How Pricing Discrepancies Expose Critical Tech Debt During M&A Technical Due Diligence

When Pricing Secrets Reveal Hidden Tech Debt in M&A Deals

When buying a tech company, most teams focus on revenue projections. But in my 37 acquisitions across e-commerce and marketplaces, I’ve learned pricing inconsistencies often reveal dangerous tech debt. Let me show you how these red flags operate – and why they matter more than any financial spreadsheet.

The $1,000 Coin That Exposed a $10M Problem

Remember that APMEX coin listing priced $500 higher than the original dealer? We found something similar during one acquisition. Identical products showed $1,000+ price gaps across channels. Not a sales tactic – a tech failure screaming “Walk away!” Here’s what we found underneath:

The Real Culprits Behind Pricing Gaps

• Inventory APIs that hadn’t talked to each other in years
• CSV files manually uploaded like it’s 1999
• Three different pricing engines fighting for control

How Scaling Limits Become Integration Nightmares

One acquisition taught me this the hard way. The target’s “enterprise-ready” system choked at 10 simultaneous users. Post-merger, we spent more rebuilding their inventory system than we paid for the company itself.

5 Make-or-Break Questions We Ask Now

• What’s the most SKUs you’ve ever handled without crashing?
• How do you prevent selling phantom inventory?
• When prices change, how fast does that update everywhere?
• Show us your API error logs from peak traffic
• Can your database handle 10X today’s load?

We always request code samples like this to spot hidden issues:

// Sample inventory update endpoint
app.post('/inventory', async (req, res) => {
try {
const { sku, quantity } = req.body;
// Critical flaw: No transaction locking
await db.query('UPDATE inventory SET stock = stock - $1 WHERE sku = $2',
[quantity, sku]);
res.status(200).json({ success: true });
} catch (error) {
// No retry logic implemented
res.status(500).json({ error: 'Update failed' });
}
});

When “Real-Time” Really Means “Maybe Tomorrow”

Once found a pricing engine that updated every four hours – via cron jobs straight from the dial-up era. The tech debt buried in their systems cost us $2.3 million extra to fix. Now we look for:

Danger Signs in Code

• API calls that freeze pricing calculations
• Marketplace fees set in stone (literally hard-coded)
• No safety nets for inventory crashes

What Healthy Systems Look Like

• Events flowing through message queues
• Daily automated reconciliation checks
• Pricing tests running behind feature flags

Our Tech Risk Scorecard – Born From a $14M Mistake

After overpaying for a beautiful frontend hiding a backend disaster, we built this evaluation framework:

Where to Focus Your Tech Due Diligence

• Data consistency across systems (40%)
• APIs that don’t crumble under pressure (25%)
• How fast systems recover from failures (20%)
• Technical debt lurking in code (15%)

Pro Tip: Make them demonstrate inventory updates across all channels live during due diligence. If it takes longer than 5 seconds, dig deeper.

Pricing Systems: The Truth Teller in Tech M&A

Through costly mistakes, we’ve learned consistent pricing across channels usually signals:

• Modern architecture that can scale
• Smart automation preventing human errors
• Real-time monitoring catching issues early

Those $1,000 price differences? They’re not just negotiation leverage – they’re X-rays showing structural cracks. In M&A tech due diligence, treat pricing discrepancies like smoke alarms. Where there’s smoke, you’ll usually find a five-alarm fire in the codebase.

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