Methodology
ExitRadar identifies UK businesses approaching an ownership transition. This page explains the scoring architecture, financial estimation methods, and data sources behind the intelligence — and where the boundaries are.
ExitRadar identifies UK businesses approaching an ownership transition. This page explains the scoring architecture, financial estimation methods, and data sources behind the intelligence — and where the boundaries are.
Every scored company receives an Exit Score between 0 and 100. The score is a weighted composite of two independent pillars: Exit Timing (45%) and Business Quality (55%).
Exit Timing measures the likelihood that the current owners are approaching a transition — retirement, disengagement, succession gaps. It answers: when might they sell? Business Quality measures whether the company is financially sound, operationally mature, and worth approaching in the first place. It answers: is this a business an acquirer would actually want?
The two pillars are scored independently, then combined. A company can score highly on timing but poorly on quality. The composite reflects both dimensions.
We enforce a hard quality floor: any company scoring below 35 on Business Quality is suppressed from search results regardless of its timing score. A retiring owner does not make a weak business worth acquiring.
The 45% timing pillar detects behavioural and structural signals that precede ownership transitions. Patterns observed in real UK business sales, not arbitrary thresholds.
Average board age is one of the strongest predictors of exit. A board averaging 65+ with no younger directors is materially more likely to be in an exit window than one averaging 45. We score on age brackets, not exact ages — Companies House records birth month and year only.
A single director with no recent appointments represents concentrated key-person risk. Combined with a late-career age bracket, the company has no internal succession path — the most common profile among businesses that sell reactively.
A pattern of director resignations without replacement, or rapid recent turnover, can signal an approaching transition. We track the rate of board changes relative to company age.
Late filings and overdue confirmation statements are behavioural signals. An owner punctual for twenty years who suddenly files late may be mentally checked out. We analyse multi-year filing behaviour.
When fixed asset additions slow or stop, it can indicate an owner who has stopped investing in the business’s future. A three-year decline in capex, combined with other timing signals, suggests management for cash extraction rather than growth.
When the longest-serving director has 25+ years in post with no co-directors of similar tenure, the business identity is concentrated in one person. Not inherently negative, but increases the probability that the next major event is a transition.
A business that has paid regular dividends and then stops may be conserving cash ahead of a sale, reinvesting ahead of a valuation event, or experiencing distress. We use this signal in combination with profitability data to distinguish between these scenarios.
The 55% quality pillar combines two independently scored components: Financial Health and Operational Performance.
Financial Health assesses the balance sheet: liquidity ratios, cash position, debt-to-equity levels, net debt trends, asset encumbrance from charges, and filing discipline. We score the trajectory, not just the snapshot.
Operational Performance assesses how effectively the business converts assets into profit: net profit and EBITDA margins, growth rates, return on assets, profit per employee benchmarked against sector peers, cash conversion quality, earnings stability over multi-year periods, and business maturity.
Both components use multi-year financial data where available. We weight recent performance more heavily but penalise volatility — a business swinging between strong and weak years is less attractive than one with steady, moderate performance.
Most UK private companies do not report turnover. We reverse-engineer revenue from ten independent balance sheet signals, cross-checked by a machine learning model trained on 157,000 company-years with known revenue.
A gradient-boosted decision tree (XGBoost) takes these ten estimates as input. When methods converge, confidence is higher. When they diverge, the model has learned which signals to trust for that sector and filing type.
Every estimate includes a confidence percentage — a calibrated probability that actual revenue falls within ±35% of the central estimate. When confidence is low, the report shows a range only with no central estimate.
Median absolute percentage error across the search-fund target range (EBITDA £250k–£5M, 2,973 companies in the holdout set):
EBITDA derivation. Where net profit is available, we derive EBITDA by adding back depreciation, amortisation, and interest. A cascading XGBoost model (68 features) estimates EBITDA directly when the components are not individually available.
Valuation orientation. Indicative valuation range based on sector-appropriate multiples applied to normalised EBITDA. A market orientation, not a formal valuation.
Spike-year detection. One-off events — large asset revaluations, exceptional gains or losses — are identified and flagged. Contaminated metrics are suppressed rather than displayed with misleading values.
Risk flags are surfaced separately from the Exit Score. They do not reduce the score — they sit alongside it so the buyer makes their own judgment.
Flags fall into three severity tiers: critical (issues that could block a deal), warning (areas requiring investigation), and positive (structural strengths that derisk the acquisition). Each flag includes a plain-language explanation and, where applicable, a suggested diligence action.
Common triggers: key-person dependency (sole director, no management depth), customer or supplier concentration patterns inferred from balance sheet structure, deferred capital expenditure, data quality concerns (limited filing history, abbreviated accounts), financial anomalies, and sector-specific risks drawn from a knowledge base covering regulatory exposure and industry-specific structural factors.
UK business ownership is rarely a single entity. We detect holding structures, suppress non-trading shells, and estimate consolidated group financials where subsidiary data is available.
Detection. Four independent methods: SIC code classification (642xx, 643xx, 7010), PSC ownership chain analysis, name pattern matching, and financial profile detection — a deterministic model that catches shell entities missed by the other three methods.
Two types, different treatment. Group holding companies — parents of active UK trading subsidiaries — are genuine acquisition targets and appear in search with a group holding label. Financial profile holdings — shell entities, treasury vehicles, dormant intermediaries — are suppressed from search entirely. Revenue estimation is suppressed for both.
Estimated group consolidation. When a company is the holding parent of UK subsidiaries, the report includes an estimated consolidated view: revenue, EBITDA, profit, net assets, net debt, and headcount aggregated across all active subsidiaries. The holding entity’s own revenue is excluded to avoid double-counting intercompany management fees.
Subsidiary perspective. When an entity is itself part of a group, the report shows the same consolidated view from the subsidiary’s perspective — parent, all active siblings, and the entity itself aggregated into a single group picture.
Pre-exit restructuring signal. A holding company incorporated within the last three years that owns established trading businesses is a recognised pattern for pre-exit restructuring. We detect this and factor it into the exit timing score.
All data is sourced from Companies House bulk data products, published under the Open Government Licence v3.0. We process the full register: company profiles, officer appointments, annual accounts filings (including XBRL-tagged financial data), persons of significant control records, and company charges.
What we have: Balance sheet data for most filed accounts, officer dates, director birth month and year (not full dates), filing dates and types, PSC ownership chains, company charges, SIC codes, registered office addresses, company status history.
What we do not have: Profit and loss data for micro-entities and most small companies filing abbreviated accounts (the majority of UK SMEs), real-time trading data, management accounts, customer lists, contracts, employee-level data beyond declared headcount. Director ages have a ±1 year margin. Revenue is estimated, not filed, for the vast majority of scored companies.
XBRL parsing only. ExitRadar reads accounts filed in XBRL (iXBRL) format. The vast majority of UK companies in the search-fund target range file in XBRL, but a small minority — typically older micro-entities, dormant entities, or paper filers — submit PDF-only accounts that we cannot machine-read.
No metric in an ExitRadar report is shown in isolation. Every financial figure, score component, and operational metric is benchmarked against the company’s full sector peer set — companies in the same activity classification and EBITDA band.
Reports include a comparable companies table ranking the target against sector peers on Exit Score, estimated revenue, EBITDA margin, headcount, and average director age. Sector averages are displayed as a baseline.
This contextualisation matters because a 12% EBITDA margin means something different in construction (where 8% is typical) than in B2B software (where 25% is the floor).
ExitRadar reports are pre-approach intelligence. They identify which companies are worth your time and give you enough context to approach with confidence and specificity. They are not a substitute for due diligence.
Management accounts, site visits, customer concentration analysis from real data, and direct conversation with the owner remain essential. Our methodology gets you to the table with a well-informed view. What happens at the table is yours.