Commercial Due Diligence Red Flags

Executive summary

Commercial due diligence red flags are the signs in a target's customer, pricing and pipeline data that the revenue in the model is weaker, less repeatable or less diversified than the seller's presentation suggests. A buyer pays a multiple of earnings, and earnings depend on revenue that continues. The commercial review exists to test whether it will.

The most useful red flags are not exotic. They are customer concentration, quiet churn behind a growing top line, growth that came from price rather than volume, a pipeline that has not converted at the rate the forecast assumes, discounting that has been buying revenue, contracts that allow customers to leave or reprice at will, and customer relationships that belong to one person. Each can be tested from the target's own customer-level data in days, without a market study.

The method is to rebuild the revenue from the invoice file. Start with a customer-by-period table, and compute concentration, retention, price and volume effects, and contract coverage yourself rather than accepting the seller's summary. Then convert each flag into one of four outcomes: a price adjustment, a revenue haircut in the model, an action in the first 100 days, or a condition in the purchase agreement.

Contract interpretation, change-of-control rights, exclusivity, most-favored-customer terms and similar matters are for counsel. This guide tells you which terms to pull out and where the money is. It is part of the due diligence hub and works alongside financial due diligence and the quality of earnings review.

When to use this

Use this guide when:

• The investment case depends on revenue growth, a pricing action, or the stability of a few large customers.
• Reported revenue has grown but the seller cannot explain how much came from new customers, existing customers and price.
• The target sells through a small number of relationships, distributors or channel partners.
• A single customer, product or end market is described as "most of the business" or "sticky" without data.
• You are screening an add-on and need a fast revenue-quality read. The add-on acquisition screening scorecard is built for that.
• You want to test a seller-prepared report. The vendor-led diligence versus sell-side QoE guide covers how to read it.

If you cannot obtain customer-level data for at least three years, treat that as the first red flag. A business that cannot say who bought what, when, is a business that cannot show its revenue is repeatable.

Diagnostic questions

Put these to management and answer as many as you can from the invoice file before the call.

1. Provide revenue by customer, by product or service, and by month for the last 36 months, tied to the general ledger.
2. What share of revenue comes from the top customer, the top five and the top ten, and how has that moved over three years?
3. For customers who were active at the start of each year, what share of revenue was retained, lost, reduced and expanded?
4. How much of the revenue growth over three years came from price increases, from higher volume or mix, and from new customers?
5. Which customers have contracts, what are the terms (length, pricing, exclusivity, termination rights, minimum volumes), and which are simply repeat purchasers with no commitment?
6. How long has each of the top 20 customers been with the company, and who in the company owns each relationship?
7. Which customers have reduced orders, extended payment, asked for price relief, or started dual-sourcing in the last twelve months?
8. How much revenue was recognized from one-time projects, pull-forward orders, bill-and-hold arrangements or year-end promotions?
9. What are the average and worst discounts and credits by customer, and who can approve them?
10. How is the pipeline defined, what did it contain a year ago, and how much of it converted to revenue?

Framework: seven red flags and how to test each

Red flag 1: Customer concentration

Concentration is not a problem in itself. It is a problem when the customer has leverage, when the relationship is unprotected, or when the customer is more profitable than the rest. Measure three things: share of revenue, share of gross margin, and share of accounts receivable. A customer that is 20% of revenue and 35% of margin is a different risk from one that is 20% of revenue and 10% of margin. Also compute the trend: a top customer that has grown from 10% to 22% in three years has become more important, and often more demanding.

Find out how the relationship is held. Does the customer have a contract, and for how long? Who owns the relationship? What is the customer's own situation: is it growing, in financial difficulty, or being acquired? The right response to concentration is usually deal structure (price, a deferred payment tied to retention, or a condition that the buyer speaks with the customer before signing, where the confidentiality terms allow) rather than walking away.

Red flag 2: Hidden churn

A growing top line can hide a leaking base. New customers fill the bucket while old ones drain out. The test is a cohort view. Take customers who were active at the start of a year and ask what their revenue was at the end. Two measures matter:

Gross revenue retention (GRR) = (Starting revenue − Lost − Downsell) ÷ Starting revenue
Net revenue retention (NRR) = (Starting revenue − Lost − Downsell + Expansion) ÷ Starting revenue

GRR cannot exceed 100% and shows how much of the base survives. NRR includes expansion and shows whether the base grows by itself. For businesses with project or transactional revenue, define the starting cohort carefully (customers who bought in the prior twelve months) and say so. The net revenue retention guide goes deeper on the definitions and the levers.

Red flag 3: Price-driven growth

Price can be a legitimate source of growth, particularly where the seller under-priced for years. It becomes a red flag when it is the only source of growth, when customers are reducing volume in response, or when the increases were one-time and cannot be repeated. Build a price, volume and mix bridge by product or product family: for each item, the revenue change from price is the new price minus the old price times the new volume, and the change from volume is the volume change times the old price. Mix is the remainder across products. Check whether customers who took larger increases reduced volume, switched products or left. The price realization rate drift guide shows how to detect leakage between list and realized price.

Red flag 4: Discount dependence

Rising discounts, credits, rebates and free freight are a way of keeping revenue that the market is no longer paying for. Compare realized price to list over time and by customer. A widening gap in the last year is worth a conversation. The pricing waterfall analysis guide shows the layers to include. Ask who can approve discounts and what the approval record shows. If there is no discount governance, there is likely further leakage that the new owner can recover, and also an indication that the revenue base was bought.

Red flag 5: Weak pipeline conversion

Management forecasts for new customers are usually built from a pipeline. Test the pipeline against its own history. Take a snapshot from twelve months ago, the list of opportunities by stage and value, and compare what happened. What share closed, how long did it take, and at what value compared with the original estimate? Apply that history, not the seller's hope, to the current pipeline. Where the CRM is incomplete or stages are inconsistent, the forecast deserves less weight. The pipeline coverage and forecast accuracy guide covers the method.

Red flag 6: Contract fragility

List the terms that determine how easily revenue can leave or reprice: term and renewal, termination for convenience, minimum volumes, price adjustment clauses, exclusivity, most-favored-customer terms, change-of-control or assignment rights, and service-level penalties. Pull them into a table so a lawyer can read them in one place. Do not interpret them yourself. A significant share of revenue under contracts that the customer can end on 30 days' notice is not contracted revenue, whatever the seller's presentation says. Contract interpretation and the effect of a change of control are questions for counsel.

Red flag 7: Relationship dependence

If customers buy from a person (the founder, a long-serving salesperson), the relationship may not transfer. Ask each of the top customers' owners in the business who else the customer deals with, and compare to who actually attends meetings and signs orders. Where customer calls are not permitted, structure protections instead: a transition period, retention terms for the person (drafted by counsel and HR), and part of the price deferred against customer retention.

Step-by-step process

1. Define the revenue assumptions in the model. List growth by customer type, price actions, new logos, retention. Owner: deal lead. Output: one-page revenue hypotheses.
2. Request customer-level data. Invoice or order data by customer, product and month for 36 months, tied to the general ledger, plus contracts for the top customers. Owner: diligence lead. Output: data received and reconciled to revenue.
3. Build the customer table. Concentration, margin by customer, trend over three years. Owner: commercial analyst. Output: concentration view with three measures.
4. Compute retention. GRR and NRR by cohort year, by segment. Owner: commercial analyst. Output: retention view, with definitions stated.
5. Build the price, volume and mix bridge. By product family, per year. Owner: commercial analyst with finance. Output: growth decomposition.
6. Test the pipeline. Compare a historical snapshot to actual conversion; apply history to the current pipeline. Owner: commercial analyst. Output: adjusted new-logo forecast.
7. Extract contract terms. Table for counsel to review. Owner: deal lead with counsel. Output: term table and counsel's notes.
8. Adjust the model and type findings. Price, haircut, action or condition. Owner: deal lead with finance. Output: updated revenue case and findings register.
9. Reconcile with financial diligence. Check revenue cut-off, credits and rebates against the quality of earnings. Owner: finance lead. Output: reconciled adjustments.

Templates

Customer concentration table (copy into a spreadsheet)

Retention cohort table

Price, volume and mix bridge

Contract term extract (for counsel)

Commercial findings register

Metrics and KPIs

Setting targets without invented benchmarks. Anchor to the target's own best three-year cohort, to contractual floors in the customer agreements, and to named listed peers who disclose retention or concentration. Do not compare retention to a published average for "B2B businesses". Retention for a distributor with repeat transactional buyers and for a company with annual service contracts is measured differently and should not be held to one number.

Common mistakes

1. Accepting the seller's customer summary. Rebuild it from the invoice file and tie it to the ledger.
2. Measuring concentration by revenue only. Margin and receivables concentration often tell a different story.
3. Counting new-logo revenue as retention. Retention is about customers who were already there.
4. Letting price growth stand in for demand. If volume is flat or falling, ask what happens when price stops rising.
5. Applying the seller's pipeline conversion. Use the historical conversion from a prior snapshot, and say so.
6. Reading contracts yourself. Extract and tabulate the terms; leave interpretation to counsel.

Worked example

Illustrative. The numbers below are invented to show the method. They are not benchmarks.

A target reports revenue of $24.0M this year, up 20% from $20.0M the year before. The seller presents this as broad-based growth. The buyer rebuilds it from the invoice file.

Concentration.

Customer A is 22% of revenue and the agreement is a one-year purchasing arrangement that the customer can end on 60 days' notice. The relationship is held by the founder.

Retention. Customers who were active at the start of the year generated $20.0M the year before. During the year the cohort behaved as follows:

• GRR = (20.0 − 2.0 − 1.0) ÷ 20.0 = 85%.
• NRR = (20.0 − 2.0 − 1.0 + 4.0) ÷ 20.0 = 105%.
• New customers contributed $3.0M, and $21.0M + $3.0M = $24.0M, which ties to reported revenue.

Price effect. The price bridge shows that $1.6M of the $4.0M expansion was price increases on existing customers.

• NRR excluding price = (20.0 − 2.0 − 1.0 + 4.0 − 1.6) ÷ 20.0 = 97%.

What this tells the deal team:

• Reported growth of 20% is real in revenue terms, but the base itself shrank slightly once price is removed (97%). Growth depended on new customers ($3.0M) and price ($1.6M).
• 15% of the starting revenue was lost or reduced in one year. That is the figure to test against the seller's claim that the business is sticky. Ask what the lost customers said.
• Customer A is the largest single exposure and is held by the founder under a short contract. That is a deal-structure conversation (a retention-linked deferred payment, a founder transition period drafted by counsel, a customer call before signing if permitted).

The findings register carries four entries: the concentration risk (structure), the retention shortfall (model haircut), the price dependence (model haircut and test), and the founder-held relationship (condition).

AI prompt assistance

AI can speed up the table-building and draft the questions. It cannot verify the data or interpret contracts. Provide the data yourself and check every figure against the ledger.

1. Build the concentration and retention tables.

I will paste an invoice-level export with customer, date, product and revenue for three years. Compute revenue by customer by year, the top 1, 5 and 10 shares, and for each year the gross and net revenue retention of customers active at the start of the year. State your definition of "active" and show the calculation for one year in full.

Check: tie total revenue by year to the general ledger, recompute one customer's retention by hand, and confirm the definition of the starting cohort.

2. Build the price, volume and mix bridge.

Given revenue and units by product family for two years, compute for each family the price effect (change in average price times current units) and the volume effect (change in units times prior average price), and show any remainder as mix or other. Show totals and confirm they equal the change in revenue.

Check: confirm that the sum of effects equals the total revenue change before using the output.

Implementation guide

Downloadable assets

The concentration table, retention cohort table, price-volume-mix bridge, contract term extract, pipeline conversion test and findings register above are designed to be copied into a spreadsheet. For the working capital side of the same transaction, see working capital peg in M&A and the working capital peg estimator.

Related resources

• Due diligence hub: the full set of diligence guides.
• Financial due diligence checklist: revenue cut-off, credits and receivables.
• Quality of earnings report before you close: how revenue findings reach the adjustments.
• Vendor-led diligence vs sell-side QoE: testing seller-prepared work.
• Add-on acquisition screening scorecard: a lighter revenue-quality screen.

Frequently asked questions

What are the main commercial due diligence red flags?

The main red flags are customer concentration, hidden churn behind overall growth, growth driven by price rather than volume, rising discounts and credits, a pipeline that converts below the forecast, contracts the customer can end easily, and relationships held by one person. Each can be tested from customer-level invoice data and the target's own contracts.

How do you test customer concentration in due diligence?

Rebuild revenue by customer from the invoice file, tie it to the ledger, and compute the top one, five and ten shares of revenue, gross margin and receivables over three years. Then check each large customer's contract term, termination rights and who owns the relationship. Concentration matters most where the customer has leverage and the relationship is unprotected.

What is the difference between gross and net revenue retention?

Gross revenue retention measures how much of the starting revenue from existing customers survives after losses and downsell, and it cannot exceed 100%. Net revenue retention adds expansion from those same customers. Reading both, and net retention excluding price increases, shows whether the base is healthy or whether growth is coming from elsewhere.

How can you tell if growth was driven by price?

Build a price, volume and mix bridge by product family: price effect is the change in average price times current units, and volume effect is the change in units times the prior price. If volume is flat or falling while revenue rises, price is carrying growth. Check whether customers with larger increases reduced orders or left.

How this works in a System of Performance

Commercial red flags change after the deal closes, which is when they matter most. Which customers are slipping, which price increases held, which discounts were approved and whether the pipeline converted are held in the ERP, the CRM and spreadsheets that rarely agree. A Performis routine can take on a recurring job like this, with the scope and fit agreed with you: it works from the records your team supplies, prepares the work for your named reviewer, and keeps each approval on record. If you want to see where your own revenue depends on a few accounts or a few price actions, bring it to us as a launch partner.