multiple, with their ongoing cost treated as a going-forward expense. A large lifetime cohort means less recurring revenue and more cost than the headline figures suggest.
Founder dependency, in most cases. At this scale acquisition is often entirely the founder's audience or one placement, and support is entirely the founder. The retention arithmetic still matters, but the question of what actually transfers usually determines the outcome.