A target company presents a five-year forecast projecting revenue growth from Rs 300 crore to Rs 500 crore, EBITDA margin expansion from 14% to 18% and free cash flow generation of Rs 250 crore cumulative. The model is internally consistent, well-formatted and supported by a 40-page strategy deck.
The question is not whether the model is well-built. It is whether the assumptions embedded in it are credible when decomposed into their operational components.
The Decomposition Methodology
A revenue forecast of Rs 500 crore in Year 5 is not testable as a single number. It becomes testable when decomposed:
Revenue = Customers x Volume per Customer x Price per Unit
For each component, ask: what assumption is embedded, and what evidence supports it?
Customers: The forecast assumes 150 active customers in Year 5, up from 100 today. That requires net addition of 50 customers over five years: approximately 15 new customers per year less 5 churned, for a net gain of 10 per year. Is the company currently adding 15 new customers per year? What is its current churn rate? Is the customer acquisition cost consistent with the budget allocated to sales and marketing?
Volume per customer: The forecast assumes average volume per customer increases by 20% over five years. What drives the increase? Product expansion? Price-tier migration? Geographic expansion within existing accounts? Is the assumption supported by the cohort data showing existing customers expanding their purchases?
Price per unit: The forecast assumes 3% annual price increases. Has the company achieved 3% price increases in the last three years? How price-sensitive are its customers? What is the competitive pricing dynamic?
Each assumption, when decomposed, produces a specific, testable claim about the business that can be verified against historical data, market evidence and operational capability.
Challenging the Margin Assumption
EBITDA margin expansion from 14% to 18% is a claim about the cost structure. Decompose it:
Revenue growth faster than cost growth: Which costs are fixed and which are variable? At what revenue level do fixed costs create operating leverage? Is the projected leverage consistent with the company’s actual cost behaviour at lower revenue levels?
Input cost reduction: Does the forecast assume raw material costs decline? Is that assumption consistent with commodity price trends and supplier pricing behaviour?
Operating efficiency: Does the forecast assume productivity improvements? What specific initiatives are planned? Are they funded in the capex and opex budget? What is the company’s historical track record of delivering efficiency improvements?
Mix improvement: Does the forecast assume a shift toward higher-margin products or customers? Is the shift supported by the sales pipeline, the product roadmap and the competitive positioning?
Challenging the Cash Flow Assumption
Cumulative free cash flow of Rs 250 crore over five years is the ultimate test. Decompose it:
Operating cash flow = EBITDA - tax - working capital change. Is the working-capital assumption consistent with the revenue growth? At what rate do receivables and inventory grow relative to revenue?
Free cash flow = Operating cash flow - capex. Is the capex budget sufficient to support the projected revenue? Is maintenance capex adequate, or has it been suppressed to improve the cash flow projection?
In Northrop Management Private Limited’s due diligence practice, the Management Plan Challenge decomposes every significant projection into its component assumptions, tests each against evidence and reassembles the forecast using the evidenced assumptions rather than the management’s preferred ones. The gap between management’s forecast and the evidenced forecast is the optimism premium, and it is the single most important output of commercial diligence.
Ashish Chaudhary, frames the challenge discipline directly: “A forecast becomes testable when its assumptions are decomposed. Rs 500 crore of revenue is a narrative. 150 customers at Rs 3.33 crore average revenue, growing at 10 net additions per year with 3% annual price increases, is a set of hypotheses that can be tested against data. The buyer who tests the hypotheses will pay for what is evidenced. The buyer who accepts the narrative will pay for what is hoped.”
Questions for the Boardroom
- Have we decomposed the target’s revenue forecast into customers, volume and price, and tested each component against historical data?
- What is the target’s historical forecast accuracy, and what discount should we apply to the current projections?
- Is the margin expansion assumption supported by identified, funded, accountable initiatives, or is it a general aspiration?
- Is the capex budget sufficient to support the projected revenue, or has capex been suppressed to improve the cash flow forecast?
- If we replaced every assumption in the management plan with the historical average, what would the forecast look like?
Closing Implication
A forecast is a collection of assumptions. The quality of the forecast depends on the quality of the assumptions. Decomposition converts a narrative into testable hypotheses, and testing the hypotheses against evidence is the discipline that separates informed acquisition pricing from optimistic acquisition pricing.
