DEEP DIVE
Why Excel Makes Deals Die in Diligence
Excel runs 90% of private equity exits. And right now, it's absolutely destroying them.
PE firms are being cautious right now, scrutinizing every number, questioning every model. In this environment, the difference between a clean exit and a blown deal increasingly comes down to one thing: whether your critical data lives in Excel or in systems built for institutional scrutiny.
This isn't about Excel being "bad" - it's a great tool in the right circumstances. This is more about companies outgrowing the tools that got them started. That spreadsheet tracking revenue recognition worked fine at $5M ARR. But at $45M? When a QoE team starts pressure-testing your ASC-606 compliance? That's when formulas break, links fail, and buyers start applying valuation haircuts.
Here's what's happening in deal rooms right now:
- Revenue recognition disasters that push timelines by weeks
- Cohort analyses that won't reconcile, triggering automatic discounts
- Version chaos that stops model reviews cold
Let's dig into the carnage.
When ASC-606 Lives in Excel, Deals Die in Diligence
Imagine a $45M ARR SaaS portfolio company that has their exit process ground to a halt when QoE testing reveals timing mismatches in their Excel-based revenue recognition, especially around multi-element contracts and mid-term upgrades.
The banker has to pause everything while finance rebuilds schedules from scratch.
Why does this keep happening? Because ASC-606 demands consistent treatment of performance obligations, but in Excel, formulas drift. Logic gets copied inconsistently across tabs. Manual overrides pile up. The moment diligence applies real pressure, these Band-Aids come apart.
The early warning signs are always there:
- Revenue schedules with hidden tabs and manual overrides
- Contract changes handled by overwriting cells, not versioned entries
- "Reconciling items" that mysteriously clear each month
The fix isn't hugely complicated but most companies don't realize they need it until they're already in the data room.
The Cohort Confusion That Costs Millions
Here's another scene playing out weekly: The buyer asks for logo and net revenue retention by cohort. GTM ops produces a stitched-together Excel file from CRM exports. When QoE rolls it forward, nothing ties to finance; upgrades are double-counted, downgrades vanish into formatting errors.
The buyer's response? Apply a provisional haircut to NRR until the data can be proven.
Remember TransAlta? They lost $24M from a simple spreadsheet misalignment. The same logic that impacted that deal is killing PE exits every single day.
You know you have this problem when:
- Multiple cohort files exist, each defining "churn" differently
- Retention metrics won't reconcile to ARR bridges
- Someone says "We just need to refresh the VLOOKUPs" before every send
Version Hell: When "Final_v7" Meets Private Equity
The most preventable disaster? Version chaos in the data room.
The sell-side tracks diligence tasks in Excel and manually uploads files to the VDR. Soon, multiple versions of the same KPI schedule are circulating among different buyer workstreams. In a model review call, figures conflict. Buyers halt analysis until they know which version is correct.
That pause costs a week minimum. Trust? That's gone forever.
A successful private equity exit strategy depends on clean, accurate, and transparent data. Poor data quality leads to lower valuations, prolonged due diligence processes, and lost deals. Studies indicate that 70%-90% of M&A deals fail to meet expectations, often due to flawed due diligence.
The Bottom Line
The average PE holding period hit 6.1 years in 2024. With US buyout funds currently holding more than 12,000 companies that would take approximately nine years to fully distribute at current rates, the pressure to execute clean exits has never been higher.
Yet most portfolio companies are still running their most critical processes – the ones that directly impact valuation – in tools that were never designed for institutional scrutiny.
The tragedy is that these aren't complex problems to solve.
- A controlled revenue recognition system
- Automated cohort tracking
- Proper version control.
These are table stakes for any company expecting to trade at a premium multiple. But because Excel "works" (until it doesn't), companies keep pushing the upgrade to next quarter.
By the time you're in the data room, it's too late. The buyers have already seen your Excel chaos. They've already adjusted their models. They've already decided you're a risk.
Three things you can do this week:
- Audit your recognition: Pull your revenue schedules and look for manual overrides, hidden tabs, and "temporary" fixes that have become permanent
- Test your cohorts: Can you reconcile retention metrics to your financial statements in under an hour? If not, you have a problem
- Check your versions: How many "final" versions of your key metrics exist? If it's more than one, you're not ready for diligence
The good news? Fixing these issues before you go to market can mean the difference between a competitive auction and a desperate fire sale. In today's exit environment, that's the difference between returning 5x to your LPs and explaining why you need another two years.
Data quality isn't IT plumbing. It's valuation insurance. And right now, most PE-backed companies are drastically underinsured.