DEEP DIVE
3 Data Problems Killing Your Exit Multiple (And How Buyers Spot Them in 48 Hours)
Buyers don't care about your data strategy. They care about one thing: can they trust the numbers you're showing them?
When they can't, they don't negotiate. They walk. Or worse, they stay at the table and systematically dismantle your valuation.
Problem 1: Revenue Attribution That Doesn't Add Up
Here's what happens in month two of diligence.
The buyer's team pulls your revenue reports from three systems. Finance says $47M. Your CRM says $51M. The marketing attribution model says $49M.
None of them reconciles.
The buyer doesn't think you're lying. They think you don't know. And if you don't know your own revenue sources, what else don't you know?
Picture a $35M performance marketing agency preparing for exit. Marketing ops reports show they drove $28M in attributed revenue through paid channels. Sales leadership claims $31M from direct outreach and partnerships. Finance closes the books at $35M total revenue.
The buyer's diligence team spots it immediately. The numbers literally can't exist in the same universe. Either there's $24M in overlap where both teams are claiming credit for the same deals, or someone's tracking is fundamentally broken.
What the buyer learns: the agency's attribution model gave fractional credit to every touchpoint. A customer who clicked three ads, attended a webinar, then responded to a sales email? Marketing claimed 80% credit. Sales claimed 100% credit. Nobody enforced source-of-truth rules.
The agency wasn't lying. They just never built systems that forced a single version of the truth.
This costs you 0.5x to 1x EBITDA multiple. Not because the revenue isn't real. Because the buyer assumes there are ten more problems just like this one hiding in your systems.
Problem 2: Customer Data That Can't Survive Integration
Private equity-backed companies get acquired for their customer base. But here's what strategic buyers discover when they try to integrate.
Half your customer records are duplicates. Email addresses don't match between systems. Purchase history is fragmented across platforms. The "single customer view" your CEO promised doesn't exist.
The buyer is already modeling post-acquisition integration costs. Now they're adding six months and $2M to clean up your data before they can even start capturing synergies.
That integration risk comes straight out of your purchase price.
Take a B2B SaaS company at $18M ARR selling to a strategic acquirer. Their pitch deck showed pristine cohort analysis. Customer lifetime value trending up. Churn rates are declining quarter over quarter.
Then the buyer's integration team starts mapping the customer database to their own systems.
Customer ID 7742 exists four times with different email domains. The "enterprise" tier subscription is tagged differently across Stripe, Salesforce, and the product database. Usage data lives in a separate analytics warehouse that can't be joined to billing records without a 47-step ETL process that someone built in Python two years ago.
The cohort analysis that justified the $4.2M EBITDA multiple was built on customer groupings that don't actually exist in any system. An analyst manually categorized customers every quarter based on "industry feel" and company website reviews.
The buyer doesn't kill the deal. They just recalculate integration costs and timeline. Six months becomes twelve months. $1M becomes $2.5M. And that money comes straight out of the purchase price, dollar for dollar.
Yahoo learned this the expensive way. When Verizon discovered undisclosed data breaches during diligence, they cut the purchase price by $350M. Not for the breach itself. For the operational chaos and integration risk it revealed.
Problem 3: The "Dashboard Theater" That Fools No One
Your executive dashboards look beautiful. Your board loves them. Your CEO presents them at every meeting.
Then, a buyer's data team asks to see the underlying data model.
What they find: manual exports from five different systems, stitched together in Excel, with formulas that break if anyone changes the date range. The dashboard refreshes weekly because that's how long it takes someone to rebuild it.
This is what I call dashboard theater. Pretty visualizations sitting on top of garbage data. And buyers spot it immediately.
They ask three questions that expose the problem:
- "Can you show us this metric for the same cohort across three different time periods?"
- "How do you define 'active customer' and where is that logic enforced?"
- "Walk us through how this forecast ties back to individual customer contracts."
If you can't answer these in real time with system-generated reports, you've just told the buyer your operational intelligence is manual, fragile, and unreliable.
Here's how it plays out at a management consultancy with $42M in revenue.
The CFO presents gorgeous utilization dashboards. Billable rates are trending at 73% across the firm. Project profitability averaging 38% margin. Resource allocation optimized across practice areas.
The buyer asks to see Q2 utilization by consultant, broken down by project type, compared against the same period last year.
Three days pass. The CFO comes back with a PDF. Not a live report. A screenshot.
The buyer pushes. "Can we see the underlying data model?"
What emerges: "utilization" is defined differently across practice areas. Strategy consulting counts proposal work as billable. Digital transformation doesn't. Internal training hours are billable in one group, overhead in another. The 73% firm-wide number is a weighted average calculated in Excel each month by someone who manually reconciles timesheets, project codes, and payroll data.
The dashboard looks sophisticated. The data infrastructure is held together with duct tape and one analyst who knows which cells to update.
The buyer doesn't walk. But they just added "data infrastructure overhaul" to their integration plan. And they're modeling it as a 6-month delay before they can reliably measure or improve the business.
That delay costs you at the negotiating table. Because time is money, and buyers price in every month, they can't drive operational improvements.
That's it. Here's what you learned today:
- Revenue attribution conflicts cost you a 0.5-1x EBITDA multiple when buyers can't reconcile your numbers
- Customer data chaos adds $2M in integration costs that come directly out of your purchase price
- Dashboard theater breaks under buyer scrutiny and signals deeper operational problems
Start this week: Pull your last three months of revenue data from every system that touches it. If the numbers don't match within 2%, you have a problem that will cost you millions at exit. Fix it now, not when you're in month two of diligence.
The exit window opened. Just not for mid-market companies with data problems.
Mega-deals are closing. IPOs are back. But buyers are pickier than ever about which mid-market companies get through the door. And the first filter is data quality.
You can either clean this up now or watch bigger portfolio companies exit while you sit on the shelf for another three years.