Financial due diligence · Customer analysis
Which customer analyses are indispensable in an acquisition?
Revenue tells you how much a business has sold. A customer analysis tells you how solidly that revenue actually stands. For a buyer, that difference is everything.
A profit and loss account can show handsome growth. On its own it says very little about the customers carrying that revenue. Does the growth come from many new customers or from one exceptionally large account? Do customers return each year? Is the core market growing, or is a temporary project masking a decline elsewhere?
That is why a customer analysis belongs in almost every financial due diligence. Not as an extra dashboard at the back of the file but as one of the building blocks of the purchase decision.
1. How many active customers are there really?
The first question looks simple: how many customers does the business have?
Even so, you have to decide up front what counts as an active customer. Does every historical customer record count? Only customers with revenue in the period under review? And do you treat different sites or legal entities as one customer group or separately?
A reliable answer therefore starts not with COUNT but with a clear definition and a stable customer number. Without a unique key field, duplicate names, name changes and spelling variants can distort the customer count.
2. Which customers come back?
A returning customer is not automatically the same thing as contractually recurring revenue. Repeat purchase still says a great deal about the durability of the customer relationship.
Look per customer at the periods in which revenue was realised. That makes visible which customers buy once, which come back regularly, which fall away and which order again after a break.
The relevant measure depends on the business model. In project based services, annually recurring revenue can be meaningful. In high frequency trade, monthly or quarterly patterns may matter more.
3. How concentrated is the revenue?
Customer concentration shows how much revenue depends on a small number of accounts. The usual questions are:
- what percentage of revenue comes from the largest customer;
- how much do the top 5, top 10 or top 20 represent;
- is that concentration rising or falling;
- which significant customers are growing or shrinking.
High concentration is not bad by definition. A strong long term relationship with a large customer can be valuable. The buyer does need to understand what happens if that customer lowers volumes, renegotiates or leaves.
4. What types of customer make up the revenue?
A single revenue figure can conceal several economic realities. B2B and B2C, distributors and end customers, large enterprises and SMEs can each carry a different sales process, margin profile and risk.
Segmenting by customer type helps you see where growth and return actually come from. It also shows whether the reported strategy matches the real revenue mix.
5. How is revenue spread geographically?
A geographical analysis shows which countries or regions the business is genuinely active in. That matters for growth opportunities and equally for dependency, local regulation, currency and commercial reach.
Watch the quality of the location field here. The invoice address, the delivery address and the country of the legal customer can each mean something different. Pick the field that fits the question and document that choice.
6. How do customers develop over time?
A ranking of customers by total revenue is only a snapshot. The movement over time tells you more:
- which customers grow structurally;
- where revenue is starting to fall away;
- which new customers quickly become significant;
- whether growth is broadly carried or comes from exceptions.
Good visualisation makes that movement visible without hiding the underlying detail.
7. Does customer revenue reconcile to the accounts?
This is the check that makes the other analyses credible. Totals from sales reports or invoice data have to reconcile to the relevant revenue in the general ledger.
Differences do not automatically point to errors. Credit notes, posting dates, manual entries, intercompany transactions or a different scope can all explain them. What the differences do have to be is visible and explainable.
A handsome customer dashboard only becomes DD grade once the numbers trace back verifiably to the underlying invoices and postings.
The analysis starts before the dashboard
These analyses need more than an Excel file with customer names and revenue. At a minimum, make sure you have:
- a stable and unique customer number;
- sales invoices and credit notes at line level;
- journal entry lines from the general ledger;
- usable customer attributes such as type, country and region;
- a reconciliation between commercial and financial totals;
- clear definitions of active, returning and concentrated.
Without that base, expensive DD time goes into repairing data instead of assessing the business.
From separate analyses to one verifiable model
Where the data is available in a supported accounting system, Insitely retrieves the relevant accounting and invoice data through a secure connection. It is then structured for further analysis in Power BI and Excel.
An external analyst no longer has to rebuild the same exports, joins and checks for every file. The familiar analysis tools stay and the preparatory data process becomes far more consistent.
Which customer questions cost you the most work today?
Put your current DD process next to these seven analyses. In a substantive conversation we look at where data is missing, where manual checks keep coming back and where Insitely can speed the process up.
About Roel Bäumer
Eighteen years in data and BI by now, with medium to large businesses. With Insitely I combine a reusable financial data layer with implementation and M&A data expertise. Where a file calls for extra analysis or bespoke work, I can also join an existing deal team as a data specialist.
Further reading: Why detailed general ledger data is indispensable in financial due diligence