The gap between where you think you win and where you actually win is costing you millions.
Here's what changes when you let the data speak.
The Confidence Problem Every CRO Faces
Ask any revenue leader where their company wins, and they'll answer without hesitation. They'll rattle off their core industries, the sweet spot headcount range, and the deal stories that validate their strategy. They've built their entire go-to-market motion around these assumptions.
Then they built their first real propensity model.
What happens next is the same pattern we see with every client: the market looks completely different when you strip away intuition and examine actual buying behavior. Not theory. Not what worked three years ago. Not what the board presentation says. Real conversion data, properly weighted, revealing where your revenue engine naturally thrives.
The discoveries are immediate—and they fundamentally change how you approach pipeline generation.
Discovery #1: Your Best Buyers Aren't Where You Think They Are
The accounts that look perfect on paper convert at far lower rates than you'd expect.
Your "core ICP," the segment everyone references in planning meetings, the category your positioning was built around, often underperforms significantly. Meanwhile, there's almost always a cluster of accounts that nobody's been systematically targeting. Yet these overlooked segments consistently move faster through your sales cycle, close at higher rates, and expand more predictably once they're customers.
This isn't random luck. These high-performing segments share underlying conditions that surface-level firmographics never captured. Your CRM categorized them wrong. Your marketing ignored them. Your sales team stumbled into them accidentally—and when they did, the deals felt easier.
A propensity model built on real conversion data makes these segments impossible to miss.
The result: Most companies discover a 20-30% more addressable market than they thought they had, and realize they've been over-investing in segments that will never convert efficiently.
Discovery #2: Most of Your Signals Don't Matter (And the Ones That Do Will Surprise You)
Revenue teams love complexity. They'll layer in dozens of data points: tech stack, compliance requirements, hiring velocity, org structure, budget cycles, competitor displacement patterns.
Then the model runs and converges on four or five signals that actually drive conversion probability.
The signals that matter are rarely the ones executives expected. They sit deeper in a company's operating structure. They're behavioral, not demographic. You wouldn't discover them through sales conversations or customer interviews. You find them by analyzing what buyers with the highest propensity to convert have in common, regardless of what they told you in discovery calls.
The practical impact: You stop wasting cycles chasing false signals. Your SDRs focus on accounts that actually match buying patterns. Your marketing targets segments where the signals align. Conversion rates improve because you're finally prioritizing the right indicators.
Discovery #3: Your CRM Is Lying to You (And It's Worse Than You Think)
This is the part that surprises leadership most: their data is fundamentally broken.
When we audit CRM data for propensity modeling, we consistently find:
- Duplicate records treating the same company as multiple accounts
- Misclassified industries that distort segment performance
- Broken parent-child hierarchies hiding actual account relationships
- Regional mismatches that throw off territory analysis
- Missing or incorrect outcome data that makes true conversion invisible
- Attribution errors that credit the wrong campaigns, channels, or reps
I've worked on datasets where more than 60% of records couldn't be used until we rebuilt them from scratch.
If you try to model corrupted data, the output reflects the distortion rather than reality. Your model will confidently tell you to chase the wrong accounts, invest in the wrong segments, and ignore your highest-probability opportunities.
The uncomfortable truth: You can't build a reliable propensity model until you fix your data foundation. Most companies have never done this properly. Their entire understanding of "where we win" is built on structurally flawed information.
Discovery #4: Performance Is More Concentrated Than Your Strategy Assumes
Most GTM strategies spread resources evenly across a broad set of accounts. Equal air cover. Equal SDR capacity. Equal marketing spend.
Propensity models reveal how misguided this approach is.
Conversion probability isn't evenly distributed. It's highly concentrated. A relatively small segment of your addressable market carries the majority of your near-term revenue potential. These aren't just "high-fit" accounts. They're accounts where multiple behavioral signals align, creating a materially higher likelihood of movement in the next 90-180 days.
The model shows you exactly where this concentration sits: which segments, which account clusters, which behavioral patterns correlate with faster cycles and higher close rates.
What changes: You stop treating all qualified accounts the same. You concentrate effort where the probability is highest. You stop burning pipeline capacity on accounts that look good but won't convert this year. Efficiency compounds quickly.
Discovery #5: Your Hidden Revenue Pockets Have Been Invisible
The most valuable discovery for every company we work with is the same: high-propensity segments that were never part of their go-to-market strategy.
These aren't edge cases. They're often 15-25% of your addressable market. They convert faster. They expand more predictably. They require less discounting and fewer executive escalations to close.
Yet they've been completely invisible because your segmentation was anchored in firmographics, not behavior. Your ICP definition excluded them. Your marketing never targeted them. Your sales team never built a motion around them.
When these segments surface in the model, they become the fastest path to pipeline acceleration and revenue growth. They're not theoretical opportunities. They're accounts where the signals already indicate high propensity. You just weren't looking.
The unlock: You build deliberate motions around segments with proven conversion patterns instead of betting on intuition about what "should" work.
Discovery #6: Forecasting Becomes Precision, Not Performance Art
Most revenue forecasts are built on:
- Sales rep intuition about deal likelihood
- Historical averages that ignore changing market conditions
- Stage-based probability that treats all accounts in a stage equally
- Executive adjustments based on "feel"
A propensity model changes forecasting fundamentally.
Once you know where real conversion probability sits (which accounts carry genuine near-term signals, which segments are actually moving), you can forecast with a precision most revenue teams have never experienced.
You're not guessing which accounts will close based on stage or rep optimism. You're weighting the pipeline based on the same signals that have historically predicted conversion. The model tells you where to expect movement, where deals will stall, and where you're over-projecting based on weak signals.
The shift: CROs gain confidence in their forecast accuracy. Boards get realistic expectations. GTM teams align around a shared, data-grounded view of what's actually closeable.
Discovery #7: The Model Becomes Your Operating System
The biggest shift isn't analytical. It's operational.
A strong propensity model doesn't stay as a one-time deliverable. It becomes the foundation for how revenue teams make daily decisions:
- Sequencing: SDRs prioritize high-propensity accounts instead of working alphabetically through a list
- Messaging: Marketing crafts segment-specific content based on the conditions that drive conversion, not generic pain points
- Territory design: RevOps assigns accounts based on propensity concentration, not just geography or company size
- Product strategy: Product teams see which segments expand fastest and build for retention, not just acquisition
- Compensation: Finance aligns incentives with accounts that actually have near-term probability
The model stops being a research project. It becomes the lens through which the entire revenue organization views the market.
What this looks like in practice: Teams move faster, waste less effort, and generate more pipeline from the same capacity because every decision is grounded in actual buying behavior.
What Most Companies Get Wrong About Propensity Modeling
Revenue leaders often assume building a propensity model requires:
- A massive dataset spanning years of history
- Expensive data science resources
- Complex machine learning infrastructure
- Months of iteration to get useful output
None of that is true.
What you actually need:
- Clean data: Your CRM records need to be deduplicated, properly structured, and accurately tagged
- The right signals: Behavioral and operational indicators that actually correlate with conversion—not vanity metrics
- Proper weighting: A methodology that separates correlation from causation and surfaces genuine predictive patterns
- An action framework: A process that translates model output into prioritized account lists, segment strategies, and measurable GTM changes
When those four elements come together, you get a validated propensity model that immediately changes how your revenue team operates.
We build them in two weeks, not because we cut corners, but because we've done this enough times to know exactly which signals matter and how to extract them from your existing data.
The Real Question Isn't Whether You Need a Model
Every B2B company with more than 500 target accounts needs a propensity model. The market is too large, the signals too complex, and the cost of misallocated effort too high to rely on intuition.
The real question is: How much longer are you willing to operate without knowing where you actually win?
Every quarter you wait, you're:
- Chasing accounts with low conversion probability
- Ignoring segments where you'd naturally thrive
- Spreading capacity across the wrong priorities
- Missing pipeline you should be generating
- Forecasting based on hope instead of signal
The companies that build propensity models early don't just gain efficiency. They gain a structural advantage over competitors still guessing.
Stop Guessing. Start Knowing Where You Win.
At The Pipeline Group, we've built this into a science. Our team has developed over 100 propensity models and crafted more than 200 ICPs for B2B companies. We know what works because we've seen the patterns across hundreds of revenue engines.
Here's what makes our approach different:
28 Integrated Data Sources
We don't rely on your CRM alone. Our platform synthesizes signals from 28 external data sources, giving you a complete view of account behavior, technographic changes, hiring patterns, funding events, and buying signals your team can't see in Salesforce.
Purpose-Built Platform for Market Signal Detection
We've built proprietary technology that identifies the behavioral patterns and market signals that actually predict conversion. This isn't a generic analytics tool. It's purpose-built for propensity modeling and ICP refinement, designed specifically for B2B revenue leaders who need precision, not guesswork.
Two-Week Delivery. Complete Validation. Ready to Execute.
Most consulting firms take months to deliver a propensity model. We deliver a complete, validated, actionable model in two weeks. You get:
- High-propensity segments with proven conversion patterns and clear behavioral signals
- Account-level propensity scores across your entire addressable market
- Signal analysis showing exactly what drives buying behavior in your best customers
- GTM playbook translating model insights into prioritized account lists, messaging frameworks, and territory strategies
- Platform access to monitor ongoing signal changes and track propensity shifts in real time
This isn't a research project. It's an operating system for your entire revenue team.
What CROs Tell Us After Two Weeks
"We've been chasing the wrong 40% of our market for two years. The model showed us segments we'd completely ignored that convert 3x faster." — CRO, Series B Sales Enablement Platform
"Our forecast accuracy went from 62% to 91% in one quarter. We finally know which deals are real." — CMO, Enterprise Data Infrastructure Company
"The propensity scores changed how our SDRs prioritize accounts. Our meeting-to-opportunity conversion rate jumped 34% in 60 days." — Operating Partner, Growth Equity Fund
Ready to See Where Your Revenue Engine Actually Wins?
If you're a CRO, CMO, or Operating Partner tired of guessing which accounts matter most, let's build your propensity model.
Two weeks. 28 data sources. One clear answer: where you win.
Schedule Your Propensity Model Build →
Or email us directly at marko@thepipelinegroup.io
Marko Marais is the Chief Product Officer at The Pipeline Group, Inc, where he leads the development of propensity modeling methodologies and ICP frameworks for B2B revenue teams. Marko has personally built over 200 ICPs and led his team in developing more than 100 propensity models for companies ranging from early-stage startups to publicly traded enterprises. His work focuses on one principle: revenue teams perform better when they know exactly where they win, and they stop wasting effort on accounts that will never convert.
Read Marko's previous article: The 5 ICP Mistakes Costing You 20% of Your Pipeline