How do you account for customer attrition in intangible asset valuations?
Short Answer
Customer attrition is modelled by analysing historical churn data at the cohort level, applying survival curves to project future revenue decline from existing customers, and determining the weighted-average useful life.
Full Explanation
Customer attrition modelling is the foundation of customer relationship valuation because it determines how long the acquired customer base will generate economic benefits. The process begins with historical analysis: examine customer-level revenue data for 3-5 years to calculate annual retention rates by cohort (the percentage of customers from each vintage year that continue to generate revenue). Common attrition metrics include: logo retention rate (percentage of customers retained, regardless of revenue changes), revenue retention rate (percentage of revenue retained from the same customer base), and net revenue retention (which includes expansion revenue from existing customers). Once historical attrition rates are established, survival curves project the expected revenue from existing customers into the future. The most common models are: constant attrition (same percentage lost each year — simplest but least realistic), declining attrition (lower attrition as the customer base ages, reflecting that long-standing customers are less likely to leave), and cohort-specific attrition (different rates for different customer segments). The weighted-average useful life is determined from the survival curve — typically the period over which 80-95% of the asset's value is consumed. For SaaS businesses, NRR above 100% creates a unique modelling challenge because existing customer revenue grows over time. In this case, the customer relationship valuation must carefully distinguish between revenue from existing customers (included) and revenue from genuinely new customers (excluded). Attrition assumptions are among the most scrutinised inputs in a PPA, and auditors typically request supporting cohort data and sensitivity analysis around the selected attrition rate.
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