M&A Scoring Engine
Thirteen independent signal sources. Seven scoring dimensions. A single 0–100 acquisition readiness score updated daily. This is the methodology that turns noisy market data into a ranked queue of proprietary deal flow.
Built for PE investment partners screening pipeline at scale, corporate development teams mapping strategic adjacencies, and M&A advisors who need a defensible scoring methodology behind every shortlist recommendation.
No credit card required. First 25 company profiles free.
Overview
Most acquisition target screening is binary — in play or not. The M&A Scoring Engine replaces that with a continuous score that updates as new signals arrive. Three layers work together to produce every score.
Thirteen independent sources — job boards, funding databases, web analytics, social signals, review platforms, and regulatory feeds — run continuously. Each source is scored independently on a severity scale. The engine cross-correlates signals so a single false positive (one removed job posting) doesn't move the needle, but a pattern (removed postings + traffic decline + executive departure) triggers an escalation.
Raw distress signals feed into a structured scoring model that evaluates every company on seven dimensions: distress level (30 pts), market fit (20 pts), tech stack age (15 pts), team stability (15 pts), ARR band (10 pts), and growth trajectory (10 pts). Each dimension is weighted so the final score reflects both urgency and opportunity.
Every score is calibrated against sector-specific baselines. A traffic decline that is severe for a vertical SaaS company may be normal seasonality for an e-commerce platform. The engine maintains peer-group benchmarks so scores are relative to industry context, not absolute thresholds.
Layer 1
Every acquisition target was a signal before it was a deal. The engine monitors 13 data streams and scores each signal on a four-level severity scale. The key insight: no single signal is decisive. The scoring power comes from stacking.
| Severity | Example Signals | Points | Detection Method |
|---|---|---|---|
| CRITICAL | Debt covenant breach, missed payroll, SEC 8-K material event, tax arrears | 28 | SEC EDGAR (EN), e-disclosure.ru (RU), Kontur (RU) |
| HIGH | Layoffs >10% headcount, CEO departure, failed fundraise, down round | 20 | LinkedIn Apify delta, Crunchbase, Google News RSS + LLM classify |
| MEDIUM | CTO/CPO departure no backfill, major product pivot, Glassdoor drop >0.5 | 12 | LinkedIn exec tracking, G2/Capterra scrape, website change detection |
| LOW | Marketing budget cut, conference presence reduced, GitHub commit drop >50% | 5 | Blog RSS cadence, GitHub public API, social post trend analysis |
A company with two or more HIGH signals in 90 days scores equivalent to CRITICAL — regardless of the individual severity scores. This prevents a borderline company from being under-scored when multiple distress indicators arrive simultaneously. The stacking logic caps the distress dimension at the maximum 30 points (not 28 + 20) so no single dimension can dominate the total score, but the stacking ensures that multi-signal detection is properly weighted.
Layer 2
The distress signals feed into a structured 100-point model that evaluates every company across seven dimensions. Each dimension has a maximum point allocation, a specific data source, and transparent scoring logic — so a score of 78 means something specific, not a black-box number.
Max points from distress signal severity. 0 signals = 0 pts; 1 LOW = 5; 1 MEDIUM = 12; 1 HIGH = 20; 1 CRITICAL = 28; 2+ HIGH in 90 days = 30 (stacking cap). This dimension measures urgency — how much pressure is on the company to transact.
Based on company product category. Pure B2B SaaS = 20 pts; API/infrastructure = 16; B2B2C = 14; Marketplace = 10; Services/agency = 4; Consumer = 0. Scores reflect acquirer demand: B2B SaaS companies have the widest buyer pool and highest transaction velocity.
LLM-classified from company tech stack signals. Modern (React/Vue + Node/Python + cloud DB) = 15 pts; Mixed = 9; Legacy (jQuery, PHP monolith, on-prem) = 3. Modern stacks reduce post-acquisition integration cost and timeline.
No executive changes in 12 months = 15 pts; 1 departure = 9; 2+ departures = 3; CEO departed with no replacement = 0. Stable management increases the probability of a clean transaction and reduces post-close attrition risk.
$3M–$10M (sweet spot for most acquirers) = 10 pts; $1M–$3M or $10M–$20M = 7; $500K–$1M = 4; outside these bands = 1. The sweet spot reflects ARR ranges where the company is validated but hasn't scaled to institutional pricing.
Declining (employee count -10%+ with worsening review sentiment) = 10 pts (highest distress = highest acquisition opportunity); Flat = 5; Growing = 2. Growth trajectory is scored inversely — declining companies need a buyer more urgently.
Daily Telegram alert. Immediate deal team review.
Weekly shortlist. Assign outreach owner.
Re-score in 30 days. Watch for new signals.
On file. No active outreach.
Layer 3
A raw score means nothing without sector context. A 30% traffic drop is critical for a vertical B2B SaaS company with 2,000 customers. It is a slow Tuesday for an e-commerce platform in Q4. The sector matching layer normalises every signal against peer-group benchmarks so scores are comparable across industries.
Benchmarked against industry cohorts by ARR band. Traffic decline sustained for 2+ quarters scores higher. Executive departures weighted 2× in scoring because management depth is thinner.
Benchmarked against platform peers. Feature-level signals (product pivot, pricing changes) carry more weight. Revenue model shifts (usage-based to seat-based) flagged as strategic distress indicators.
Liquidity metrics (buyer-to-supplier ratio, take rate trend) are primary signals. GMV decline and supplier churn above peer median trigger MEDIUM severity automatically.
API usage signals (tier downgrade, developer activity) supplement financial signals. Integration dependency analysis surfaces M&A interest vectors from platform acquirers.
Revenue concentration and client retention are primary distress indicators. Employee headcount changes signal account loss patterns. These companies score lower on market fit (4/20) but high on distress when signals fire.
Traffic and engagement metrics dominate. Review sentiment and social signals carry higher weight. Billboard/awareness metrics supplement financial data. Appropriate market-fit caps prevent over-scoring.
The scoring engine maintains a peer-group baseline per sector. When a new signal arrives, it is compared against that baseline before the severity level is assigned. This means:
Same raw signal, different sectors, different scores. A B2B SaaS company that loses its CTO without backfill scores higher on distress than a marketplace that loses the same role — because SaaS platforms are more dependent on technical continuity. The sector context is not a modifier applied after scoring. It is embedded in the scoring logic itself.
When you define a target universe in the app, the engine only scores companies within your selected sectors against their relevant benchmarks. A PE firm screening B2B SaaS targets never sees marketplace sector calibration. The scoring is segment-specific because the risk profile is segment-specific.
End-to-End Flow
The engine runs continuously — every new data point adjusts every relevant score. Here is how a single company moves from "tracked but unremarkable" to "priority acquisition target."
The engine pulls from job boards (removed listings, hiring freeze signals), Crunchbase (funding gaps, down rounds), SimilarWeb (traffic trends), LinkedIn (executive changes, headcount deltas), Glassdoor/G2/Capterra (review velocity and sentiment), news feeds (press tone, regulatory filings), SEC EDGAR (EN), e-disclosure.ru (RU), and GitHub (commit frequency). Each source runs on its own cadence — some hourly, some daily, some weekly.
Output per company: a raw signal array with timestamps, severity levels, and source attribution.
Raw signals are classified by severity (CRITICAL/HIGH/MEDIUM/LOW) per the matrix. If two or more HIGH signals arrive within 90 days, the stacking rule escalates to the maximum distress score. The engine also tracks signal decay — a MEDIUM signal that has persisted for 6 months without resolution is reclassified higher because the distress is chronic, not acute.
Output per company: a severity-classified distress signal vector with stacking modifiers applied.
The classified signals feed into the seven-dimension model. Each dimension is scored against sector-specific baselines. A B2B SaaS company with declining employee count and worsening reviews scores high on growth trajectory (distress = opportunity). An infrastructure company with the same signal scores differently because the relationship between headcount and business health is different in that sector.
Output per company: a 100-point score with per-dimension breakdown and percentile rank against sector peers.
Based on the final score, the company is assigned to one of four tiers. Priority targets trigger an immediate Telegram alert to the deal team. Strong targets enter the weekly shortlist queue. Monitor targets are flagged for re-evaluation in 30 days — or sooner if a new signal arrives. Every tier transition (e.g., Monitor to Priority) triggers an alert so no scoring change goes unnoticed.
Output for the deal team: a daily ranked queue with explainable scores, evidence links, and recommended engagement timing.
The M&A Scoring Engine replaces noisy data streams with a ranked, explainable queue of acquisition targets — updated daily, sector-calibrated, and ready for deal team review.
No credit card required. First 25 company profiles free.