How we rank the best SaaS deals on TrustMRR
TrustMRR is the cleanest source of verified-revenue SaaS for sale, but their own ranking is mostly chronological or by single-axis filters. Buyers waste hours scrolling through listings that look great by one metric and rotten by another.
Our composite Deal Score fixes that. Each listing gets a 0-100 score based on four signals that actually predict whether an acquisition makes sense for an operator buyer. Updated daily. Methodology open for scrutiny.
Payback period
Months to recoup the acquisition cost at the current profit margin. 6-month payback gets a perfect 100; 60+ months scores 0. This penalises overpriced asks even when MRR looks good.
Category multiple gap
How does the asking price compare to the median multiple in the same category? An AI tool trading at 3x ARR is normal; a fintech at the same multiple is a bargain. We compute the median per category from every listed deal on TrustMRR.
Growth signal
30-day MRR growth percentage. +20% or more is a max-score signal. Negative growth caps the contribution. Stagnant SaaS can still be good deals if the price reflects it, but momentum tilts the score.
Quality signal
Revenue-per-visitor (efficiency proxy) and customer count (stability proxy). A SaaS with 1,000 customers paying $5 each is fundamentally more stable than one with 5 customers paying $1,000 each, all else equal.
The math, briefly
payback_months = asking_price / (mrr × profit_margin / 100)
multiple = asking_price / (mrr × 12)
category_gap = (median_category_multiple - multiple) / median_category_multiple
public_score = 0.40 × payback_score(payback_months)
+ 0.25 × category_gap_score(category_gap)
+ 0.20 × growth_score(growth_30d)
+ 0.15 × quality_score(rev_per_visitor, customers)
deal_score = public_score + proprietary_modifier // clamped 0..100Each sub-score is clamped to 0-100. A listing must have at least an asking price and revenue figure to be scored. Categories with fewer than five listed deals don't produce a median, so we use a neutral fallback for the category gap on those. The proprietary modifier is described below.
The 5th signal: our proprietary modifier
On top of the 4 transparent pillars, we apply a proprietary adjustment of up to ±15 points based on signals we don't fully disclose. This is deliberate: if every input were public, sellers could engineer their listings to game the ranking.
What we will say is that the modifier blends:
- ·Category momentum: is the niche heating up or cooling down? We blend Google Trends signal, investor sentiment, and our own market intelligence per category. A SaaS in a heating category gets a tailwind; a cooling one gets a headwind.
- ·Other internal inputs: we keep this part deliberately vague. It includes signals around buyer demand, post-acquisition operability, and listing freshness.
The modifier is deterministic — the same inputs always produce the same output — and runs on every listing equally. We update its calibration internally; the 4 public pillars stay stable.
What we explicitly don't do
- ·We don't pay-to-rank. No listing can buy a higher position. Affiliate commission is paid only by TrustMRR when an acquisition closes, never by the seller.
- ·We don't hand-curate. The score is deterministic and runs every day at 05:15 UTC. Same input, same output.
- ·We don't hide deals. Every TrustMRR listing flows through. Lower scores mean lower visibility, never censorship.
Limitations we're honest about
Profit margin and growth are self-reported (although TrustMRR verifies revenue via Stripe). Recently listed SaaS have no growth history yet. Categories with few peer listings have weaker baselines. Treat the Deal Score as a strong shortlist signal, not a substitute for due diligence.
Found a flaw or have a better weighting idea? Email us at methodology@mrrdeals.com.