The New HubSpot Lead Scoring Model: A Guide for 2025

With the timeframe involved, now is a great time to step back, assess a lead scoring implementation, and build using the new tools. Additionally, it is crucial to reevaluate existing scores when new lead scoring criteria are established to ensure all records are accurately prioritized based on updated criteria.

Introduction to Lead Scoring

Lead scoring is a crucial process in sales and marketing that helps identify and prioritize high-quality leads. It involves assigning a numerical score to each lead based on their behavior, characteristics, and demographic information. The goal of lead scoring is to qualify leads effectively and ensure that sales teams focus on the most promising leads. By using lead scoring, businesses can improve their sales efficiency, conversion rates, and customer satisfaction. In this section, we will explore the basics of lead scoring, its importance, and how it can be implemented in sales and marketing efforts.

Key Transitional Dates

It's crucial to be aware of the timeline for sunsetting the old scoring properties:

• May 1, 2025: The ability to create new custom score properties using the legacy system was disabled. You can no longer go to Settings > Properties and select the 'Score' field type for new properties.
• July 1, 2025 (Potential): Some sources suggest the ability to edit existing legacy score properties may be disabled around this time. (It's best to confirm this within your HubSpot portal or documentation.)
• August 31, 2025: Existing legacy score properties will stop updating entirely. Records will no longer gain or lose points based on the old criteria. Any workflows or lists relying on these properties to update will cease to function as intended.
• Q4 2025: Legacy score properties may be deleted from portals if they are not actively used in assets (like lists or workflows).

Action Required: Migration is not automatic. You must manually recreate your scoring logic within the new Lead Scoring or Health Scoring tools.

What is Lead Scoring

At its core, lead scoring is a methodology used to rank prospects based on their perceived value to your organization. By assigning points to leads based on various attributes (like demographics, firmographics) and behaviors (like website engagement, email interaction), and deducting points based on specific negative attributes (like geographic location or annual revenue), you can create a system that helps marketing and sales teams:

• Prioritize efforts: Focus your time and resources on leads that are most likely to convert.
• Improve efficiency: Prevent sales teams from wasting time on unqualified leads.
• Enhance alignment: Create a common understanding between marketing and sales on lead quality (MQLs vs. SQLs). Collaboration between marketing and sales departments is essential for selecting relevant attributes and criteria for scoring leads.
• Personalize outreach: Tailor your communication based on a lead’s score and the factors that contribute to it.

How does Lead Scoring help my Business?

Lead scoring helps focus the attention of sales and marketing teams on the most important contacts and companies. By analyzing how leads interact with various engagement metrics, such as online behavior and email engagement, businesses can gain better insights into lead conversion rates. By aligning a scoring model to both your Total Addressable Market and your ideal custom profile, you will then surface the most relevant leads you have. A lead's score based system is crucial in prioritizing and analyzing leads, allowing for the identification of sales qualified leads and facilitating effective marketing strategies.

Components of a Lead Scoring Model

A lead scoring model consists of several components, including demographic information, behavioral data, and scoring criteria. Demographic information includes factors such as job title, company size, and industry, while behavioral data includes actions such as form submissions, email opens, and website interactions. Scoring criteria are the rules that determine how points are assigned to each lead based on their behavior and characteristics. A well-designed lead scoring model should take into account both positive and negative attributes, such as engagement scores and negative points, to provide a comprehensive view of each lead. By using a combination of these components, businesses can create a robust lead scoring model that helps them identify and prioritize high-quality leads.

Replicating an existing setup

While it is possible to replicate the old scoring model with the new tool, in practice, this will be challenging since they are designed to work in different ways.  

Creating a new Model

Introducing the Core Change: Fit vs. Engagement Scoring

The most significant change in HubSpot’s new model is the separation of scoring into two distinct categories: Fit and Engagement. This dual-metric approach provides a much clearer picture of a lead’s potential. Custom scores can be tailored to prioritize contacts and companies based on specific actions or demographic properties, highlighting the flexibility and functionality of the lead scoring tool in managing and qualifying leads.

1. Fit Score:

• Purpose: Measures how well a prospect matches your Ideal Customer Profile (ICP).
• Based On: Relatively static demographic and firmographic data.
• Examples: Industry, company size, annual revenue, job title, location, technologies used.
• Answers the Question: “Is this the right type of company or person for us to sell to?”

2. Engagement Score:

• Purpose: Measures a prospect’s level of interest and interaction with your brand.
• Based On: Dynamic behavioral data showing buying intent.
• Examples: Website page views (especially high-value pages like pricing), form submissions, content downloads (whitepapers, case studies), email opens/clicks, webinar registrations/attendance, demo requests, specific interactions tracked via custom events.
• Answers the Question: “How interested is this prospect in us right now?”

Why Two Scores Are Better Than One

This separation solves several common lead scoring problems:

• Clear Prioritization: You can easily identify leads who are both a High Fit and High Engagement – these are your prime candidates for immediate sales outreach.
• Targeted Nurturing: Leads with High Fit but Low Engagement (the "diamond in the rough") might have been missed previously. Now, they can be specifically targeted with marketing nurture campaigns to build interest.
• Reduced Wasted Effort: Sales avoids spending cycles on Low Fit but High Engagement leads (those who download everything but aren't a good match). These can be deprioritized or directed to self-service options.
• Strategic Insights: Analyzing the matrix helps refine marketing campaigns and ICP definitions.

Fit Score

Engagement Score

Recommended Action

High

High

Immediate Sales Outreach

High

Low

Marketing Nurture Campaign

Low

High

Direct to Self-Service/Alt.

Low

Low

Deprioritize / Low-touch Nurture

Key Features of the New HubSpot Scoring Tools

Beyond the Fit/Engagement split, the new tools (primarily the Lead Scoring app for Marketing Hub Pro/Enterprise and Health Scoring for Service Hub Pro/Enterprise) offer enhanced functionality:

• Flexible Score Builder: An intuitive interface to define criteria for both Fit and Engagement scores. Numerical scores are assigned based on the importance of selected attributes chosen through collaboration between marketing and sales departments.
• Company & Contact Scoring: Apply scoring logic at both the individual contact and the overall company level, crucial for Account-Based Marketing (ABM).
• AI-Assisted Scoring (Enterprise): HubSpot’s AI analyzes your historical data (successful conversions) to provide recommendations for refining your Fit and Engagement scoring criteria, blending human expertise with machine intelligence.
• Predictive Lead Scoring (Enterprise): A separate, automated feature using machine learning to calculate a “Likelihood to close” score (probability of closing within 90 days) and assign a “Contact priority” tier (Very High, High, Medium, Low). This complements, but is distinct from, the user-defined Fit/Engagement scores.
• Score Thresholds: Define ranges (e.g., 0-49 = Low, 50-74 = Medium, 75-100 = High) to easily categorize leads and trigger workflows.
• Score History & CRM Card: Scores are prominently displayed on contact/company records, showing how they’ve changed over time and which criteria were met.
• Score Decay: Automatically reduce scores for leads who become inactive over a defined period, ensuring scores reflect current engagement levels.
• Recency & Frequency Scoring (Coming Soon/Available): Ability to score based on how often or how recently an action occurred (e.g., visited pricing page 3 times in the last week).
• List Exclusion: Instead of using negative points (which are being phased out), you can exclude specific lists of contacts or companies (e.g., competitors, partners, existing customers) from being scored entirely.
• Custom Event Scoring: Incorporate interactions tracked via custom behavioral events or third-party integrations (like Amplitude, Pendo) into your scoring model.
• Combined Scores (Enterprise): Option to merge Fit and Engagement scores into a single, unified score.

Migrating to the New Model: Planning Your Approach

Since migration is manual, careful planning is essential.

1. Audit Your Current Model:
   • Document all existing positive and negative criteria in your legacy HubSpot Score properties.
   • Analyze its effectiveness: Are the leads it identifies actually converting? Is it aligned with sales feedback?
   • Export your current scores for reference.
2. Define Your New Fit & Engagement Criteria:
   • Revisit your Ideal Customer Persona (ICP): What firmographic and demographic traits truly define your best customers? These form the basis of your Fit score.
   • Evaluate Marketing Touchpoints: Which actions genuinely indicate buying intent? Prioritize high-intent actions (e.g., demo request, pricing page view) over lower-intent ones (e.g., blog view) for your Engagement score.
   • Separate your audited legacy criteria into Fit vs. Engagement categories.
3. Build in the New Tool:
   • Use the Lead Scoring app (Marketing Hub) or Health Scoring app (Service Hub) to recreate your model.
   • Start with the Fit score criteria.
   • Build out the Engagement score criteria, considering recency and frequency where applicable.
   • Define your score thresholds (Low, Medium, High).
   • Set up list exclusions if needed.

1. Test and Validate:
   • Apply the new scoring to a segment of your database or use HubSpot's testing features.
   • Review the initial results. Are the scores making sense? Are the right leads being prioritized?
   • Adjust criteria and point values as needed.
2. Train Your Team:
   • Educate both sales and marketing on the new Fit/Engagement model.
   • Establish clear guidelines for lead handling based on score combinations (e.g., High/High vs. High/Low).
   • Create a feedback loop for ongoing refinement.
3. Update Assets:
   • Modify any lists, workflows, reports, or integrations that relied on the old legacy score properties to use the new Fit, Engagement, or Combined score properties.

Leveraging Lead Scoring in Automation

The true power of lead scoring is realized when integrated into your automation strategy:

• Automated Notifications for Sales Teams: Trigger tasks or notifications for sales reps when a lead reaches a specific Fit/Engagement threshold (e.g., High Fit + High Engagement). Lead hits are integral to automating this process, as they trigger notifications to the sales team when a lead reaches a specific score.
• Targeted Content Delivery: Enroll leads into specific nurture workflows based on their score profile. Low-engagement leads might receive educational content, while high-engagement leads get case studies or demo offers.
• Lifecycle Stage Updates: Automatically update a contact’s lifecycle stage (e.g., from Lead to MQL) when they meet certain scoring criteria.
• Dynamic List Segmentation: Create smart lists based on score ranges for targeted campaigns or reporting.

Reporting and Analyzing Lead Quality

Continuously monitor the effectiveness of your new scoring model:

• Generate Lead Score Reports: Use HubSpot’s reporting tools to visualize the distribution of leads across Fit and Engagement scores. Track how leads move between score categories over time.
• Analyze Conversion Rates by Score: Correlate lead scores with actual sales outcomes (e.g., MQL-to-SQL conversion rate, close rates). Are higher-scoring leads converting at a higher rate?
• Analyze Lead Attribution: Connect scoring data with source data. Which marketing channels or campaigns are generating the highest quality leads (high Fit/Engagement scores), not just the highest volume?
• Refine Based on Data: Use these insights to regularly tweak your scoring criteria and point values for continuous improvement. Most lead scores are typically based on a point range, which helps in categorizing leads for a more accurate evaluation of lead quality.

Common Challenges and How to Overcome Them

• Challenge: Defining accurate Fit/Engagement criteria.
• Solution: Deeply analyze historical closed-won data. Collaborate closely between sales and marketing to define the ICP and key buying signals. Start simple and refine over time. Additionally, emphasize the importance of positive points in evaluating lead actions and engagement with the brand.
• Challenge: Maintaining data quality (Inaccurate Data Points).
• Solution: Implement regular data cleansing processes. Use HubSpot’s data management tools. Ensure forms capture accurate information. Use list exclusions to filter out irrelevant contacts.
• Challenge: Adapting to the dual-score mindset.
• Solution: Clear training and documentation for sales and marketing. Emphasize the strategic value of understanding both Fit and Engagement.
• Challenge: Over-complicating the model.
• Solution: Start with the most critical criteria. Avoid adding too many rules initially. Focus on the factors with the biggest impact on conversion.

Enhancing Lead Scoring with External Data

• Integrating CRM and Marketing Data: Ensure seamless data flow between HubSpot and other tools (e.g., sales engagement platforms, other CRMs if applicable) for a complete view of the customer journey.
• Using Third-Party Tools: Consider data enrichment tools (like ZoomInfo, Clearbit) to automatically append firmographic/demographic data for more accurate Fit scoring. Integrate product usage data (via tools like Segment, Pendo, Amplitude) to inform Engagement scores for SaaS businesses. A predictive lead scoring model can further enhance this process by identifying and prioritizing sales qualified leads, utilizing contact properties and interactions to predict lead conversion probabilities.

Summary

HubSpot's shift to Fit and Engagement scoring represents a significant advancement in lead qualification. By embracing this dual-metric approach and leveraging the new features like AI assistance and score decay, businesses can gain a much clearer understanding of their leads, improve sales and marketing alignment, and ultimately drive more efficient growth. While the migration requires effort, the result is a more precise, actionable, and intelligent lead scoring system fit for the complexities of modern B2B sales and marketing.

Generating Lead Score Reports

Generating lead score reports in HubSpot provides valuable insights into the types of prospects entering the sales funnel. These reports help businesses focus on crucial leads and enhance overall business insights.

By visualizing lead distribution across scoring ranges, businesses can identify trends and adjust their lead scoring models accordingly.

Analyzing Lead Attribution

Analyzing lead attribution helps businesses understand the most effective sources and activities that generate high-quality leads. By determining which marketing channels deliver the highest quality leads, businesses can optimize their marketing strategies and focus their efforts on the most promising prospects.

Sales Alignment Through Lead Scores

Sales alignment is critical to the success of any lead scoring initiative. By using lead scores, sales teams can focus on the most promising leads and prioritize their efforts accordingly. Lead scores can also help sales teams understand the lead’s interest and behavior, enabling them to tailor their approach to each lead’s specific needs. Furthermore, lead scores can be used to route leads to the right sales teams, ensuring that each lead is handled by the most suitable sales representative. By aligning sales efforts with lead scores, businesses can improve their conversion rates, reduce sales time, and increase customer satisfaction.

Aligning Sales and Marketing

Aligning sales and marketing teams is essential to the success of any lead scoring initiative. Sales and marketing teams should work together to define scoring criteria, identify trends, and assign points to each lead. By collaborating on lead scoring, sales and marketing teams can ensure that they are working towards the same goals and that their efforts are aligned. This alignment can help businesses improve their sales efficiency, conversion rates, and customer satisfaction. Additionally, aligning sales and marketing teams can help reduce human error, improve data quality, and increase the effectiveness of lead scoring initiatives.

Common Challenges in Lead Scoring and How to Overcome Them

Lead scoring can present several challenges, such as inaccurate data points and inefficient manual scoring. Addressing these challenges is essential for maintaining the accuracy and effectiveness of your lead scoring system.

Scoring attributes, derived from user behavior and engagement, are crucial for creating accurate lead scoring models that help sales teams target the most promising prospects effectively.

The following subsections will provide strategies for dealing with inaccurate data points and balancing positive and negative scores.

Dealing with Inaccurate Data Points

Inaccurate data points can skew lead scores, leading to flawed marketing and sales efforts. To address this, businesses should identify non-leads and deduct points accordingly.

Regular data audits and cleansing processes can help maintain data accuracy and ensure a more accurate representation of lead quality.

Balancing Positive and Negative Scores

Balancing positive and negative scores is crucial for effectively identifying sales qualified leads. Negative points can be assigned for undesirable behaviors or attributes, such as inactivity or lack of decision-making authority.

Implementing score degradation techniques ensures that older interactions lose value over time, preventing inflated lead scores and maintaining the relevance of current data.

Enhancing Lead Scoring with External Data

Integrating external data sources can greatly improve lead scoring metrics. This enhancement leads to increased precision and accuracy in identifying potential leads. By incorporating product usage data, such as feature engagement and time spent in the app, businesses can refine their lead scoring models and achieve greater accuracy.

Collaboration between the sales and marketing team is crucial in enhancing lead scoring systems. Consistent communication and feedback loops between these teams ensure that scoring criteria align with actual conversion results, leading to improved insights on lead quality and a more effective sales approach.

The following subsections will guide you through integrating CRM and marketing data and using third-party tools to enhance lead scoring.

Integrating CRM and Marketing Data

Integrating CRM and marketing data creates a comprehensive view of leads, enhancing the lead scoring process. Start by identifying key data points that need synchronization, such as lead sources, engagement metrics, and conversion rates.

Utilize HubSpot’s data integration features to automate data flow between sales and marketing tools, ensuring real-time updates. Regular data audits and cleansing processes are essential to maintain data accuracy and integrity across platforms. This integration not only improves targeting but also fosters collaboration between sales and marketing teams, leading to better alignment and performance.

Using Third-Party Tools

Third-party tools can provide additional data points, enhancing the lead scoring process by incorporating insights from various platforms. Tools like Segment can integrate different data sources, enabling more precise lead scoring through enriched profiles.

The 'Hubspot Score' contact property allows users to assign points based on user-defined criteria and interactions, thereby enhancing the granularity and effectiveness of lead scoring for sales and marketing efforts.

By supplementing lead information with valuable insights, businesses can refine their scoring models and achieve greater accuracy in identifying high-potential leads.

Best Practices for Lead Scoring

There are several best practices for lead scoring that businesses should follow. First, businesses should define clear scoring criteria and assign points to each lead based on their behavior and characteristics. Second, businesses should use a combination of demographic and behavioral data to create a comprehensive view of each lead. Third, businesses should regularly review and update their lead scoring model to ensure that it remains effective and accurate. Finally, businesses should use lead scoring to prioritize leads and focus their sales efforts on the most promising leads. By following these best practices, businesses can improve their sales efficiency, conversion rates, and customer satisfaction.

Implementing Best Practices

Implementing best practices for lead scoring requires a combination of technology, process, and people. Businesses should invest in lead scoring software that can help them automate and streamline their lead scoring process. They should also establish clear processes and procedures for defining scoring criteria, assigning points, and prioritizing leads. Finally, businesses should train their sales and marketing teams on how to use lead scoring effectively and ensure that they are aligned and working towards the same goals. By implementing these best practices, businesses can improve their sales efficiency, conversion rates, and customer satisfaction, and ultimately drive more revenue and growth.

Summary

In conclusion, mastering HubSpot lead scoring is essential for optimizing your marketing and sales efforts in 2025. You can effectively prioritize and nurture your leads by understanding the basics, leveraging advanced strategies, and following best practices. Regularly monitoring and updating your lead scoring models, integrating external data, and automating processes will ensure that your system remains accurate and efficient. Implement these strategies to enhance your lead management process and drive higher conversion rates.

Frequently Asked Questions

• What is the main change in HubSpot's new lead scoring? The biggest change is the split into two distinct scores: Fit Score (how well a lead matches your ICP) and Engagement Score (how interested a lead is based on their interactions). Legacy single-score properties are being sunsetted.
• How does the new HubSpot lead scoring system work? You define criteria (demographic/firmographic for Fit, behavioral for Engagement) and assign points. HubSpot calculates the scores based on contact/company data and activities. The scores are displayed on records and can be used for segmentation, automation, and reporting. Enterprise users also have access to AI assistance, combined scores, and predictive scoring.
• Do I have to use both Fit and Engagement scores? While you can create just one type, the primary benefit comes from using both to get a complete picture. Enterprise users can combine them into a single score if preferred.
• What are the benefits of predictive lead scoring in HubSpot? Predictive lead scoring (Enterprise only) uses machine learning to automatically calculate a lead's "Likelihood to close" score based on historical data, saving time and potentially uncovering patterns humans might miss. It complements the manually configured Fit/Engagement scores.
• How do I handle negative scoring in the new model? The concept of assigning negative points is being de-emphasized. Instead, use List Exclusions to prevent specific groups (like competitors or unqualified leads) from being scored at all. Score decay also helps lower scores for inactivity.
• Is the migration from the old system automatic? No. You must manually recreate your scoring logic in the new Lead Scoring or Health Scoring tools before the August 31, 2025 deadline when the old properties stop updating.
  

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