How We Found $3M Hidden in a Portfolio Company's Pricing (In 2 Weeks)

Read time: 5 minutes
Target audience: PE Operating Partners, Portfolio Company CEOs, Pricing Leaders

The portfolio company was bleeding.

Customer acquisition costs up 40%. Win rates down 25%. The board wanted answers. The CEO had theories. The data told a different story.

In 2 weeks, we built an engine that found $3M in pricing opportunities they'd been sitting on for 18 months.

Here's exactly how.

The Situation

Company: B2B SaaS platform, $30M ARR
Problem: Growth stalling despite product improvements
Hypothesis: "We need more features to justify our premium"
Reality: They were underpricing enterprise by 40%

Week 1: Encoding Their Pricing Logic

Day 1-2: Map Current State

We documented their existing pricing methodology:

How they segmented customers
What drove pricing decisions
Which competitors they tracked
How they measured willingness to pay

Key finding: They had strong pricing intuition but no systematic process.

Day 3-4: Build the Competitor Matrix

Instead of checking 3 competitors monthly, we built an engine that tracked:

27 direct competitors
15 adjacent solutions
8 enterprise alternatives

The data showed:

They were priced 15% above SMB competitors
They were priced 40% below enterprise competitors
They were comparing themselves to the wrong peer set

Day 5: Encode the Intelligence

We turned their pricing logic into repeatable rules:

IF customer has >500 employees AND >3 departments using product
THEN position against enterprise competitors
ELSE position against SMB competitors

The insight: 40% of customers were enterprise but paying SMB prices.

Week 2: Finding the Money

Day 6-7: Usage Analysis

The engine analyzed 18 months of usage data:

Which features enterprise customers actually used
How usage correlated with company size
Where power users concentrated

Discovery: Enterprise customers used 3x more API calls but paid the same price.

Day 8-9: Win/Loss Patterns

We encoded their win/loss review process:

Automated analysis of 200+ closed/lost deals
Pattern detection on pricing objections
Correlation with deal size and segment

Revelation: They lost SMB deals on price but NEVER lost enterprise deals on price.

Day 10: The $3M Opportunity

The engine identified three specific opportunities:

Opportunity 1: Enterprise Repricing ($1.8M)

12 enterprise accounts underpriced by average of $150K/year
These customers had budget allocated but were never asked for it
Implementation: Grandfather current terms, new pricing at renewal

Opportunity 2: Usage-Based Expansion ($800K)

API tier pricing would generate $800K from existing usage
No behavior change required, just proper monetization
Implementation: 90-day notice, then automatic

Opportunity 3: Feature Unbundling ($400K)

5 premium features buried in base package
30% of customers would pay extra for just 2 of them
Implementation: Create "Professional" tier

The Implementation Playbook

Phase 1: Quick Wins (Month 1)

Implement API usage tiers
Stop discounting to enterprise
Raise prices for new enterprise logos

Result: $50K additional MRR in 30 days

Phase 2: Systematic Changes (Month 2-3)

Roll out new enterprise pricing
Launch Professional tier
Build pricing confidence in sales team

Result: $150K additional MRR by month 3

Phase 3: Full Transformation (Month 4-6)

Reprice renewals strategically
Implement value-based pricing model
Continuous competitive monitoring

Result: $250K additional MRR run rate

Why This Worked (And Why Manual Analysis Didn't)

1. Complete Data, Not Samples

Manual approach: Analyze 10 deals, extrapolate
Engine approach: Analyze all 500+ deals, find actual patterns

2. Continuous, Not Periodic

Manual approach: Quarterly pricing review
Engine approach: Weekly competitive updates, daily usage analysis

3. Systematic, Not Sporadic

Manual approach: Different analysis each time
Engine approach: Same methodology, improving weekly

The Uncomfortable Truth

That $3M wasn't hidden. It was invisible.

Without systematic analysis, you can't see:

How you're priced relative to true competitors
Which customers would happily pay more
Where you're leaving money on the table

The data was always there. The insights were always available. But without an engine to surface them systematically, they stayed buried.

Your Hidden Millions

Every B2B SaaS company has pricing opportunities hiding in plain sight:

Signs you're sitting on hidden value:

You check competitor pricing quarterly (or less)
Your pricing is based on "feel" not data
You have one price for widely different customer segments
You've raised prices <10% in the last 2 years
You discount >20% on average

Quick assessment:

What % of customers are using >3x median usage? (If >20%, you have a tier problem)
What % of enterprise deals did you lose on price? (If <10%, you're underpriced)
How many pricing changes in last 12 months? (If <3, you're not experimenting enough)

Build Your Pricing Intelligence Engine

Week 1: Encode Your Methodology

Map current pricing logic
Build competitor tracking
Define customer segments

Week 2: Find the Opportunities

Analyze usage patterns
Review win/loss data
Identify quick wins

Week 3+: Capture the Value

Implement changes systematically
Monitor impact
Refine continuously

The ROI Is Immediate

Cost of building pricing engine: ~$50K
Value found in first analysis: $3M
Time to positive ROI: 2 weeks

This isn't complex. It's not AI magic. It's systematic analysis of data you already have, using logic you already know, to find money you're already leaving on the table.

The Competition Is Already Moving

While you're debating whether to analyze pricing, your competitors are:

Testing new models weekly
Tracking your every price change
Optimizing based on real data

Every month you wait is money left on the table and competitive advantage lost.

Start This Week

Pick your highest-value product
List your enterprise customers
Check what they're actually paying
Compare to your enterprise competitors
Find your first $1M

It's there. You just need to look systematically.

Ready to find the millions hidden in your pricing?

Build a pricing intelligence engine in 2 weeks. Find opportunities immediately. Capture value systematically.

15Rock helps B2B companies build pricing intelligence engines. We've found $50M+ in pricing opportunities across our portfolio. Your millions are waiting.