The pricing operations manager sent me a calendar invite: "Urgent: Need to discuss discount escalation problem."
I'd never spoken to the pricing ops team. PMM owned positioning and messaging. Pricing ops managed approvals and contract workflows. Our work didn't overlap.
I showed up to the meeting out of curiosity.
She pulled up a report I'd never seen: Every discount request over 20% in the past quarter, segmented by reason sales gave for the discount.
"Look at this," she said. "42% of high-discount deals cite 'competitive pricing pressure.' Another 28% cite 'customer needs to prove ROI first.' These aren't pricing problems—these are positioning problems."
She walked through examples:
Deal after deal where sales requested 25-30% discounts because "Competitor X is cheaper." When she asked sales if our product delivered more value than the competitor, they couldn't articulate it clearly enough to justify the price premium.
Deal after deal where sales requested phased pricing or heavy discounts because "customer needs to see value before committing to full price." Our value proposition wasn't creating enough confidence in fast time-to-value.
"You own positioning," she said. "These discount patterns are telling you exactly where your positioning is failing. But you'd never know it because PMM and pricing ops don't talk."
She was right.
I'd been building positioning in a vacuum—customer interviews, competitive research, sales feedback. But I'd never looked at discount data.
Discount patterns revealed where positioning broke down under real revenue pressure. They showed which value props sales actually believed in (they charged full price) and which they didn't (they discounted to close).
That meeting started a partnership between PMM and pricing ops that became one of the most valuable feedback loops in our company.
What Discount Patterns Reveal About Positioning
After that first meeting, the pricing ops manager and I started a monthly review of discount data segmented by reason, competitor, and segment.
The patterns were brutal—and revealing.
Pattern #1: Discount Policy = Positioning Confidence Signal
I'd always thought of discounts as a sales execution issue. Some reps negotiated well, others caved too easily.
Pricing ops showed me: Discount patterns are remarkably consistent by segment and competitor. It wasn't rep variability—it was positioning strength.
Deals against Competitor A:
- Average discount: 8%
- Discount frequency: 22% of deals
Deals against Competitor B:
- Average discount: 23%
- Discount frequency: 67% of deals
Sales charged full price against Competitor A because they believed our value prop was strong. They discounted against Competitor B because they didn't.
I'd built competitive battle cards for both competitors. But only the Competitor A battle card gave sales genuine confidence in our differentiation.
PMM insight: Discount patterns showed me which battle cards actually worked, not just which ones sales read.
Pattern #2: Pricing Tier Removal = ICP Misalignment
Pricing ops tracked deals where sales removed features from standard packages to get to lower price points.
Example: Our Enterprise tier included Features A, B, C, D, and E for $X price.
43% of enterprise deals in the past quarter negotiated removal of Features D and E to reduce price by 20-25%.
I'd assumed Features D and E were valuable to enterprise customers. That's why we'd bundled them in the Enterprise tier.
But enterprises kept asking to remove them.
When I interviewed customers who'd removed those features, the pattern was clear: Features D and E solved problems that only enterprises in specific industries had (compliance in healthcare, audit requirements in financial services).
Enterprises outside those industries saw them as bloatware, not value.
Our tier packaging assumed "all enterprise customers need these features." Revenue data showed that wasn't true.
PMM decision: Unbundle Features D and E into industry-specific add-ons instead of forcing them on all enterprise customers. Let healthcare enterprises buy the compliance pack, let financial services enterprises buy the audit pack, let everyone else skip both.
Result: Enterprise close rates improved 18% because we stopped forcing unwanted features on prospects. Average deal size stayed flat because prospects who needed those features bought the add-ons.
Pattern #3: Packaging Complexity = Deal Desk Load
Pricing ops measured how many deals required custom package configuration vs. standard tier purchases.
Their data:
- 61% of deals purchased standard tiers without modification
- 39% of deals required custom packaging (features from multiple tiers, feature removals, or custom bundles)
39% custom deals meant 39% of deals needed pricing ops review, legal review, and approval cycles—adding 2-3 weeks to every deal.
Why were so many deals requiring customization?
Pricing ops showed me patterns:
Custom Bundle #1: SMB customers wanting Professional tier + one Enterprise feature (SSO).
This happened 47 times in six months. Nearly once per week, sales negotiated the same custom bundle.
If prospects wanted this combination that often, it shouldn't be a custom bundle—it should be a standard offering.
Custom Bundle #2: Mid-market customers wanting Basic tier features + specific integrations that were only available in Professional tier.
This happened 31 times in six months.
Again, if this happened 30+ times, it shouldn't require custom packaging every time.
PMM decision: Create two new standard packages based on these recurring custom bundles:
- "Professional + SSO" tier (formalized the most common SMB custom bundle)
- "Basic + Integrations" tier (formalized the most common mid-market custom bundle)
Result: Custom deal frequency dropped from 39% to 22%. Sales cycles shortened because prospects could buy standard packages instead of waiting for custom pricing approval.
The Weekly Pricing Ops ↔ PMM Sync
After discovering how valuable discount and packaging data was, pricing ops and I built a standing weekly 30-minute sync.
Here's what we review:
First 10 Minutes: High-Discount Deal Review
Pricing ops shares every deal from the past week where discount exceeded 20%.
For each one, we look at:
- Discount reason: Competitive pressure? Budget constraints? ROI uncertainty?
- Competitor (if any): Which competitor forced the discount?
- Segment: Is this discount pattern isolated or systematic in this segment?
What this catches:
Week of August 12: Three deals against Competitor C all requested 25-30% discounts citing "competitor pricing."
I checked Competitor C's pricing—they hadn't changed it. But they'd just launched a new feature that addressed a gap we had.
Sales was discounting to compensate for the feature gap, not competitive pricing.
PMM decision: Work with product to prioritize that feature. In the meantime, update battle cards to position around alternative approaches that didn't require that specific feature.
Without the weekly discount review, I wouldn't have caught the pattern for weeks.
Next 10 Minutes: Custom Package Requests
Pricing ops shares deals that requested custom packaging or feature modifications.
We look for patterns:
- Same custom request appearing multiple times (should this be a standard offering?)
- Features being removed frequently (are we bundling wrong?)
- Features being added from higher tiers (should pricing be adjusted?)
What this catches:
Week of September 3: Two deals in manufacturing vertical asked for the same custom bundle—our Basic tier + premium analytics.
Two deals isn't a huge pattern, but it was interesting because we'd never seen manufacturing prospects care about analytics before.
I interviewed both prospects. They were using our product differently than other segments—they needed production line analytics, which our premium analytics package happened to support even though we'd never positioned it that way.
PMM decision: Build manufacturing-specific positioning emphasizing production analytics use case. Create manufacturing demo environment showing that workflow.
Within two months, manufacturing became our third-largest vertical. We'd stumbled into product-market fit without realizing it, and only caught it because pricing ops flagged the custom package pattern.
Last 10 Minutes: Upcoming Pricing/Packaging Changes
PMM previews upcoming positioning changes, launches, or new messaging that might affect pricing.
Pricing ops flags upcoming pricing changes, tier restructures, or policy updates that PMM needs to enable sales on.
What this prevents:
I was planning to launch new messaging emphasizing enterprise scalability. Pricing ops told me they were about to eliminate our lowest tier to focus upmarket.
If I'd launched enterprise messaging while the lowest tier still existed, I'd have confused the market. If pricing ops had eliminated the tier without PMM updating messaging, sales would've had inconsistent positioning.
Coordination prevented both problems.
What Pricing Data Revealed About Our ICP
The most uncomfortable insight from working with pricing ops: Our stated ICP didn't match our revenue reality.
Stated ICP (what PMM had been messaging to):
- Enterprise companies (2,000+ employees)
- Financial services and healthcare verticals
- $500K+ deal sizes
Actual high-performing segment (what pricing data revealed):
- Mid-market companies (250-1,000 employees)
- Healthcare vertical specifically (financial services had high discount rates and long sales cycles)
- $300-600K deal sizes
The data that proved it:
Pricing ops segmented deals by company size and vertical, then calculated:
- Average discount rate
- Deal cycle length
- Custom packaging frequency
- 12-month retention
Enterprise (2,000+ employees):
- Average discount: 24%
- Deal cycle: 127 days
- Custom packaging: 68% of deals
- 12-month retention: 76%
Mid-market (250-1,000 employees):
- Average discount: 11%
- Deal cycle: 61 days
- Custom packaging: 28% of deals
- 12-month retention: 89%
Enterprise looked like our ICP on paper (bigger logos, bigger deal sizes when they close). But mid-market was dramatically better on every revenue efficiency metric.
Mid-market deals closed faster, at higher gross margin (less discounting), with less operational complexity (fewer custom packages), and better retention.
We were positioning for enterprise because it felt more strategic. But mid-market was where we actually won.
PMM decision: Reposition as "built for mid-market scale with enterprise security." Stop trying to compete for 5,000+ employee enterprises where our product wasn't differentiated. Double down on 250-1,000 employee companies where we had clear advantages.
This felt like narrowing our market. It was. But pricing data showed we weren't winning outside mid-market anyway—we were just burning sales cycles.
The Uncomfortable Truth About Packaging
Working with pricing ops revealed something I didn't want to admit: Our packaging tiers weren't based on customer needs. They were based on what product had built.
How we'd created our three-tier packaging:
Product had shipped Features A-E. PMM had created three tiers:
- Basic: Features A, B
- Professional: Features A, B, C, D
- Enterprise: Features A, B, C, D, E
The logic: Basic for small customers, Professional for mid-market, Enterprise for large companies.
Pricing ops showed me actual buying patterns:
What customers actually bought:
- SMB: Basic + Feature C only (they didn't want Feature D)
- Mid-market: Professional without Feature D (28% of deals removed it)
- Enterprise: Enterprise without Features D and E (43% of deals removed them)
We'd bundled features into tiers based on "what kind of customer uses this," but customers bought based on "which specific features do I need."
Feature D was valuable to a specific use case, not a specific company size. We'd bundled it in Professional tier assuming all mid-market companies needed it. They didn't.
The hard conversation:
I brought this data to our VP of Product. "Our tier packaging doesn't match how customers actually buy. Should we consider use-case-based packaging instead of size-based tiers?"
He pushed back: "Tier-based packaging is standard in SaaS. Everyone does it this way."
I showed him the pricing ops data: "39% of deals require custom packaging because our tiers don't match customer needs. Every custom deal adds 2-3 weeks to the sales cycle and requires legal and pricing ops review. We're losing deals because our packaging is confusing."
We rebuilt packaging around use cases instead of company size:
- Core Platform (Features A, B)
-
- Analytics Add-on (Feature C)
-
- Compliance Add-on (Feature D)
-
- Enterprise Security Add-on (Feature E)
Customers could buy exactly what they needed without custom packaging.
Custom deal frequency dropped from 39% to 18%. Sales cycles shortened. Deal desk load decreased.
But I never would've pushed for that change without pricing ops data proving the packaging was broken.
What Integration Looks Like in Practice
The partnership between PMM and pricing ops isn't just data sharing. It's integrated decision-making.
Example 1: Competitive Pricing Changes
Competitor A lowered their pricing 20%.
Pricing ops caught it first (sales started requesting higher discounts). They flagged it to PMM.
PMM analyzed: Were we genuinely overpriced, or was Competitor A sacrificing margin in desperation?
Research showed Competitor A had just lost a major funding round. They were discounting to hit growth targets, not because their value prop justified the price cut.
PMM decision: Don't lower prices. Instead, build positioning around "sustainable business model vs. fire-sale pricing." Position their discount as a red flag for prospects.
Pricing ops decision: Approve 10% discounts in competitive deals against Competitor A (not the 25% sales was requesting), with messaging emphasizing our long-term viability.
Result: Win rate against Competitor A stayed high without destroying margin.
Example 2: Product Launch Pricing
PMM was launching a new product feature. Product suggested adding it to the Enterprise tier at no additional cost.
Pricing ops ran analysis: 78% of Enterprise customers would use this feature. 34% of Professional-tier customers would upgrade specifically for it.
If we added it to Enterprise for free, we'd give existing customers $X value for free and miss $Y upgrade revenue from Professional-tier prospects.
Pricing ops recommendation: Make it an add-on available to Professional and Enterprise tiers. Price it at $X based on perceived value from customer interviews PMM had conducted.
PMM decision: Position it as a premium add-on solving a specific use case, not a tier upgrade requirement.
Result: 41% of Professional-tier customers bought the add-on. We generated $2.4M in add-on revenue instead of giving away the feature for free.
I never would've considered add-on pricing without pricing ops input. PMM defaults to simplicity (just add features to tiers). Pricing ops thinks about revenue optimization.
Why This Partnership Matters
Most companies treat pricing operations as administrative (contract approvals, discount tracking) and product marketing as creative (messaging, positioning).
That's a mistake.
Pricing ops sees patterns PMM needs:
- Which positioning creates pricing confidence (low discount rates)
- Which segments have packaging misalignment (high custom deal frequency)
- Which features customers will pay for (upgrade and add-on data)
- Which competitors threaten margin (competitive discount patterns)
PMM has context pricing ops needs:
- Why customers value certain features (informs packaging decisions)
- How positioning affects perceived value (informs discount policy)
- Which segments have different needs (informs tier strategy)
- What competitive changes mean for pricing (discount vs. hold strategies)
Together, PMM and pricing ops can optimize for both message-market fit (PMM) and price-value fit (pricing ops).
Separately, PMM builds positioning that sounds good but leaves money on the table, and pricing ops approves discounts without understanding if the underlying problem is price or positioning.
How to Start This Partnership
If you're a PMM who's never worked with pricing ops, here's how to start:
Ask for one data cut: High-discount deals by reason.
Don't ask for a comprehensive analysis. Just ask to see deals where discount exceeded 20%, segmented by the reason sales gave.
Look for patterns. If 40% cite "competitive pressure," that's a positioning problem. If 30% cite "ROI uncertainty," that's a value prop problem.
Bring insights, not complaints.
Don't go to pricing ops saying "sales discounts too much." Go saying "I saw patterns in discount data that could inform better positioning. Can we discuss?"
Propose a monthly 30-minute sync.
Not weekly (too frequent to start). Monthly is enough to catch patterns without being burdensome.
Review high-discount deals, custom packaging requests, and tier migration trends.
Make it a two-way partnership.
Don't just take data from pricing ops. Offer them insights from customer research, competitive intelligence, and win/loss analysis that help them make better pricing decisions.
Pricing ops needs PMM's context as much as PMM needs their data.
The Uncomfortable Question
Working with pricing ops forced me to confront a question I'd been avoiding: How much revenue were we leaving on the table because PMM and pricing ops weren't aligned?
The answer, based on six months of joint analysis:
- Packaging misalignment: $2.8M in lost upgrades (customers who would've paid for add-ons if we'd offered them)
- Positioning-driven discounts: $1.9M in margin lost to unnecessary discounts (discounts given because sales didn't believe in our value prop)
- ICP misalignment: $3.2M wasted on sales cycles in segments where we had low win rates and high discount requirements
Total: $7.9M in annual revenue impact from misalignment between positioning strategy and pricing operations.
That's not a precise number—there are assumptions baked in—but it's directionally correct.
We weren't leaving money on the table because we had bad positioning or bad pricing. We were leaving money on the table because positioning and pricing weren't connected.
Once PMM and pricing ops started working together, we captured that revenue.
Discount rates dropped. Custom deal frequency decreased. Packaging aligned with customer needs. Positioning created pricing confidence.
The partnership between PMM and pricing ops isn't optional. It's how you turn good positioning into captured revenue.