In virtually every SaaS acquisition, customer relationships are the most valuable identifiable intangible asset — often representing 40-60% of the total intangible value allocated in a purchase price allocation. This is hardly surprising. A SaaS business is fundamentally a machine for acquiring, retaining, and expanding customer relationships. The technology platform, the brand, and the workforce all exist in service of that central asset.
Yet customer relationship valuations in SaaS businesses are frequently challenged — by auditors, regulators, and acquirers themselves — because the standard valuation frameworks were developed for traditional businesses with very different customer economics. SaaS customer relationships behave differently from those in manufacturing, retail, or professional services, and the valuation must reflect those differences.
SaaS customer relationships are fundamentally different from traditional customer relationships. Their value is driven by recurring revenue, expansion dynamics, and net retention rates rather than one-off transactions. The valuation approach must capture these SaaS-specific economics.
The Multi-Period Excess Earnings Method is the standard approach for valuing customer relationships because customer relationships are typically the primary intangible asset — the central asset around which the business generates its earnings. The Relief from Royalty method is generally not suitable because customer relationships are rarely licensed to third parties, making comparable royalty rates unavailable.
Under MPEEM, the customer relationship value equals the present value of the excess earnings attributable to the existing customer base after deducting contributory asset charges for all other assets that participate in generating those earnings.
A SaaS company has £20M ARR from existing customers, a technology platform valued at £15M, and an assembled workforce valued at £3M. After deducting the returns required by these contributing assets (say 12% on technology and 15% on workforce), the remaining excess earnings are attributable to the customer relationships. These are then discounted to present value over the expected customer lifetime.
The most critical input in a customer relationship valuation is the attrition curve — the rate at which existing customers are expected to leave over time. In traditional businesses, attrition is modelled using historical churn rates applied uniformly. SaaS businesses require a more nuanced approach.
| Metric | Definition | Typical Range (Enterprise SaaS) |
|---|---|---|
| Gross logo churn | % of customers lost per period | 5-15% annually |
| Gross revenue churn | % of revenue lost from churned customers | 8-20% annually |
| Expansion revenue | Revenue growth from existing customers | 10-30% annually |
| Net revenue retention | Revenue retained + expansion from existing cohort | 100-130% annually |
The distinction between logo churn and revenue churn matters enormously. A SaaS company might lose 10% of its customers annually (logo churn) but only 5% of its revenue (revenue churn) because smaller customers churn at higher rates. Furthermore, the expansion revenue from remaining customers may more than offset the lost revenue, producing net revenue retention above 100%.
Enterprise, mid-market, and SMB segments typically have very different churn profiles. Valuing them as a single pool obscures critical dynamics.
Plot historical retention by customer vintage. SaaS customers who survive the first year typically have much lower churn rates in subsequent years.
Revenue-weighted churn is the economically relevant metric. A customer representing £2M ARR leaving has a very different impact from ten £20K customers leaving.
This is a judgement call. Expansion revenue from existing customers reflects the value of the relationship, but including it requires careful separation from growth attributable to new products or features.
IFRS 3 and ASC 805 distinguish between contractual and non-contractual customer relationships, and SaaS businesses often have both:
In practice, most SaaS businesses have a mix. Enterprise customers with 3-year contracts and auto-renewal clauses represent contractual relationships. Self-serve SMB customers on monthly plans are non-contractual. The valuation should treat these segments separately, with different attrition assumptions and useful lives.
For contractual relationships, the useful life extends beyond the initial contract term to include expected renewals — but only if there is historical evidence supporting the renewal assumption. A 3-year contract with a 90% historical renewal rate might have a useful life of 8-12 years.
Net revenue retention (NRR) above 100% creates a valuation challenge. If existing customers generate more revenue over time than they did initially, the customer relationship value increases — but the excess earnings also need to be separated from the value attributable to new products and features (which are technology assets, not customer relationship assets).
The practical approach is:
The contributory asset charges deducted in a SaaS MPEEM typically include:
| Contributory Asset | Charge Basis | Typical Rate |
|---|---|---|
| Working capital | Fair value of net working capital | Risk-free rate (3-5%) |
| Fixed assets | Fair value of PP&E | Pre-tax WACC (8-12%) |
| Assembled workforce | Replacement cost of team | 15-20% of replacement cost |
| Technology platform | Fair value (usually from RFR) | 10-15% of fair value |
| Trade name | Fair value (usually from RFR) | 8-12% of fair value |
The technology platform charge is particularly important in SaaS valuations because the platform is essential to delivering the subscription service. An underestimated technology charge inflates the customer relationship value; an overestimated charge deflates it. Getting this right requires the technology asset to be valued first, typically using the Relief from Royalty method.
Using logo churn instead of revenue churn. Logo churn overstates attrition in SaaS because high-value enterprise customers churn at lower rates than low-value SMB customers.
Ignoring cohort vintage effects. Year-1 churn is always higher than year-5 churn. Applying a flat annual churn rate significantly undervalues long-lived customer relationships.
Double-counting with backlog. If the PPA also identifies an order backlog intangible (for contracted but undelivered revenue), the customer relationship cash flows must exclude the backlog period to avoid double-counting.
Failing to separate expansion from new customer revenue. Revenue growth comes from both existing customers expanding and new customers joining. Only the former contributes to customer relationship value.
Customer relationships in SaaS businesses are unique: they generate recurring revenue, they expand over time, and they exhibit vintage-dependent attrition patterns. Valuations that ignore these dynamics will misstate the most important intangible asset in the business. The Opagio Valuator models SaaS-specific customer relationship valuations with segment-level attrition curves and NRR adjustments. Try it free.
Ivan Gowan is the Founder and CEO of Opagio. With 25 years in fintech — including leadership roles at IG Group — Ivan brings operational experience in building and scaling SaaS businesses, including the customer relationship dynamics that drive their value. Meet the team.
Estimate the value of your intangible assets using industry-standard methods like Relief from Royalty, MPEEM, and With & Without.
A practical guide to valuing customer relationships as intangible assets using the MPEEM method. Covers churn analysis, contributory asset charges, attrition modelling, and the distinction between contractual and non-contractual relationships.
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How the Weighted Average Return on Assets (WARA) reconciliation ensures internal consistency in purchase price allocations. Includes a step-by-step worked example and common pitfalls to avoid.
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How to determine appropriate discount rates for intangible assets in purchase price allocations and standalone valuations. Covers WACC adjustments, asset-specific risk premiums, and the relationship between discount rates and the WARA reconciliation.
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