Ask any RevOps or GTM leader what keeps them up at night and you’ll hear a familiar refrain:
“We’re drowning in data, but we don’t know which signals actually matter.”

That frustration is real. Traditional lead scoring offers some structure, but it’s often static, simplistic, and disconnected from reality. Propensity modeling changes that. Instead of manually assigning points to behaviors, it uses data-driven prediction to tell you who’s most likely to buy, churn, or expand.

In this post, we’ll unpack what propensity models are, why they matter for B2B SaaS growth, how they work, common challenges, and (most importantly) how to start building them into your CRM workflows.

What Exactly Is a Propensity Model? (And Why It's Different From Lead Scoring) 

At its core, a propensity model is a predictive engine. It estimates the likelihood that a lead, account, or customer will take a specific action—like signing a contract, upgrading their plan, or even churning.

Quote: "Definition: A propensity model is a predictive algorithm (statistical or machine learning) that estimates the likelihood (propensity) that an entity (lead, account, or customer) will take a specific future action (e.g. purchase, churn, upgrade)."

Where lead scoring is rule-based (“+10 points for attending a webinar”), propensity modeling is adaptive. It analyzes past outcomes, learns which signals truly matter, and adjusts accordingly.

Think of it this way: lead scoring is a checklist. Propensity modeling is a compass.

And in SaaS, this compass can point you toward:

• Leads most likely to close this quarter
• Customers at risk of churning
• Accounts ready for expansion or cross-sell

Accounts ready for expansion or cross-sellPropensity modeling elevates lead prioritisation from heuristic to predictive.

Why B2B SaaS Teams Should Care  

If you’re in B2B SaaS, you don’t win by chasing every lead. You win by chasing the right leads. Propensity models help you do exactly that.

• Sharper prioritization → Spend time on the prospects that are statistically more likely to convert.
• Better efficiency → Free your sales team from chasing dead ends.
• Unified focus → Marketing, sales, and RevOps all rally around the same predictive signals.
• Support for complex motions → Perfect for ABM campaigns, churn prevention, and expansion plays.

Teams that adopt propensity modeling don’t just see cleaner funnels. They see higher conversion rates, tighter alignment, and faster revenue cycles.

 The Anatomy of a Propensity Model

So what actually goes into a propensity model? Think of it as three key pieces: 

 Signals (Features) 

These are the inputs that tell your model why someone is likely to act. They typically fall into buckets:

• Engagement & Behavior: website visits, webinar attendance, email clicks, product usage
• Firmographics: company size, industry, funding stage, growth rate
• Technographics: tech stack, cloud provider, vendor overlap
• Competitive / Intent: researching competitors, consuming related content
• Sales Interactions: meeting frequency, velocity through pipeline stages
• Customer Success Data: NPS scores, ticket volume, sentiment

 Target Variable (Label)

This is what you want your model to predict, for example:

• “Closed won within next 90 days”
• “Churn within next 6 months”
• “Upgrade / cross-sell within next quarter”

Model Type & Training

The math behind it can be as simple as logistic regression or as complex as ensemble models. What matters more is that you train it on clean historical data, validate its accuracy, and monitor its performance over time.

Building a Propensity Model: The Strategic Process   

You don’t need a PhD in machine learning to get started. Here’s the playbook most successful SaaS teams follow:

1. Audit your data → Clean it, fix gaps, remove duplicates.
2. Engineer features → Create meaningful signals (like rolling averages of logins).
3. Define outcomes → Be crystal clear on what you’re predicting.
4. Train & validate → Split your data, test your model, measure accuracy.
5. Deploy scores → Push propensity scores into your CRM.
6. Operationalize → Map scores to plays (“>80 = sales outreach this week”).
7. Monitor & retrain → Models decay. Refresh often.

The goal isn’t perfection out of the gate, it’s actionable insights that improve over time.

💡 Want a ready-made signal checklist to guide your first model? Download our Propensity Model Signal Checklist.

The Challenges No One Tells You About

Propensity models aren’t magic. Here are the most common pitfalls to watch for:

• Messy data → Garbage in, garbage out.
• Overfitting → A model that performs brilliantly on past data but fails on new leads.
• Bias → If your history is skewed, your model will be too.
• Black-box skepticism → Sales won’t trust a model they can’t understand.
• Action gap → A model without GTM playbooks is just an expensive report.

Recognizing these upfront makes it easier to sidestep them.

Best Practices for Winning with Propensity Models

Want your model to actually stick? Follow these principles:

• Start small: Don’t wait for the perfect dataset, launch with what you have.
• Prioritize reliability: Focus on signals you can consistently capture.
• Keep it explainable: Use feature importance to show sales why a lead scored high.
• Retrain frequently: Buyer behavior changes. Your model should too.
• Integrate tightly: If scores aren’t visible in the CRM, they won’t be used.
• Tie to plays: A score is only useful if it triggers clear action.

 What Propensity Modeling Won't Do   

Let’s set expectations:

• It won’t close deals for you.
• It won’t replace ICP strategy or positioning.
• It won’t be 100% accurate, ever.

Instead, it’s a decision support system. A way to guide humans toward smarter action.

Getting Started Without Overcomplicating It

If you’re considering this journey, here’s how to dip your toe in:

• Audit your CRM: Do you trust the data inside?
• Pick one motion: Maybe start with “propensity to buy” for inbound leads.
• Run a pilot: Small, low-risk, but measurable.
• Measure lift: Compare conversion rates between scored vs. unscored leads.
• Expand: Once proven, extend to churn and expansion models.

Conclusion &  Next Steps

Propensity modeling is more than just an upgrade to lead scoring, it’s a shift toward truly predictive GTM strategy. For SaaS companies under pressure to grow predictably, it’s quickly moving from “nice-to-have” to “must-have.”

And here’s the kicker: you don’t need to start from scratch. Brendan Hughes curated a library of 120+ real-world signals B2B SaaS companies are already using in their propensity models.

👉 See the full list of CRM Propensity Model Data Points

Use it as your signal map. Start small. Build momentum. And turn your data into the growth engine it was always meant to be.

Frequently Asked Questions (FAQ)   

1. What is a “propensity model” in B2B SaaS?

A propensity model predicts the likelihood (or probability) that a prospect, account, or customer will take a specific action (e.g. convert, churn, expand) using historical and behavioral data. It’s more data-driven and adaptive than static rule-based scoring.

2. How is propensity modeling different from traditional lead scoring?

Traditional lead scoring assigns fixed point values to behaviors or attributes (e.g. “+10 points if opened email”). Propensity modeling instead uses algorithms to learn which signals actually drive outcomes, and weights them dynamically.

3. Which types of signals are commonly used in propensity models?

Common signal categories include:

• Product usage / engagement metrics
• Firmographic attributes (size, industry, funding)
• Technographic data
• Competitive / intent indicators
• Sales & pipeline interactions
• Customer success / support metrics

4. How often should a propensity model be retrained?

That depends on how quickly buyer behavior changes, how volatile your market is, and the amount of new data. As a rule of thumb, many SaaS teams retrain quarterly or semi-annually. Monitor for model drift and performance degradation.

5. Can small or early-stage SaaS companies use propensity modeling?

Yes, but you’ll need sufficient data quality and volume. Start with a simpler model, focus on signals you reliably collect, and test it on a subset of leads/accounts before scaling.

6. What are common pitfalls or mistakes when building propensity models?

Some to watch out for:

• Poor or incomplete data
• Overfitting the model to historical data
• Signal leakage (using future data that wouldn’t be available at prediction time)
• Lack of interpretability or transparency
• Failure to operationalize model scores with real business plays

7. How do you integrate a propensity model into CRM / GTM workflows?

Typical integration steps include: scoring leads/accounts automatically, surfacing those scores in CRM dashboards, defining threshold-based plays (e.g. >80% → sales outreach), and triggering downstream automation (alerts, assignments, emails).

8. What level of accuracy or lift should I expect?

It varies by industry, data richness, and model maturity. A good model might deliver a meaningful lift over baseline (e.g. higher conversion rates, better win rates). Use control groups or A/B tests to benchmark performance.

9. Can I build a propensity model without machine learning expertise?

Yes. there are low-code or no-code tools and platforms that let you build simple propensity models. The key is to properly frame signal design, outcome definition, and evaluation logic. Over time, you can iterate toward more sophisticated models.

10. How do I choose which motion to start with (buying propensity, churn, expansion)?

Pick the motion with the clearest business impact, strongest data availability, and lower barrier to experimentation (e.g. inbound lead conversion). Once you prove value in one use case, you can expand to others.