TrustMRR
Reading MRR data correctly
TrustMRR data is easy to misread in two ways: getting the units wrong, and misreading a skewed distribution. This page covers both so your numbers come out right.
Last updated: July 9, 2026
Quick answer
TrustMRR revenue is in US dollars, not cents - never divide by 100. Also read medians alongside averages, because the distribution is heavily skewed by a small number of high earners.
- revenue_mrr, asking_price, revenue_last_30d, and revenue_total are all in dollars.
- Never divide by 100 - there are no cents to convert.
- Median (~$7 MRR) tells a different story than the average ($4,682).
- Software margins run 60-80%, so modest MRR can still be a real business.
On this page
Dollars, not cents
Never divide by 100
TrustMRR revenue is stored in US dollars, not cents. The fields revenue_mrr, asking_price, revenue_last_30d, and revenue_total are all USD. Dividing by 100 will make every number 100x too small.
This trips people up because many payment systems store amounts in cents. TrustMRR does not. If a startup shows revenue_mrr of 4682, that is $4,682 per month, not $46.82.
// WRONG - TrustMRR values are already dollarsconst mrr = row.revenue_mrr / 100; // 100x too small// RIGHT - use the value as-isconst mrr = row.revenue_mrr; // dollars
Where the units bite hardest
The dollars-not-cents rule matters most the moment you leave the interface and touch the raw fields yourself - an export, a spreadsheet, or a direct query. Inside TrustMRR the values are already formatted for you; the risk appears when you reformat them and reflexively apply a cents-to-dollars conversion that does not belong here.
- Exports - treat every revenue column as dollars the moment it lands in your spreadsheet.
- Manual multiples - divide asking_price by an annualized profit in dollars, with no /100 anywhere.
- Charts - plot the raw values; a stray divide-by-100 silently shrinks the whole axis 100x.
- Comparisons - when you line a price up against MRR, confirm both are in the same dollar units first.
Read the distribution, not just the average
The average MRR is $4,682, but the median tracked SaaS earns roughly $7 MRR across the 3,478 revenue-positive startups. Most products make almost nothing because they skip validation. If you quote the average as though it were typical, you will badly overstate what a normal company earns.
Margins soften the small numbers
Software businesses average 60-80% profit margins (Developer Tools ~76.8%, mobile micro-SaaS ~79.5%, Education ~72.9%). A modest MRR on a high-margin product can still be a healthy, sellable business.
A checklist before you trust a number
- 1
Confirm the units
The value is in US dollars. There is no cents conversion, so no divide-by-100.
- 2
Ask which statistic it is
Is it an average or a median? The $4,682 average and the ~$7 median describe different companies.
- 3
Bracket it with percentiles
Use p25 and p75 to see the realistic middle band rather than judging by a single point.
- 4
Factor in margin
Read the revenue against 60-80% software margins before deciding whether a number is small or healthy.
When in doubt, trace the row
Every figure ties back to a verified startup. If a number looks off, open the underlying rows - the <a href="/docs/ai-research-chat">AI Research Chat</a> cites them directly - and confirm the units and the statistic before you act on it.
Frequently asked questions
Is TrustMRR revenue in dollars or cents?
Dollars. revenue_mrr, asking_price, revenue_last_30d, and revenue_total are all stored in US dollars. Never divide by 100 - there are no cents to convert.
Why is the median MRR only about $7?
Because most products skip validation and make almost nothing. Across 3,478 revenue-positive startups the median is about $7 MRR, while a small number of high earners lift the average to $4,682.
When am I most likely to misread the data?
When you leave the interface and handle raw fields in an export, spreadsheet, or query, and reflexively divide by 100 as if the values were cents. They are already dollars, so that conversion makes every number 100x too small. Confirm the units, then confirm whether you are looking at an average or a median.
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