7 Best Lead Scoring Tools for Prioritization in 2026
We tested 14+ lead scoring solutions to find the best for prioritizing sales outreach. These platforms combine fit and engagement signals to rank leads by likelihood to convert, helping your sales team focus on the prospects that actually matter.
HubSpot offers predictive lead scoring built natively into its CRM. The AI analyzes your historical conversion data to identify patterns that predict success. Manual scoring rules let you layer in custom criteria for fit and engagement signals.
Starting price$890/mo
Strengths
Native CRM integration
Predictive AI included
Easy setup
Custom rules option
Behavior tracking
Limitations
Requires Enterprise tier
HubSpot ecosystem lock-in
Limited customization
Needs historical data
Who it's for: Best for HubSpot users who want powerful lead scoring without adding another tool or complex implementation projects.
Madkudu specializes in scoring for product-led growth companies. Their platform identifies Product Qualified Leads by combining fit scoring with product usage signals. Machine learning models train on your data to predict which users will convert.
Starting priceCustom
Strengths
PLG focus
PQL identification
ML models
Product signals
CRM agnostic
Limitations
Custom pricing
PLG-centric
Implementation effort
Data requirements
Who it's for: Best for product-led growth companies who need to identify which free users or trial accounts are ready for sales engagement.
Salesforce Einstein provides AI-powered scoring native to the Salesforce platform. Lead Score predicts conversion likelihood while Opportunity Score forecasts deal success. Account insights identify which accounts are most likely to buy.
Starting price$75/user/mo
Strengths
Native Salesforce
Multi-object scoring
Predictive AI
Automatic updates
Enterprise scale
Limitations
Salesforce only
Per-user pricing
Needs clean data
Complex for SMB
Who it's for: Best for Salesforce Enterprise users who want AI scoring deeply integrated into their existing CRM workflows.
Clearbit provides best-in-class firmographic data for fit-based scoring. Enrich leads instantly with company size, industry, tech stack, and more. Match leads against your ideal customer profile and score based on fit criteria.
Starting priceCustom
Strengths
Best enrichment data
Real-time lookup
ICP matching
Clean data
Flexible integration
Limitations
Fit scoring only
No behavior signals
Custom pricing
Requires scoring logic
Who it's for: Best for teams who need accurate firmographic data to score leads by fit and want to build custom scoring on top.
Infer uses machine learning to discover signals in your data that predict conversion. The platform analyzes thousands of data points to find patterns humans would miss. Explainable AI shows why each lead received its score.
Starting priceCustom
Strengths
Signal discovery
Explainable scores
Pipeline prediction
Advanced ML
Custom models
Limitations
Complex setup
Data requirements
Custom pricing
Technical implementation
Who it's for: Best for data-rich organizations who want advanced machine learning to uncover hidden patterns in their conversion data.
LeanData combines lead scoring with intelligent routing and assignment. Score leads based on rules, then automatically route them to the right reps. Account matching connects leads to existing accounts for proper scoring context.
Starting priceCustom
Strengths
Routing + scoring
Account matching
Workflow automation
Salesforce native
ABM support
Limitations
Salesforce only
Rules-based mainly
Complex setup
Custom pricing
Who it's for: Best for Salesforce users who need lead scoring combined with sophisticated routing and account-based matching capabilities.
Breadcrumbs offers no-code lead scoring that anyone can set up. Build scoring models by defining fit and activity criteria without engineering help. Revenue attribution shows which scored segments actually convert to revenue.
Starting priceFree
Strengths
No-code setup
Free tier
Revenue attribution
Easy iteration
Multiple models
Limitations
Newer platform
Less mature ML
Limited integrations
Simpler models
Who it's for: Best for growing teams who want effective lead scoring without dedicated ops resources or complex implementation projects.
We tested each platform for scoring accuracy, ease of setup, and integration quality.
Scoring Accuracy (30%) — Correlation between scores and actual conversions.
Setup Simplicity (25%) — Time and effort required to implement effective scoring.
Model Flexibility (20%) — Ability to customize and iterate on scoring models.
Integration Depth (15%) — CRM and marketing automation connections.
Pricing Value (10%) — Cost relative to accuracy and feature set.
How to Choose
Choose HubSpot Lead Scoring if you need HubSpot user.
Choose Madkudu if you need product-led growth.
Choose Einstein or LeanData if you need Salesforce user.
Choose Clearbit if you need need enrichment data.
Choose Breadcrumbs.io if you need limited budget.
Common Questions
Lead scoring assigns numerical values to leads based on their characteristics (fit) and behaviors (engagement). Higher scores indicate leads more likely to convert. Sales teams use scores to prioritize outreach and focus on the best opportunities.
Rule-based scoring uses manual criteria you define (company size, page views). Predictive scoring uses machine learning to discover patterns in your historical data. Predictive is often more accurate but requires conversion data to train models.
For predictive scoring, most platforms need 6-12 months of historical data with at least 100-500 conversions to train accurate models. Rule-based scoring can start immediately since you define the criteria based on your ICP.
