What are earnouts and how do they affect deal pricing?
Short Answer
Earnouts are contingent payments in M&A: the seller receives additional payment if the acquired company hits post-acquisition targets (revenue, profit, retention).
Full Explanation
Earnouts bridge valuation gaps: buyer and seller disagree on fair value. Solution: buyer pays base price (buyer's estimate), seller gets additional payment (earnout) if targets are hit. Example: seller wants £100M; buyer offers £80M. Earnout structure: £80M upfront + up to £20M earnout if revenue grows 20% annually for 3 years. This aligns seller and buyer incentives: seller stays motivated to support integration and growth; buyer reduces downside risk if projections miss. Earnout mechanics are critical: how are targets defined (revenue, profit, customer retention)? Who controls the operating decisions that affect targets? Disputes are common: seller claims buyer sabotaged growth; buyer claims seller was overly optimistic. For valuations, earnouts affect entry pricing: a deal at £80M base value with £20M earnout potential is really priced at £80M minimum, £100M maximum depending on execution. Financial reporting depends on the buyer's framework: under IFRS 3 the earnout is recognised at fair value at acquisition, and later changes in an earnout classified as a liability go through profit or loss, not goodwill; under FRS 102 section 19 it is included in the cost of the combination when payment is probable and can be measured reliably, and later revisions adjust cost and goodwill. For companies considering acquisition, earnouts can be attractive if you're confident in hitting targets: you get certainty of deal (base price) plus optionality (earnout upside). For buyers, earnouts are protective: if post-acquisition performance disappoints, they avoid overpaying.
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Further Reading
AI in Private Equity: Due Diligence for AI-Enabled Targets
Private equity firms are acquiring AI-enabled targets at record rates, but traditional due diligence frameworks miss critical AI-specific risks. This article provides a comprehensive AI due diligence methodology covering technical, commercial, regulatory, and talent dimensions.
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