REVENUE VIEW
€67M
Base value from the matrix. The number that comes out when you value a software company on its top line and stop there.
BENCHMARK — SHORT VERSION — AUGUST 2026
How to force 88 WealthTech targets into a buying order.
Anyone running a buy-and-build thesis rarely has too few targets. They have too many, and no order among them. What follows is the fully anonymized scoring and prioritization framework from a live M&A pipeline for wealth and asset management software in Europe. Not a market report — a working model.
- 88Targets scored
- 38No revenue figure
- 47Tier 4 — cut list
- 7Tier 1
01 — THE FINDING
Four numbers that explain the rest.
The biggest lever is not in the valuation. It is in the data. 38 of 88 targets carry no revenue figure at all, which makes them neither rankable nor negotiable.
The most expensive misunderstanding sits in the multiples. At a single-digit EBITDA margin, the revenue approach produces numbers three times above what any buyer will ever pay.
A pipeline of 88 names is in truth a work list of 20.
02 — THE MODEL
Five criteria, two scores.
The priority score, which includes process proximity, decides who you call this week. The asset score, which excludes it, decides how good the company actually is. Where the two diverge, you have found the best target on the list that nobody has called yet.
| Criterion | Weight | What is measured |
|---|---|---|
| Strategic fit | 25% | Hits the core market, product edge complements the existing stack |
| Bite size | 20% | Large enough to matter, small enough to digest |
| Process proximity | 25% | Identified (1) through ready for near-term acquisition (5) |
| Availability | 20% | Is the owner willing to sell at all |
| Data quality | 10% | Are revenue and earnings on file, or is it guesswork |
Tier 1 from 4.0 · Tier 2 from 3.6 · Tier 3 from 2.8 · below that, Tier 4. Tier 4 is not a waiting list. It is the cut list.
03 — VALUATION
The multiple matrix.
| Category | < €2M | €2–5M | €5–15M | €15–40M | > €40M |
|---|---|---|---|---|---|
| Core SaaS platform | 1.2–2.5x | 2.0–3.5x | 3.0–5.0x | 3.5–5.5x | 3.0–5.0x |
| Data / analytics | 1.5–3.0x | 2.0–4.0x | 3.0–5.0x | 3.5–5.5x | 3.0–5.0x |
| RegTech | 1.5–3.0x | 2.5–4.5x | 3.5–6.0x | 4.0–6.5x | 3.5–6.0x |
| Point solution | 1.0–2.0x | 1.5–3.0x | 2.0–3.5x | 2.5–4.0x | 2.0–3.5x |
| Consulting / services | 0.5–1.0x | 0.6–1.1x | 0.7–1.2x | 0.8–1.4x | 0.8–1.5x |
Market anchors 2026: public comps around 4.6x revenue, median WealthTech transaction 5.6x, best-in-class private around 7x. This matrix sits deliberately below that. The anchors describe growing, high-margin companies in auctions. A roll-up pipeline is the opposite: sub-scale, project-heavy, owner-managed, one single bidder.
| Factor | When it applies | Effect |
|---|---|---|
| 1.15 | High recurring revenue share, cloud-native, little project business | +15% |
| 0.80 | Mixed model or unclear data — the default for everything unknown | −20% |
| 0.60 | Project- and service-heavy, legacy stack, low growth | −40% |
04 — THE COUNTER-CHECK
The most expensive mistake in this pipeline.
One target from the data set: €25M revenue, core SaaS platform, independent. Matrix and factor give a range of €53M to €83M. The same company reports €2M EBITDA — an 8 percent margin.
EARNINGS VIEW
€24M
At 12x EBITDA. The number a buyer's investment committee arrives at independently, before the first meeting.
Between the two numbers sits a factor of three. And the lower one binds. No financial investor and no strategic buyer pays €67 million for €2 million of earnings, however elegant the revenue derivation. The realistic range is €25M to €45M.
The rule. Below a 15 percent EBITDA margin, the earnings view binds. Above 25 percent, the revenue view. In between, the average — and explain why at the first meeting.
05 — FINDINGS
Six conclusions from the data set.
Each one cost something to learn. None of them is specific to this pipeline.
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01
A pipeline without numbers is a list of names.
38 of 88 targets with no revenue figure. A structured research round is the cheapest lever in the entire process.
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02
Pipelines decay faster than they are maintained.
Companies that get acquired come off the list. Over five percent decay per year.
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03
The best assets are rarely at the front.
The highest asset score in the pipeline belongs to a company at process stage 2. Process proximity measures progress, not quality.
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04
Revenue multiples lie at thin margins.
A factor of three between the revenue and the earnings view. EBITDA figures were available for exactly two of 88 targets.
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05
Point solutions are rarely acquisitions.
42 percent of the list are single features wrapped in a company. The purchase price is low. The integration cost is not.
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06
The most dangerous buyer is not on the list.
While you maintain your own pipeline, a well-capitalized competitor buys the same companies.
LONG VERSION
14 pages, with the full results table across all 88 targets.
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WHAT IS IN IT
Every target, every number.
Per target: identifier, category, revenue band, process stage, both scores, the applied multiple, the factor and the EV range. Plus the structure of the data set by region, category and maturity, and the seven market anchors with sources.
All names replaced by identifiers. From a live pipeline, not a market study.
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HOW TO GET IT
One email. No form.
Send it and the PDF comes back from me directly. Your address is used to send the document and, if you want it, one piece a week after that.
IF READING IT ISN'T THE END OF IT
This model runs on any pipeline, not just this one.
Scoring 88 targets, ranking them and forcing them into a buying order is days of work, not months — provided someone has already held the framework against real transactions. Ten acquisitions in three years. Seven brands, ten locations, five countries, around 300 people integrated onto one platform and then sold.
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