The Great Debate: Predictive vs. Rule-Based Intent Scoring
Let’s talk about something I always hear in SaaS marketing: how should we approach B2B intent scoring? It’s a hot topic, and for good reason—it’s central to how we prioritize accounts and align sales and marketing.
Here’s how I explain it: “There’s this ongoing debate about intent scoring. Should it be a fully predictive model, where a score is automatically generated without user input? Or should it be a rule-based model, where you assign weights to specific actions?”
Both approaches have their pros and cons, and they fit different needs depending on your company’s goals and tech stack. Let me break them down for you.
TL;DR
- Predictive intent scoring uses AI to forecast near-term conversion actions but can feel like a black box and struggles with B2B’s long sales cycles.
- Rule-based scoring allows assigning weights to specific actions, offering flexibility and transparency for prioritizing high-intent accounts.
- Factors combines predictive models for short-term accuracy with flexible rule-based systems featuring pre-built templates, decay mechanisms, and dynamic scoring.
- Measuring success requires tracking predictive power and ensuring transparency, so teams trust and effectively use the scoring system.
The Predictive Model Approach
Predictive scoring uses AI to automatically generate likelihood-to-convert scores, and while its simplicity and automation are appealing, it comes with notable challenges.
The downside is that it’s a black-box model. You get a score, but how do you trust it? How do you build intuition around it? When your sales team asks, ‘Why should we reach out to these companies?’ you can’t just say, ‘A black-box system told me so.’
Another big challenge with predictive models in B2B is deciding what to predict. Is the goal to predict a gated content download? The first inbound inquiry? A sales meeting? Or the creation of an opportunity? The long sales cycles in B2B make this even trickier. Given the complexity of sales cycles in many companies, it’s hard to predict with confidence for each of these stages. Without a clear prediction target, the model risks becoming vague and less actionable.
The Rule-Based Model Approach
Rule-based scoring lets marketers assign weights to specific actions and combine them into a final score. While it’s more transparent and customizable than predictive models, the key to success lies in finding a system flexible enough to fit your use case.
Here’s what I always emphasize when it comes to rule-based scoring:
- Comprehensive Data Integration
You need a system that can handle any type of data for scoring. This includes:
- Marketing campaigns tracked in Salesforce.
- Sales meetings and calls.
- Website activity and engagement.
- Company-level signals, like LinkedIn ad clicks.
- Review site intent from platforms like G2 or Capterra.
- Custom intent signals tailored to your business
- Flexible Rule Definition
You want the ability to define rules that align with your goals. For instance, you might assign higher weights to engagements from C-level executives compared to interactions from anonymous users.
With the right flexibility and data integration, rule-based scoring gives your team clarity and control over how to prioritize leads and accounts.
The Factors Approach: A Blended Solution
At Factors, we’ve developed an approach that blends the best of predictive and rule-based scoring. Our predictive model focuses on near-term conversion actions. We ask questions like, ‘Is this account likely to submit an inbound inquiry within the next 30 days?’ rather than trying to predict if an account will become an opportunity 6 months from now. That’s just crystal ball gazing.
We complement this predictive layer with a flexible rule-based system that includes:
- Pre-built templates to simplify weight assignments.
- Default scoring systems to help you get started quickly.
- Natural decay mechanisms to ensure scores remain accurate over time.
Here’s why the decay mechanism is crucial: Without decay, scores just keep climbing, even if there’s no recent activity. You need a system where inactivity brings the score down naturally, and new activity boosts it based on assigned weights and frequency. That keeps your scoring dynamic and reflective of real-time engagement.
This combined approach ensures you always work with actionable, up-to-date insights to prioritize the right accounts.
Measuring Success: The True Test of Intent Scoring
One often overlooked aspect of B2B intent scoring is figuring out how to measure its effectiveness. You need to know what the score for an account was before a conversion action happened. Once you’ve created an opportunity, you don’t want a circular dependency where you give it a high score simply because the opportunity was created—that’s not helpful.
Instead, the focus should be on predictive power. You want to be able to say that if you pick the top 10% of non-opportunity accounts graded by the system, 60% of your future opportunities came from that group, even before the opportunity existed.
This kind of transparency and predictive accuracy is critical for adoption. Without it, intent scoring models lose credibility. People need conviction in the scoring model you implement. If they don’t trust it, they’ll try it for a month, say, ‘Sorry, it didn’t work,’ and abandon it completely.
Building trust in your intent scoring model ensures it becomes a tool your team relies on rather than something they dismiss after a short trial.
Implementation Best Practices
When implementing an intent scoring system, consider these key factors:
- Start with Clear Objectives: Define what conversion actions matter most for your business
- Choose the Right Data Sources: Integrate all relevant data points, including:some text
- Website behavior
- Marketing campaign engagement
- Sales activities
- Third-party intent data
- Set Up Proper Validation: Ensure you can measure the effectiveness of your scoring system
- Maintain Transparency: Keep your scoring rules clear and explainable to stakeholders
The Future of Intent Scoring
As privacy regulations evolve and third-party cookies phase out, intent scoring systems must adapt. The future lies in solutions that can:
- Respect user privacy while providing valuable insights
- Integrate multiple data sources for a complete picture
- Offer transparent, explainable scoring mechanisms
- Provide clear ROI measurement capabilities
Conclusion
Intent scoring is not just about generating a number – it's about creating actionable insights that sales and marketing teams can trust and use effectively. Whether you choose a predictive model, rule-based approach, or a hybrid solution, the key is ensuring transparency, measurability, and practical applicability for your specific business context.
At Factors, we simplify intent scoring by combining predictive accuracy with flexible rule-based models. Our platform integrates data from all your key sources—website behavior, marketing campaigns, and sales activities—while maintaining transparency and trust. With tools like pre-built templates and decay mechanisms, we ensure actionable insights that drive results. Ready to prioritize high-value opportunities? Let’s connect and get started!
