We want to move away from subjective valuation multiples and use a data-driven, regression-based model using public company data to set our target multiple. How do we build this quantitative framework to justify our premium valuation to a sophisticated private equity buyer?

Question

Tyler Smith · Accepted Answer

Traditional valuation multiples are often based on hearsay and broad industry averages that fail to account for your specific operational efficiency. To capture a premium valuation, you can use a quantitative, regression-based model similar to the Ankura framework. Start by pulling a robust dataset of comparable publicly traded companies or private transaction data from databases like Capital IQ. Use this data as your training set. Run a regression analysis to identify which financial metrics, such as revenue growth, EBITDA margin, or capital efficiency, have the strongest statistical correlation to enterprise value. Once you have established the mathematical relationship between these variables, plug your own metrics into the model. This gives you a data-driven, objective multiple based on actual market pricing rather than an investment banker's guess. When presenting this to a private equity buyer, speak their language. Show them the regression formula and the coefficients. Explain how your proprietary automation and structured EOS® processes directly drive the metrics that command a premium, such as your superior operating margins. This analytical approach removes subjectivity from the negotiation table, making it incredibly difficult for the buyer to justify a discount.