I'd just presented our competitive strategy to the executive team.
Six weeks of research: competitor analysis, market positioning maps, feature comparisons, and sales interviews. The strategy was clear:
- Competitor A: Incumbents with legacy tech—position against them on modern architecture
- Competitor B: New entrant with limited features—position on comprehensive platform
- Competitor C: Enterprise-focused vendor—position on faster implementation and lower TCO
The CRO listened patiently. Then he asked, "What's our current win rate against each competitor?"
I didn't know. I'd built competitive strategy based on what competitors offered and how we were different, not on whether that differentiation actually won deals.
"I'll get that data," I said.
After the meeting, I asked the VP of Revenue Operations for competitive win rates. He sent me a dashboard I'd never seen.
Win rate by competitor (trailing 12 months):
- vs. Competitor A: 68%
- vs. Competitor B: 31%
- vs. Competitor C: 52%
My competitive strategy was exactly backwards.
I'd been investing the most effort building positioning against Competitor A, where we were already winning 68% of deals. I'd been treating Competitor B as a minor threat, when we were losing 69% of deals against them.
I'd built strategy based on market perception, not revenue reality.
RevOps showed me 18 months of competitive deal data segmented by competitor, deal size, segment, and outcome. The patterns destroyed every assumption my competitive strategy had made.
What Win Rate Data Revealed About Competitive Position
I spent three days analyzing RevOps's competitive deal data with filters I'd never considered: win rate trends over time, deal size by competitor, segment variations, and discount patterns.
Finding #1: Win Rate by Competitor Showed Where to Focus
What I'd assumed:
- Competitor A was our primary threat (market leader, most deals against them)
- Competitor B was a minor player (smaller brand, fewer deals)
- Competitor C was growing threat (aggressive sales, good product)
What win rate data showed:
Competitor A (41% of competitive deals):
- Win rate: 68%
- Average deal size when we win: $480K
- Average discount when we win: 8%
We were already crushing Competitor A. Yes, we faced them most often, but we had a strong competitive position. More battle cards and positioning wouldn't meaningfully improve 68% win rate.
Competitor B (18% of competitive deals):
- Win rate: 31%
- Average deal size when we win: $620K
- Average discount when we win: 24%
We were losing badly to Competitor B. And when we did win, we had to discount heavily. This was our actual competitive threat, but I'd been treating them as minor because they had smaller market share.
Competitor C (27% of competitive deals):
- Win rate: 52%
- Average deal size when we win: $410K
- Average discount when we win: 12%
Roughly even matchup. Competitive positioning could swing this, but it wasn't the disaster I'd assumed.
The uncomfortable truth: I'd been building competitive strategy based on market share and brand prominence, not win rate data. I was solving the wrong competitive problem.
Strategy shift: Reallocate 70% of competitive effort to Competitor B (where we're losing), maintain light positioning against Competitor A (already winning), and moderate investment in Competitor C (swingable deals).
Finding #2: Win Rate Trends Over Time Revealed Market Shifts
RevOps didn't just show me point-in-time win rates. They showed me trends over 18 months.
vs. Competitor A:
- 18 months ago: 62% win rate
- 12 months ago: 65% win rate
- 6 months ago: 67% win rate
- Current: 68% win rate
Steady improvement. Our positioning against Competitor A was working—we'd been gaining share steadily.
vs. Competitor B:
- 18 months ago: 48% win rate
- 12 months ago: 42% win rate
- 6 months ago: 35% win rate
- Current: 31% win rate
Catastrophic decline. Competitor B had been roughly even 18 months ago. Now we were losing 7 out of 10 deals.
Something had changed dramatically, and I'd missed it because I wasn't tracking win rate trends.
I pulled Competitor B's product releases, pricing changes, and marketing campaigns over the same 18-month period.
12 months ago: Competitor B launched Feature X (a capability we didn't have)
8 months ago: Competitor B hired an aggressive VP Sales who restructured their sales team
6 months ago: Competitor B launched "migration made easy" campaign targeting our customers
Every lost deal against Competitor B in the past 6 months mentioned one of these three factors. Feature X was the most common (mentioned in 61% of losses).
The pattern: Competitor B had systematically addressed their weaknesses. I'd been positioning against their 18-month-old product, not their current product.
Strategy shift: Stop positioning against old Competitor B weaknesses (they've fixed them). Build new positioning around areas where we still have advantages, and work with product to prioritize Feature X since we're losing $4M+ in annual pipeline without it.
Finding #3: Deal Size Comparison Revealed Where We Were Underpriced or Over-Positioned
RevOps segmented competitive deals by outcome (win vs. loss) and compared deal sizes.
vs. Competitor A:
- Deals we won: $480K average
- Deals we lost: $520K average
We tended to win smaller deals, lose larger deals against Competitor A. This suggested Competitor A had stronger enterprise positioning or features that large buyers valued.
vs. Competitor B:
- Deals we won: $620K average
- Deals we lost: $340K average
Opposite pattern. We won the big deals, lost the small deals against Competitor B.
This was strange. Conventional wisdom says you lose large deals (more complexity, more stakeholders) and win small deals (simpler, faster).
I interviewed sales on recent Competitor B wins and losses:
Why we won large deals: Large enterprises valued our platform's scalability and integration capabilities. Competitor B was positioned as a "lightweight" solution—great for small teams, but concerns about enterprise readiness.
Why we lost small deals: Small companies saw us as "too complex" and "overkill for their needs." Competitor B's simplicity was an advantage for them, not a weakness.
The insight: We'd been positioning our platform comprehensiveness as universally valuable. Revenue data showed it was valuable for large buyers, but a liability for small buyers.
Strategy shift: Segment competitive positioning by deal size.
- Large deals (>$400K): Emphasize platform comprehensiveness and scalability (advantage vs. Competitor B)
- Small deals (<$200K): Reposition comprehensiveness as "flexible"—you can use what you need, ignore the rest (neutralize Competitor B's "too complex" objection)
Result: Win rate vs. Competitor B in <$200K deals improved from 23% to 39% over two quarters.
The Sales Cycle Analysis That Changed Positioning
RevOps showed me another competitive metric I'd never considered: sales cycle length by competitor.
Average sales cycle:
- vs. Competitor A: 67 days
- vs. Competitor B: 94 days
- vs. Competitor C: 58 days
Competitor B deals took 40% longer to close than Competitor A deals. Why?
I analyzed stage-by-stage progression for Competitor B deals:
Discovery → Demo: 18 days (vs. 12-day average for all deals)
Demo → Proposal: 41 days (vs. 22-day average)
Proposal → Close: 35 days (vs. 24-day average)
Deals were stalling at every stage when Competitor B was involved, but the biggest gap was Demo → Proposal.
I pulled sales call recordings from stalled Competitor B deals.
The pattern: Prospects were getting confused during demos because we were trying to show comprehensive platform capabilities while Competitor B was showing focused, simple workflows.
Our demos were overwhelming. Competitor B's demos were clear.
After demos, prospects went silent for weeks—they needed time to process complexity. Then they'd come back with more questions. Multiple demo cycles. Long evaluation. Eventually many chose Competitor B's simplicity over our power.
The positioning failure: We thought "comprehensive platform" was our competitive advantage. Sales cycle data showed it was creating evaluation paralysis.
Strategy shift: Rebuild demo narrative to show one specific workflow deeply (match Competitor B's simplicity in first demo), then expand to broader platform capabilities in second demo only if prospect expresses interest in additional use cases.
Average sales cycle vs. Competitor B dropped from 94 days to 76 days. Win rate improved from 31% to 38%.
Feature Gap Analysis from Lost Deal Reasons
RevOps tracked loss reasons in Salesforce for every competitive loss. I'd never analyzed this data systematically.
I pulled lost deal reasons for the past 12 months and coded them into categories:
Losses to Competitor B (173 total):
- Feature gap: 72 deals (42%) - we lacked features they had
- Pricing: 34 deals (20%) - they were cheaper
- Incumbent advantage: 28 deals (16%) - they were already using Competitor B
- Sales execution: 21 deals (12%) - we mishandled sales process
- Other: 18 deals (10%)
42% of losses cited feature gaps. When I broke those down further:
Feature gaps mentioned:
- Feature X: 44 deals (61% of all feature gap losses)
- Feature Y: 18 deals (25%)
- Feature Z: 10 deals (14%)
Feature X came up in 44 deals worth a combined $14.8M in lost pipeline.
I'd been building competitive positioning assuming feature parity. Revenue data showed we had a systematic product gap that no amount of positioning could overcome.
Cross-functional decision with RevOps:
I took this analysis to product with RevOps's support. "We're losing $14.8M in annual pipeline to Competitor B specifically because we don't have Feature X. Our win rate against them has dropped from 48% to 31% since they launched it. This isn't a nice-to-have feature request—it's a competitive revenue threat."
Product prioritized Feature X. Shipped it 4 months later.
Win rate vs. Competitor B in deals where Feature X was mentioned improved from 18% (before we had it) to 52% (after we had it).
I wouldn't have had the business case to push product without RevOps's lost deal data and pipeline calculations.
Building the Ongoing Competitive Data Review
After discovering how valuable RevOps's competitive data was, we built a quarterly competitive review process.
Every quarter, RevOps prepares:
Win rate by competitor:
- Current quarter vs. trailing 12-month average
- Trend over past 6 quarters
- Segmented by deal size, vertical, and region
Sales cycle by competitor:
- Average days to close
- Stage-by-stage breakdown
- Identification of stages where deals stall
Deal size comparison:
- Average deal size in wins vs. losses
- Discount rate in wins vs. losses
- Segment where we over/under-index
Loss reason analysis:
- Coded loss reasons by frequency
- Feature gaps mentioned
- Pricing pressure frequency
PMM brings to the review:
Competitive intelligence updates:
- Product releases, pricing changes, messaging shifts
- New competitors entering market
- Market share movement (if available)
Win/loss interview insights:
- Qualitative themes from interviews
- Competitive positioning that's working/not working
- Objections we're successfully handling vs. struggling with
Positioning changes planned:
- Messaging updates
- New battle cards or competitive content
- Sales enablement rollouts
The outcome: Joint decisions on where to invest competitive effort.
Example from Q3 review:
Win rate vs. Competitor C had dropped from 52% to 46%. RevOps data showed the decline started 4 months ago.
PMM competitive intel: Competitor C had hired a new CMO 5 months ago who'd completely rebuilt their positioning. New messaging emphasized a use case that overlapped heavily with ours.
Sales cycle data: Deals vs. Competitor C were taking longer (58 days → 71 days) because prospects were doing more extensive comparisons.
Loss reason data: "Better understood their unique approach" appeared in 12 of the past 18 losses to Competitor C.
Joint diagnosis: Competitor C's new positioning was creating clearer differentiation in prospects' minds. Our positioning wasn't responding to their new narrative.
PMM decision: Build new competitive positioning specifically countering Competitor C's narrative. Update battle cards, train sales, create competitive comparison content.
RevOps commitment: Track whether sales cycle and win rate vs. Competitor C improve after positioning rollout.
Result: Win rate vs. Competitor C recovered to 51% within one quarter after positioning update.
The Uncomfortable Reality Data Revealed
The quarterly competitive data review exposed truths I'd been avoiding:
Truth #1: Some competitive battles aren't winnable with better positioning
I'd been trying to improve our win rate against Competitor B through better battle cards and competitive positioning.
Revenue data showed: In deals where Competitor B's Feature X was mentioned, our win rate was 18% regardless of whether sales used battle cards or not.
This wasn't a positioning problem. It was a product problem. No amount of messaging could overcome a legitimate feature gap.
PMM decision: Stop trying to out-position Competitor B on Feature X. Either build it (product decision) or disqualify deals where it's a requirement (sales ops decision).
Truth #2: We were wasting effort on competitive threats that didn't matter
I'd been tracking 8 competitors and building materials for all of them.
RevOps data showed:
- 3 competitors accounted for 86% of competitive deals
- 5 competitors accounted for 14% of competitive deals combined
I was spending 60% of my time on content for the 5 minor competitors.
PMM decision: Sunset competitive materials for minor competitors. Focus all competitive effort on top 3 where it could move revenue.
Truth #3: Our strongest competitive position was accidental
Win rate vs. Competitor A was 68%—our strongest competitive matchup.
But when I looked at why we won, I realized we'd never built specific positioning against them. Sales had organically developed effective talking points based on customer feedback and deal experience.
The competitive positioning that worked best wasn't PMM-created. It had emerged from frontline sales experience.
PMM decision: Document what's already working (sales's organic positioning against Competitor A), codify it into battle cards, and scale it—rather than building new positioning from scratch.
What I'd Tell PMMs About Data-Driven Competitive Strategy
If you're building competitive strategy without RevOps data, you're guessing.
Here's what to ask for:
Win rate by competitor over time. Not just current win rate—trends. Is your position strengthening or weakening? Which competitors are taking share?
Deal size and sales cycle by competitor. Which competitors do you beat in large deals vs. small deals? Which slow down your sales process?
Loss reasons coded by competitor. What do you actually lose on? Product gaps? Pricing? Sales execution?
Discount rate by competitor. Where are you discounting heavily to win? That reveals weak differentiation.
Revenue data shows you where you're winning, where you're losing, and why—based on actual deal outcomes, not market perception.
Market research tells you what competitors offer. Win rate data tells you whether your positioning against them actually works.
Build competitive strategy based on what wins, not what sounds differentiated.