Match Scores are Eve's quantitative assessment of how well a company fits your ICP and how likely they are to be a good prospect for your solution.
A Match Score is a numerical rating (typically 0-100) that indicates:
How closely a company aligns with your ICP criteria
How strong the fit is between their situation and your value proposition
The likelihood they would benefit from your solution
Priority for outreach (higher scores = higher priority)
Score Ranges and Interpretation:
90-100: Excellent Match
Company strongly aligns with all ICP criteria
Multiple high-relevance intent signals present
Clear, urgent need for your solution
Action: Prioritize for immediate outreach
75-89: Strong Match
Company aligns well with ICP criteria
Some relevant intent signals or strong fit indicators
Likely need for your solution
Action: Include in regular outreach campaigns
60-74: Good Match
Company meets most ICP criteria
Moderate fit indicators
Potential need for your solution
Action: Include in campaigns, but may require more touchpoints
45-59: Fair Match
Company meets some ICP criteria
Limited fit indicators
Possible but less certain need
Action: Consider for outreach only if higher-scoring companies are exhausted
Below 45: Weak Match
Company marginally meets ICP criteria
Few or no fit indicators
Uncertain need for your solution
Action: Generally skip unless you have specific intel suggesting they're a good fit
Eve calculates match scores by analyzing:
ICP Criteria Alignment (40-50% of score):
Company size matches target range
Industry aligns with ICP
Location falls within target geography
Fit criteria are met (e.g., "uses Salesforce," "growing headcount")
Pain Point Indicators (20-30% of score):
Evidence company faces pain points you solve
Public statements, job postings, or signals indicating relevant challenges
Alignment between their situation and your value proposition
Intent Signals (15-25% of score):
Presence and relevance of intent signals
Recency of signals (newer signals score higher)
Number of high-relevance signals
Signal categories that align with your solution
Data Completeness (5-10% of score):
Amount of information available about company
Ability to verify persona presence
Public visibility and information richness
Technographic Fit (5-10% of score, when applicable):
Technology stack alignment
Integration possibilities
Complementary tools in use
The exact weighting adapts based on your GTM AI Profile—if your solution strongly addresses specific pain points, pain point indicators may be weighted higher.
Prioritization:
High-Score First Approach:
Start campaigns with 80+ match score companies
Maximize conversion rates and ROI on initial outreach
Build momentum and confidence with strong early results
Expand to lower scores once high-scores are exhausted
Segmented Approach:
Create separate campaigns for different score ranges
85+ match scores: Aggressive, high-touch outreach
70-84 match scores: Standard multi-channel sequences
60-69 match scores: Lower-touch, educational content
Different scoring tiers may need different messaging intensity
Volume vs. Quality:
Targeting only 90+ scores provides highest quality but limited volume
Including 60+ scores provides more volume but requires more touchpoints for conversion
Balance based on your capacity and goals
What Match Scores Are NOT:
Not Predictive Guarantees:
High score doesn't guarantee conversion
Low score doesn't mean company can't become a customer
Scores are probability indicators, not certainties
Not Replacement for Research:
Scores guide prioritization, but you should still review companies individually
High-value accounts may warrant outreach regardless of score
Your strategic knowledge trumps automated scoring
Not Static:
Scores can change as companies evolve
New intent signals may increase scores
Changes in company situation affect scoring
Periodic refresh may surface updated scores
Not Universally Comparable:
Scores are relative to YOUR specific ICP and offering
A 75 for you isn't the same as a 75 for a different company
Scoring is customized to your GTM profile
If Average Match Scores Are Low (<65):
Refine ICP Criteria:
Criteria may be too broad, catching poor fits
Narrow company size, industry, or geographic range
Add more specific fit criteria
Review and adjust based on actual customer profiles
Enhance GTM AI Profile:
Add more detailed pain points
Strengthen value propositions
Upload case studies and success stories to Knowledge Base
More context improves scoring accuracy
Adjust Targeting:
Current ICP definition may not align with your actual best customers
Analyze customers you've successfully sold to—what characteristics do they share?
Modify ICP to reflect proven success patterns
Exclude Poor-Fit Segments:
Add exclusion criteria to ICP
"Not in retail" or "Not under 50 employees"
Eliminating poor fits raises average scores
If Average Scores Are Very High (>85):
Consider Expanding:
ICP may be too narrow, limiting addressable market
Slightly broaden criteria to capture more volume
Test mid-score companies (70-80) to see if they convert
High scores are good, but not if you only have 20 companies total
Q: What's a good average match score for my ICP's companies? A: A healthy ICP typically has:
Average match score: 70-80
Top quartile: 85-95
Bottom quartile: 60-70 If your average is below 65, consider refining ICP criteria. Above 85 may indicate criteria are too narrow, limiting volume.
Q: Should I only reach out to companies with 80+ match scores? A: Not necessarily. While 80+ companies are highest priority, 70-79 companies can still convert well with good messaging. Start with 80+ to build momentum, then expand to 70+. Only skip companies below 60 unless you have specific reasons to believe they're good fits despite low scores.
Q: Can I manually adjust a company's match score? A: No, match scores are automatically calculated by Eve's algorithm based on available data. However, you can override prioritization decisions by choosing to target any company regardless of score. If you believe a company is a great fit despite a low score, trust your judgment and include them in campaigns.
Q: Why do some companies have much lower match scores than others in the same ICP? A: Even within an ICP, companies vary in fit quality:
Some meet minimum criteria but lack strong indicators
Others match multiple fit criteria AND have relevant intent signals
Data availability differs (some companies have more public info)
Score distribution within an ICP is normal and expected
Q: Do match scores consider my likelihood of closing deals? A: Match scores indicate problem-solution fit, not close probability. A high match score means the company likely needs your solution, but closing depends on:
Your sales execution
Pricing fit
Competition
Timing and budget
Internal politics and decision-making Match scores get you to the right conversations; sales skills close deals.
Q: How often are match scores updated? A: Match scores are calculated when companies are first researched. They may be recalculated if:
New intent signals are identified
Company information is updated
You modify your GTM AI Profile or ICP criteria Scores aren't updated in real-time but refresh periodically and when triggered by changes.
Q: Can two companies with the same match score be very different? A: Yes. Match scores are aggregate assessments. Two companies might score 78 for different reasons:
Company A: Perfect size/industry fit, no intent signals
Company B: Slightly off size/industry, but multiple strong intent signals Both score similarly but represent different opportunity types. Review companies individually beyond just the score.
Q: What if I disagree with match scores—a "75" looks better to me than an "85"? A: Trust your judgment and strategic knowledge. Match scores are data-driven guidance, but you may have information Eve doesn't (relationships, strategic value, market intel). Use scores as a starting point for prioritization, but override when your expertise suggests otherwise.
Q: Do intent signals significantly impact match scores? A: Yes, intent signals typically contribute 15-25% of the match score. High-relevance, recent intent signals can boost a score by 10-20 points. A company that would score 70 based on fit alone might score 85 with multiple strong intent signals. This is intentional—signals indicate increased likelihood of current need.
Q: Should I delete companies with low match scores? A: Not necessarily. Low scores might indicate:
Limited public information (not necessarily poor fit)
Different fit profile than typical (but still valid)
Emerging companies without extensive signals yet Review low-scoring companies before deleting. If they truly don't fit your ICP, remove them. If they might be decent fits, keep them but prioritize higher scores first.
