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Due diligence on a Tiny Acquisitions listing: proportionate diligence at very small scale

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.

What kind of channel this is

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.

What you typically get

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.

What is typically not there

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.

The churn traps specific to this channel

1. Analysing a rate when there is no sample

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.

2. Unclear asset boundaries

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.

3. Revenue that is a handful of friendly accounts

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.

4. Transfer mechanics on third-party dependencies

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.

A first-pass sequence

In order, and stopping early if any step produces a blocker:

  1. Decide up front what could make this a bad purchase at this price. Usually it is not the churn rate; it is whether the asset transfers cleanly and whether the revenue is arm's-length.
  2. Get an explicit written list of everything that conveys: domain, code repository, billing account, customer records, third-party accounts, API keys, app-store or marketplace listings.
  3. Check every third-party dependency for transferability before agreeing terms. Some genuinely cannot move.
  4. Ask how many customers are arm's-length, and how many the builder knew before they bought.
  5. If there is enough history to look at, use absolute numbers over twelve months. If there is not, record retention as unknown rather than estimating it.
  6. Verify the revenue exists at all by seeing the billing account, ideally live rather than as a screenshot.
  7. Skip the analyses that cannot pay for themselves at this deal size, deliberately and in writing, so you know what you chose not to check.

What to request

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/

ChurnLens is not affiliated with, endorsed by or a partner of any marketplace or broker named on this page. Listing formats, disclosure practices and terms change; treat the descriptions here as a starting point and verify current specifics with the marketplace itself. Nothing here is investment, legal or tax advice.

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Verify it against the raw rows

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.

See a sample report →  ·  Get the free 23-point checklist →

Frequently asked questions

How much due diligence is proportionate for a very small SaaS purchase?

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.

What is most often overlooked when buying a very small SaaS?

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.

Can you calculate churn on six months of data?

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.

9%
Median B2B SaaS revenue churn
88%
Median gross revenue retention
23
Audit Checklist Points

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