promergetREVENUE 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 — FULL EDITION — 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 complete scoring and prioritization framework from a live M&A pipeline for wealth and asset management software in Europe — the model, the multiple matrix, the counter-check, the market anchors with sources and the ranked results. Not a market report. A working model, published in full.
- 88Targets scored
- €797MCombined revenue
- 25Multiple bands
- 2Targets with EBITDA on file
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 — and the seller who has anchored on that number usually finds out eight weeks into a process, when it is too late to reposition.
Seven targets clear Tier 1. Forty-seven fall below any threshold worth acting on. The remaining thirty-four are where the actual work sits: good enough to matter, unclear enough that somebody has to pick up the phone.
A pipeline of 88 names is in truth a work list of 20.
02 — THE DATA SET
What a real pipeline is made of.
Before any valuation logic applies, the shape of the list decides how much of it is usable. Two distributions carry that answer.
BY PRODUCT CATEGORY
- Point solution37
- Core SaaS platform31
- Data / analytics14
- RegTech3
- Consulting3
BY PROCESS STAGE
- Stage 5 — acquirable now13
- Stage 44
- Stage 37
- Stage 28
- Stage 1 — identified only56
What the distribution gives away. 61 percent of the list sits at stage 1 — identified, never approached. 42 percent are point solutions: single features wrapped in a company, which as acquisitions rarely earn back their integration cost. And 38 companies have no revenue figure at all. Three filters, and a pipeline of 88 names collapses to a work list of around 20.
03 — 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.
Running both is the point. A single ranked list conflates two questions that behave differently over time: how good is this company, and how far along am I with it. Process proximity moves every week. Company quality barely moves at all.
| 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.
Where the two scores diverge, you have the diagnosis. A target with a high asset score and a low priority score is the best company on the list that nobody has called. In this data set that is T-14: asset score 4.25, the highest in the entire pipeline, sitting at process stage 2 and ranked fourteenth. The inverse case is just as instructive — a target that ranks high only because the conversation is warm is a relationship, not an acquisition.
04 — MARKET ANCHORS
The numbers you calculate against.
Every multiple in the next section sits below these anchors on purpose. Here is what they are and where they come from.
| Anchor | Value | Source |
|---|---|---|
| Public comps WealthTech | 4.6x EV / revenue | Flow Partners, 2026 |
| Median WealthTech M&A, last 12 months | 5.6x EV / revenue | Flow Partners, 2026 |
| Private best-in-class, €10–30M revenue | approx. 7.0x revenue | Windsor Drake, Q1 2026 |
| WealthTech & capital markets infrastructure | 5–10x revenue | Windsor Drake, 2026 |
| Private fintech, earnings view (upper end) | 9.7x – 17.5x EBITDA | FE International, 2026 |
| Reference transaction, strategic buyer (11/2024) | 8.3x revenue / 74.5x EBITDA | Reuters |
| Market context Q1 2026 | Multiples corrected, volume down | PwC Outlook 2026 |
These anchors describe growing, high-margin SaaS companies in competitive sale processes with several bidders. A roll-up pipeline is the opposite: sub-scale, project-heavy, owner-managed companies with no auction. Present those companies with the market medians and you are negotiating against yourself.
A multiple is not a property of the company. It is the result of how many people are bidding at the same time.
That is the whole reason the base band of this model sits at 1.5 to 4.0x revenue rather than at the 5.6x median. The gap between the two is not a discount. It is the price of a bilateral process — and it is the single number a seller has the most leverage to change, by creating competition before entering one.
05 — 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 |
Two rows deserve an explanation. Consulting and services get 0.5 to 1.5x revenue — not because those companies are bad, but because you are buying people who go home in the evening. Value a consultancy on a software multiple and you pay twice: once at closing, and again when the consultants leave. RegTech sits at the top of the matrix for the opposite reason: compliance is not a discretionary budget, so its revenue survives the customer's next cost-cutting round.
The drop in the > €40M column is not an error. Above that size the buyer universe thins out — fewer strategics can absorb the target, financial sponsors need leverage, and the process gets slower. Scale stops being a premium and starts being a constraint.
The quality factor.
The matrix alone is not enough. Two companies with €8M of revenue in the same category can be worth entirely different amounts. So the range gets multiplied by a factor.
| 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% |
This is the only part of the model that moves in a negotiation. The gap between 0.60 and 1.15 is roughly half the indicative value — and it is assigned before anyone has spoken to management. Everything unknown defaults to 0.80. A seller who cannot evidence a recurring revenue share is therefore not valued conservatively by accident. They are valued conservatively by rule.
06 — THE COUNTER-CHECK
The most expensive mistake in this pipeline.
One target from the data set: €25M revenue, core SaaS platform, independent, technically mature. 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 — and everything above that is a bet on the margin after integration, which the seller does not get paid for.
This is where most sell-side processes break, and they break quietly. The seller walks in anchored on the revenue number because an advisor produced it. The buyer walks in anchored on the earnings number because an investment committee produced it. Nobody is lying. The two numbers simply answer different questions, and only one of them survives a credit approval.
The rule. Below a 15 percent EBITDA margin, the earnings view binds. Above 25 percent, the revenue view. In between, take the midpoint and explain at the first meeting why — because a seller who explains it first controls the frame, and a seller who is shown it in week eight does not.
07 — THE RESULT
The top of the list.
Twenty of eighty-eight, in ranked order. Every company carries an identifier from T-01 to T-88, the order matches the ranking, and revenue appears only as a band. The remaining 68 rows are in the long version.
| ID | Category | Revenue band | Stage | Prio | Asset | Tier | Multiple | Factor | EV base |
|---|---|---|---|---|---|---|---|---|---|
| T-01 | Point solution | €2–5M | 5 | 4.55 | 4.35 | 1 | 1.5–3.0x | 1.15 | €5–15M |
| T-02 | Core SaaS platform | €5–15M | 5 | 4.35 | 4.10 | 1 | 3.0–5.0x | 0.80 | €15–30M |
| T-03 | Core SaaS platform | €5–15M | 5 | 4.35 | 4.10 | 1 | 3.0–5.0x | 0.60 | €15–30M |
| T-04 | Core SaaS platform | €5–15M | 5 | 4.35 | 4.10 | 1 | 3.0–5.0x | 0.80 | €15–30M |
| T-05 | Core SaaS platform | €2–5M | 5 | 4.35 | 4.10 | 1 | 2.0–3.5x | 0.80 | €5–15M |
| T-06 | Point solution | €2–5M | 5 | 4.35 | 4.15 | 1 | 1.5–3.0x | 1.15 | €5–15M |
| T-07 | Core SaaS platform | €15–40M | 5 | 4.20 | 4.00 | 1 | 3.5–5.5x | 0.60 | €60–120M |
| T-08 | Core SaaS platform | €5–15M | 4 | 3.95 | 3.95 | 2 | 3.0–5.0x | 0.60 | €15–30M |
| T-09 | Core SaaS platform | €2–5M | 5 | 3.95 | 3.60 | 2 | 2.0–3.5x | 1.15 | €5–15M |
| T-10 | Data / analytics | €2–5M | 5 | 3.95 | 3.60 | 2 | 2.0–4.0x | 1.15 | €5–15M |
| T-11 | Data / analytics | < €2M | 5 | 3.95 | 3.60 | 2 | 1.5–3.0x | 1.15 | < €5M |
| T-12 | Core SaaS platform | €5–15M | 4 | 3.90 | 3.85 | 2 | 3.0–5.0x | 0.60 | €15–30M |
| T-13 | Point solution | €5–15M | 4 | 3.85 | 3.75 | 2 | 2.0–3.5x | 0.80 | €5–15M |
| T-14 | Core SaaS platform | €15–40M | 2 | 3.65 | 4.25 | 2 | 3.5–5.5x | 0.60 | €60–120M |
| T-15 | Core SaaS platform | €5–15M | 3 | 3.65 | 3.85 | 2 | 3.0–5.0x | 0.80 | €15–30M |
| T-16 | Point solution | €2–5M | 4 | 3.65 | 3.50 | 2 | 1.5–3.0x | 0.80 | €5–15M |
| T-17 | Data / analytics | €2–5M | 2 | 3.60 | 4.10 | 2 | 2.0–4.0x | 1.15 | €5–15M |
| T-18 | Core SaaS platform | €15–40M | 3 | 3.50 | 3.70 | 3 | 3.5–5.5x | 0.80 | €30–60M |
| T-19 | Data / analytics | €5–15M | 3 | 3.45 | 3.60 | 3 | 3.0–5.0x | 0.80 | €30–60M |
| T-20 | Point solution | €2–5M | 3 | 3.40 | 3.50 | 3 | 1.5–3.0x | 0.80 | €5–15M |
Priority score includes process proximity, asset score does not. Stage 5 = acquirable near-term, stage 1 = identified only. Tier 1 rows in violet. Enterprise value, cash and debt free, before due diligence.
Read the first column and the fourth together. Not a single large company sits at the front. The biggest assets land in the middle of the table — not because they are worse, but because nobody is talking to them. Process proximity is 25 percent of the priority score, and it is the only criterion that rewards work already done rather than quality that was always there.
08 — APPLY IT
How to find your own position in four steps.
The categories here are industry-specific. The mechanics are not. This runs on any fragmented software market with owner-managed vendors between €1M and €50M of revenue — including, if you build wealth or asset management software in Europe, on you.
-
STEP 1
Pick your row.
- Core platform, data, RegTech, point solution or services
- Be honest about services revenue
- Then pick your revenue column
-
STEP 2
Assign your factor.
- Recurring share above 70% → 1.15
- Mixed or unproven → 0.80
- Project-heavy or legacy → 0.60
-
STEP 3
Run the counter-check.
- EBITDA × 12 as the second number
- Below 15% margin the lower one binds
- Above 25% the revenue view binds
-
STEP 4
Count the bidders.
- One bidder → this matrix applies
- Four bidders → the anchors apply
- That gap is worth more than the factor
If the number you get is materially below what you assumed, that is useful information and not an insult. It is also almost always fixable — but on a timeline of twelve to twenty-four months, not in a data room. The three levers, in order of impact: recurring revenue share, margin, and the number of parties at the table.
09 — FINDINGS
Seven conclusions from the data set.
Each one cost something to learn. None of them is specific to this pipeline.
01
The most dangerous buyer is not on the list.
While you maintain your own pipeline, a well-capitalized competitor buys the same companies. Five names in this data set had already been sold before the process reached them.
02
Pipelines decay faster than they are maintained.
Companies get acquired, merge or disappear. Over five percent decay per year, and nobody budgets for the re-research.
03
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 and it is almost never done.
04
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.
05
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.
06
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.
07
Tier 4 is not a waiting list.
47 of 88 targets carry no relevant score and still appear in every status meeting, because deleting them looks like giving up.
10 — WHAT THIS MODEL CANNOT DO
The four places it breaks.
A framework that is only ever described by what it gets right is a sales document. These are the limits, stated plainly, so you can judge where the output is load-bearing and where it is an opening position.
-
It is a screening tool, not a valuation.
Every range here is a pre-diligence indication for a bilateral process. Customer concentration, churn, contract terms, technical debt and key-person risk each move the number more than the entire matrix does — and none of them is visible from the outside.
-
The earnings side is almost unpopulated.
EBITDA was available for two of 88 targets. The counter-check is therefore a rule applied to an assumption for 86 of them. That is the honest state of any pipeline built from outside a company, and it is the first thing a research round fixes.
-
The quality factor is coarse on purpose.
Three values, not a continuum. It is designed to be moved after the first management call, which means every number carrying an 0.80 is a placeholder, not a judgement.
-
It prices a bilateral process only.
Introduce three credible bidders and the anchors at the top of this page apply instead of the matrix. The model measures what a company is worth to one buyer at a time. Competition is the variable it deliberately holds at one.
DOWNLOADS
Everything on this page, and the other 68 rows.
The long version — 17 pages.
The complete results table across all 88 targets: identifier, category, revenue band, process stage, priority score, asset score, tier, the applied multiple, the quality factor and the resulting EV range for every single one. Plus the full structure of the data set, the seven market anchors with sources, the scoring weights and the EBITDA counter-check worked through end to end.
- All 88 rows, ranked
- Both scores per target
- 25 multiple bands across 5 categories
- 7 market anchors with sources
- Seven findings in full
- English and German
NO FORM · NO EMAIL ADDRESS · NO NAMES, NO CONTACTS, NO EXACT REVENUE PER TARGET
The short version — 8 pages.
The model, the matrix, the counter-check and six findings, without the results table. For circulating internally when the full table is more than the audience needs.
IF READING IT ISN'T THE END OF IT
Two ways this becomes useful.
The model runs on a single company and on a whole pipeline. Which of the two you need depends on which side of the table you are sitting on.
-
IF YOU BUILD THE SOFTWARE
Where do you sit?
An indicative range for your own company under exactly this model — category, revenue band, quality factor, matrix range, and the counter-check that decides which of the two views binds.
- Indicative EV range, both views
- The factor you would be assigned by default — and what moves it
- The three things a buyer checks before the first meeting
- No mandate, no retainer, no obligation
-
BUY SIDE
IF YOU ARE THE ONE BUYING
Your pipeline
Scoring your own target list, ranking it and forcing it into a buying order is days of work, not months — provided someone has already held the framework against real transactions.
- Your targets scored on both scores
- Multiple matrix calibrated to your category
- An explicit cut list, not a waiting list
- Ten acquisitions in three years behind the model
ABOUT THE MODEL
It does not come from a textbook.
Ten acquisitions in three years. Seven brands, ten locations, five countries, around 300 people integrated onto one platform and then sold. Before that, more than a hundred conversations with competitors — which became the basis for training the agents. Today: AI-native M&A advisory for the DACH mid-market, €10M to €250M enterprise value, a maximum of five mandates a year.
Write to me directly →Data as of 20 August 2026, next update Q1 2027. Enterprise value, cash and debt free, before due diligence. Valuation ranges are preliminary indications for bilateral processes without an auction and do not replace a formal valuation opinion. This document is not investment, legal or tax advice. All company names have been replaced by identifiers; no target can be identified from this material. Market anchors are cited to their published sources and were not independently verified.