Your sales team is drowning in alerts. Website visitors flood in, but 70% don't match your ICP. SDRs waste hours vetting leads that were never going to buy. Meanwhile, your best-fit prospects slip through the cracks because they're buried in noise.
This is the ICP filtering problem, and it's killing your pipeline efficiency.
The solution? Automated qualification that scores every visitor against your Ideal Customer Profile in real-time, then routes the right leads to the right reps, instantly.
In this guide, you'll learn exactly how to set up [AI-powered lead scoring](https://www.warmly.ai/p/blog/ai-lead-scoring) that actually works, including the prompts, filters, and workflows that separate Tier 1 accounts from tire-kickers.
Quick Answer: Best ICP Filtering Approaches by Use Case
Best for real-time visitor qualification: Warmly's AI agents score visitors against your ICP in under 60 seconds, combining firmographics, behavioral intent, and buying committee data.
Best for enterprise ABM programs: 6sense offers predictive analytics and account fit scoring for large organizations with dedicated RevOps teams.
Best for HubSpot-native teams: Clearbit (now Breeze Intelligence) integrates natively with HubSpot for enrichment and scoring.
Best for budget-conscious teams: Apollo offers ICP filters and prospect scoring starting at lower price points than enterprise ABM platforms.
Best for AI-driven ICP prompts: Warmly lets you define ICP tiers using natural language prompts that evolve with your business, not rigid if-then rules.
Best for multi-source intent data: Platforms combining first-party web data with third-party signals (Bombora, job changes, social engagement) deliver the most accurate scoring.
What Is ICP Filtering? (Featured Snippet)
ICP filtering is the process of automatically identifying, scoring, and routing website visitors and leads based on how closely they match your Ideal Customer Profile. It combines:
- Firmographic data: Company size, industry, location, revenue
- Behavioral signals: Page visits, session time, repeat engagement
- AI-driven analysis: Natural language prompts that classify accounts into tiers The goal? Separate high-fit prospects from noise so sales teams focus only on accounts most likely to buy.
Key Benefits of ICP Filtering
| Benefit | Impact |
|---|
| Reduce noise | Filter out students, personal emails, competitors, non-target accounts |
| Increase conversion | 3-5x higher close rates on Tier 1 accounts |
| Speed to lead | Route qualified visitors to reps within seconds |
| Scale efficiently | Automate qualification that previously required manual review |
Why ICP Filtering Matters More Than Ever
The Hidden Cost of Manual Qualification
Here's a real scenario: A BDR at a cybersecurity company was flooded with Slack alerts containing existing customers, students, and non-ICP visitors. Every alert required manual vetting. Result? The BDR muted the channel entirely, defeating the purpose of intent data. The math doesn't work without filtering:
- Reps spend 40-60% of their day qualifying junk leads
- High-intent buyers get buried in noise
- Best-fit accounts slip through while teams chase dead ends Real discovery from a logistics company: Only 1 of 89 Google ad visitors met their $500M revenue ICP. Without filtering, 88 leads wasted sales time.
What Changed: AI Makes Real-Time Scoring Possible
Traditional approaches failed because:
- Manual spreadsheet scoring doesn't scale
- Static rule-based systems break as your ICP evolves
- Point solutions (6sense, Clearbit, ZoomInfo) are expensive and disconnected Modern AI-powered sales automation enables:
- Dynamic prompts that evolve with your business
- Real-time enrichment and scoring in under 60 seconds
- Multi-source data waterfalls combining 5+ vendors
- Contextual intelligence (e.g., "VP of Sales" means decision-maker at SMB but influencer at enterprise)
The 3-Layer ICP Filtering Framework
Layer 1: Firmographic Filtering (Company-Level)
This is your first pass. Exclude obviously wrong accounts before enrichment burns credits.
Essential Firmographic Filters
Company Size (Employee Count)
| Segment | Employee Range | Best For |
|---|
| SMB | 1-200 | Product-led, self-serve motions |
| Mid-Market | 201-1,000 | Balanced sales cycles |
| Enterprise | 1,001-10,000+ | High-touch, complex deals |
Real example: One enterprise identity company filters for 10,000+ employee U.S. companies, narrowing 18,000 total accounts to 44 high-value targets.
Revenue Range Critical for enterprise plays. Some logistics companies target accounts with $500M+ revenue. Healthcare RCM companies often focus on hospital systems with $100M+ revenue facing financial challenges.
Industry & Vertical Use Bombora taxonomy for consistency. One construction tech company expanded from one industry to seven related verticals, increasing qualified traffic 10x.
Geography Filter by country, state, or region. A global insurance company segments by U.S., Canada, UK, EU, APAC for new-hire signals.
Critical Exclusion Filters
Always filter out:
- Existing customers (unless running expansion plays)
- Active pipeline (Stage: Qualified, Demo Scheduled, Negotiation)
- Closed-Lost less than 90 days (give them breathing room)
- Personal email domains (@gmail, @yahoo, @hotmail, @outlook)
- Competitors
- Students and .edu domains (unless you sell to education)
- Internal employees (your own company domain)
Real mistake: One cybersecurity company forgot to exclude students and education leads.
Alert noise dropped 70% after adding exclusions.
Layer 2: Behavioral Intent Signals (Visitor-Level)
Not all website visits signal buying intent. Layer behavioral filters on top of firmographics using buyer intent tools.
High-Intent Page Classification
Tier 1 Intent (Hot):
- Pricing page
- Demo request page
- Free trial signup
- Product comparison pages
- Case studies
- ROI calculator
Tier 2 Intent (Warm):
- Product/features pages
- Integration pages
- Documentation
- Webinar registration
Tier 3 Intent (Cold):
- Blog posts
- Help center / support
- Career pages
- About us
Real example: One developer tools company receives 80K visitors and 260K page views monthly but keeps usage within 10K credits by placing tracking only on high-intent pages (pricing, product tours, demo request, case studies), not blog or support.
Session Quality Filters
| Signal | Minimum Threshold | High-Intent Threshold |
|---|
| Time on Site | More than 5 seconds (eliminates bots) | More than 30 seconds |
| Page Views | 1+ pages | 2+ pages in session |
| Repeat Visits | Any | 30-day active visitors |
Real example: One enterprise identity company built a HubSpot list filtering for active time over 10 seconds and multiple page views, surfacing 44 high-intent accounts from thousands.
Third-Party Intent Signals
Bombora Intent Topics
Track research on topics like "Sales Engagement Platform," "Revenue Intelligence," "Zero Trust Network Access." One SASE vendor tracked intent on "SASE" and "Zero Trust"; when accounts spiked, they enriched buying committee members and pushed to Salesforce.
Job Change Signals
New VP/Director hired = buying window. One staffing agency scraped LinkedIn posts announcing new hires, pushed 200 engagers per post into orchestration.
Social Intent
Track engagement with competitors' LinkedIn content. One data security company configured orchestrations tracking engagement with competitors' posts, triggering outreach to engaged prospects.
Layer 3: AI-Driven ICP Scoring (The Game-Changer)
Static rules can't capture nuance. AI prompts enable contextual, dynamic qualification. This is where [predictive lead scoring](https://www.warmly.ai/p/blog/predictive-lead-scoring) gets powerful.
How AI-Powered ICP Tiers Work
Instead of rigid if-then rules, define tiers with natural language prompts:
Tier 1 (Best Fit):
"Companies with 10,000+ employees in the United States, operating in software or technology, with clear evidence of a large sales or customer success team, and active hiring for revenue operations or sales enablement roles."
Tier 2 (Good Fit):
"B2B healthcare companies dedicated to improving patient outcomes. They probably serve large enterprise clients rather than our core SMB market, and sales cycles are likely longer, but they have budget and urgency."
Tier 3 / Not ICP:
"Companies outside target industries, under 50 employees, or serving primarily B2C markets."
Real example from a healthcare RCM company:
The ChatGPT default suggested "Small to medium physician practices." The sales leader (hired to target $100M+ hospital systems) corrected it to focus on large hospital systems facing financial challenges. The AI agent scraped the web, applied the new prompt, and correctly re-categorized accounts based on his business reality.
The Prompt Engineering Process
Step 1: Generate the Base Prompt
Use this master prompt with ChatGPT:
What is [YourCompany.com]'s ideal customer profile? Provide the answer in this structure:
- Tier 1 (Best Fit): Industry, Company size, Geography, Buying signals, Key characteristics
- Tier 2 (Good Fit): [same structure]
- Tier 3 / Not ICP: [same structure]
Then, provide the buying committee personas we should target.
Step 2: Refine with Your Team
- Sales: "We close 44% of Tier 1 accounts vs. 12% of Tier 2. Here's what differentiates them."
- Customer Success: "Our best customers have X in common."
- Finance: "Tier 1 has 3x higher LTV and 50% lower CAC."
Step 3: Test and Iterate
Run the prompt on:
- Closed-won accounts (should score Tier 1)
- Closed-lost accounts (should score Tier 2/3 or Not ICP)
- Current pipeline (does scoring match rep intuition?)
ICP Filtering Tools Comparison
Related: [Best 6sense Alternatives](https://www.warmly.ai/p/blog/6sense-alternatives) | [Clearbit Competitors](https://www.warmly.ai/p/blog/clearbit-competitors) | [6sense Pricing Guide](https://www.warmly.ai/p/blog/6sense-pricing)
How to Set Up Automated ICP Filtering (Step-by-Step)
Step 1: Define Your ICP in Your CRM
HubSpot Users: Create custom properties:
Warmly_ICP_Tier__c (Dropdown: Tier 1, Tier 2, Not ICP)Warmly_Intent_Score__c (Number: 0-100)Warmly_Last_Visit_Date__c (Date)Warmly_Active_Days__c (Number)WarmlyPersona_c (Text: Decision Maker, Champion, etc.)
Salesforce Users: Create custom fields at Account and Contact level:
- Account:
Warmly_ICP_Tier__c, Warmly_Intent_Score__c - Contact:
WarmlyPersonac, WarmlyBuying_Committeec
Why separate fields? Prevents overwriting existing lead scoring, allows comparison with your current model, and enables segmentation for workflows.
Related: Full Guide to Warmly Implementation
Step 2: Build ICP Segments
A segment is a reusable filter you can apply across orchestrations, Slack alerts, and CRM syncs.
Example Segment: "High-Intent ICP Tier 1"
Firmographic Filters:
- Employee Count: 1,000-10,000
- Industry: Software, Technology Services
- Country: United States
- Revenue: More than $50M (if available)
Behavioral Filters:
- Active Time: More than 10 seconds
- Pages Viewed: More than 1
- Last Seen: Last 30 days
Exclusions:
- Lifecycle Stage is not Customer
- Deal Stage is not Qualified, Demo Scheduled, Closed Won
- Email Domain is not gmail.com, yahoo.com, hotmail.com
Real example: One company started with 18,000 companies, applied firmographic filters, found 121 companies visited in last 14 days, applied ICP Tier 1 filter, surfaced 44 high-intent accounts.
Step 3: Configure AI-Powered Scoring
Option A: Using a Marketing Ops Agent (Like Warmly's)
- Connect your CRM (HubSpot or Salesforce)
- Import your audience (website visitors, CRM accounts, or both)
- Set default filters: Geography, employee range, exclude customers and active pipeline
- Paste your ICP prompt (generated in ChatGPT)
- Run the ICP agent (enriches all companies with Tier 1, Tier 2, Not ICP)
- Run the Buying Committee agent (finds 3-5 key personas per account)
- Sync results back to CRM (one-time or continuous)
Option B: Using Clay or Make.com Workflows
- Trigger: New visitor identified OR company added to CRM
- Enrichment: Pull firmographic data (Clearbit, Apollo, ZoomInfo)
- Scoring logic: Send company data + ICP prompt to OpenAI API
- Parse response: Extract Tier 1, Tier 2, or Not ICP
- Write back to CRM: Update custom field
- Route to workflow: Trigger Slack alert, sequence, or task
Pros: Full control, unlimited customization
Cons: Requires technical setup, ongoing maintenance
Step 4: Automate Routing Rules
Once accounts are scored, route them automatically using signal-based revenue orchestration.
Slack Alert Routing by ICP Tier
Channel structure:
#sales-tier1-hot - ICP Tier 1 + Pricing page visit - @mention account owner#sales-tier2-warm - ICP Tier 2 + Multiple visits - Daily digest#marketing-nurture - Tier 3 / Not ICP - Add to nurture sequence
Real example from a manufacturing software company: Reps get 15-second windows to engage high-intent prospects via AI chat or live video. Territory-based routing means each rep only sees their geographic accounts.
Real example from a computer vision company: Built 3 orchestrations per SDR (15 total): territory-based routing, vertical-specific messaging, intent-level prioritization. Each SDR receives only their leads in their Slack channel.
CRM Workflow Routing
HubSpot Workflow Example: Trigger: Contact created OR Warmly ICP Tier is known
Conditions:
If ICP Tier = Tier 1 AND Last Visit Date less than 7 days:
- Create task for account owner (Due: Today)
- Send Slack alert
- Enroll in "High-Intent Tier 1" email sequence
- Add to LinkedIn automation (if enabled)
If ICP Tier = Tier 2 AND Active Days more than 3:
- Enroll in "Warm Nurture" sequence
- Add to retargeting ad audience
If ICP Tier = Not ICP:
- Do not create task
- Do not send alert
- (Optional) Add to generic newsletter
Related: AI Outbound Sales Tools | Sales Engagement Tools
Step 5: Sync Qualification Data Back to CRM
Best Practices for Write-Back:
| Filed Type | Update Rule | Example Fields |
|---|
| Stable data | Fill if empty | Company Name, Industry, Employee Count, Revenue |
| Dynamic signals | Always update | ICP Tier, Intent Score, Last Visit Date, Active Days |
Create Warmly-specific fields to avoid overwriting existing data:
WarmlyICPTier__c instead of overwriting Lead_Score__c Warmly_Intent_Score__c instead of overwriting Engagement_Scorec
Real mistake from multiple customers: Using "always update" on stable fields caused overwrites when a new vendor returned different data.
Related: Data Enrichment Tools
Advanced: Prioritizing Limited Resources
The Credit Management Challenge
Most intent platforms charge per identified visitor or enriched contact. Poor filtering = wasted budget.
Tiered Credit Allocation:
| Tier | Enrichment Level | Alerts | Actions |
|---|
| Tier 1 | Full (company + 5 contacts) | Real-time Slack | Immediate outreach |
| Tier 2 | Company only | Daily digest | Daily digest | Add to nurture |
| Tier 3 / Not ICP | None | None | Optional content nurture |
Credit Sizing Formula:
Average monthly unique visitors x ICP match rate x 1.25 = recommended monthly credits
Example:
- 10,000 monthly visitors
- 15% identification rate = 1,500 identified
- 30% ICP match rate = 450 ICP visitors
- 450 x 1.25 = ~560 credits/month for company enrichment
- Add 5x for buying committee = ~2,800 credits/month total
The Speed-to-Lead Advantage
Data: Companies that contact leads within 5 minutes are 100x more likely to qualify them than those who wait 30+ minutes. Automated ICP filtering enables:
- High-intent visitor lands on pricing page
- AI scores as Tier 1 ICP in under 10 seconds
- Slack alert fires
- Rep joins chat or makes call while prospect is still on site
Real example: Territory-based routing gives reps 15-second windows to engage. If the rep doesn't respond, AI chatbot continues the conversation and books a meeting.
Measuring ICP Filter Effectiveness
Key Metrics to Track:
| Metric | Formula | Good | Great |
|---|
| ICP Match Rate | ICP leads / Total identified | 30% | 50%+ |
| Tier 1 Close Rate | Tier 1 closed-won / Tier 1 created | 15% | 30%+ |
| Tier 2 Close Rate | Tier 2 closed-won / Tier 2 created | 5% | 10%+ |
| Tier 3 Close Rate | Should be less than 2% | less than 2% | less than 1% |
| False Positive Rate | Scored Tier 1 but sales said "not a fit" | less than 20% | less than 10% |
| Alert Noise | Alerts ignored or muted by sales | less than 10% | less than 10% |
| Speed to Contact | Time from visit to first outreach (Tier 1) | less than 1 hour | less than 5 min |
Common ICP Filtering Mistakes (And How to Avoid Them)
Mistake #1: No Exclusion Filters
What happens: Sales drowns in noise from existing customers, active pipeline, and junk traffic.
Real example: One architecture software company's BDR Slack channel included many existing customers and non-ICP visitors. BDR ignored the channel.
Solution: Always exclude customers (Lifecycle Stage = Customer), active pipeline (Deal Stage is not blank), and personal emails (gmail, yahoo, hotmail).
Mistake #2: Filtering Too Narrowly
What happens: Lead volume drops to zero.
Real example: One global insurance company's buyer-persona filter allowed only directors, VPs, and similar titles. Segment stuck at 20. After adding broader titles, segment jumped to 64 contacts.
Solution: Start broader, then tighten. Use OR logic for titles. Include adjacent roles (Sales Ops + Revenue Ops + Business Ops).
Mistake #3: Static Scoring That Never Updates
What happens: Your ICP evolves (new product, new market), but filters don't. You keep targeting last year's buyer.
Solution: Re-run ICP scoring at least quarterly. Compare close rates by tier monthly. Update prompts when launching new products.
Mistake #4: No Feedback Loop from Sales
What happens: Marketing thinks Tier 1 = great fit. Sales disagrees. Misalignment kills pipeline.
Solution: Weekly sales + marketing sync to review top 10 Tier 1 accounts. Rep survey: "Of your last 10 Warmly leads, how many were good fits?" Target: more than 70%.
Mistake #5: Over-Reliance on Firmographics Alone
What happens: You target "perfect fit" companies with zero buying intent.
Real example: One billing software company said: "Perfect buying committee, perfect company. Now show me who's actively talking vs. engaged but dropped off 90 days ago."
Solution: The Trifecta
- ICP Tier (firmographic fit)
- Intent Score (behavioral engagement)
- Buying Committee (right people identified) Only when all three align, route to sales immediately.
Real Results
Enterprise Identity Company: From 18,000 to 44 High-Intent Accounts
Before: 18,000 companies in CRM, no way to prioritize, Gmail addresses undermined lead quality. Implementation:
- Connected HubSpot to Warmly
- Applied filters: U.S. only, 10,000+ employees, exclude customers and opportunities
- Applied active time over 10 seconds and page-view criteria
- AI agent scored ICP Tier
- Buying committee agent found 5 key personas per account
Result: 44 high-intent accounts surfaced, buying committee contacts synced to HubSpot.
Customer feedback: "The interface is better than Clay. Automated list building vs. manual spreadsheets."
Logistics Company: 1 of 89 Ad Visitors Met ICP
Challenge: Running Google Ads, 89 visitors from campaign, only 1 visitor met $500M revenue ICP.
Solution: Refined ad targeting based on Warmly data, restricted Slack alerts to ICP visitors only.
Result: Dramatically improved lead quality, lower wasted ad spend, reduced alert noise by ~70%.
B2B SaaS Company: 3x ROI Target with ICP-Driven Outreach
Goal: Close 2 deals/month (ideally 3) to hit 3x ROI on annual platform spend.
Approach: De-anonymize pricing page visitors, multi-channel orchestration (email + LinkedIn + ads), hyper-personalized messaging, ICP filters to reduce CAC.
Result (modeled): Reduced CAC, lift conversions 5-10% by targeting warmer leads vs. cold ads.
Your 30-Day ICP Filtering Checklist
Week 1: Foundation
- [ ] Define ICP tiers in writing (Tier 1, Tier 2, Not ICP)
- [ ] Generate base ICP prompt using ChatGPT
- [ ] Create custom CRM fields for ICP Tier, Intent Score, Persona
- [ ] Set up exclusion lists (customers, competitors, personal emails)
Week 2: Segmentation
- [ ] Build 3 core segments: High-Intent ICP Tier 1, Engaged ICP Tier 2, Nurture (Tier 3)
- [ ] Test segment sizes (aim for 20-50 leads/week per segment)
- [ ] Configure behavioral filters (page visits, session time, repeat visits)
Week 3: Automation
- [ ] Set up AI-powered scoring (via agent or workflow)
- [ ] Configure Slack alert routing by ICP Tier
- [ ] Build CRM workflows (task creation, sequence enrollment, retargeting)
- [ ] Enable write-back to CRM for ICP Tier and Intent Score
Week 4: Optimize
- [ ] Review top 20 Tier 1 accounts with sales. Do they agree?
- [ ] Measure: ICP match rate, Tier 1 close rate, false positive rate
- [ ] Iterate prompts based on feedback
- [ ] A/B test: Tier 1A vs. Tier 1B definitions
- [ ] Document playbook for future hires
Frequently Asked Questions
Is there a way to change the ICP prompts?
Yes. AI-powered ICP scoring uses natural language prompts that you fully control. You can edit prompts anytime to reflect new markets, products, or refined understanding of your best customers. With Warmly, you define Tier 1, Tier 2, and Not ICP using plain English descriptions. When your ICP evolves (new vertical, different company size, updated buyer personas), simply update the prompt and re-run scoring. No engineering required.
Pro tip: Review and update prompts quarterly, or immediately after launching new products or entering new markets.
How do we figure out who to focus on?
Focus on accounts where three signals align:
- ICP Tier: Company matches your firmographic criteria (size, industry, geography)
- Intent Score: Behavioral engagement shows buying interest (pricing page visits, repeat sessions, research activity)
- Buying Committee: You've identified the right decision-makers and champions When all three align, route to sales immediately. When only one or two align, add to nurture sequences and track for future intent spikes. Use buyer intent tools to measure engagement, and AI agents to classify ICP fit and find buying committee members.
How accurate is AI-powered ICP scoring?
With well-crafted prompts and multi-source enrichment, expect 80-90% alignment with human judgment. The key factors:
- Prompt quality: Generic prompts = generic results. Use specific criteria from your closed-won analysis.
- Data sources: More sources = higher accuracy. Combine firmographics, technographics, intent signals, and job data.
- Feedback loops: Sales validation improves accuracy over time. Always validate with sales feedback and close-rate analysis by tier. If Tier 1 accounts aren't closing at 3-5x the rate of Tier 2, your prompt needs refinement.
Should I filter leads before or after enrichment?
Before for firmographics (saves credits). If a company is outside your geography or industry, don't pay to enrich them.
After for behavioral and AI-driven scoring. You need enriched data to run AI classification and intent analysis.
Best practice: Apply cheap filters first (geography, employee count, exclusions), then enrich survivors, then apply AI scoring.
What if my ICP is very niche (e.g., only 6,000 possible customers)?
Upload your target account list directly. Filter ALL traffic against that list.
Example: A healthcare tech company can only sell to ~6,000 practices using a specific EMR. Most website traffic is irrelevant, so they use a whitelist. Only visitors from companies on the list trigger alerts.
How often should I update my ICP prompts?
Quarterly for most companies. Monthly if you're rapidly evolving (new product launches, market expansion). Immediately after major changes like entering a new vertical or shifting upmarket/downmarket.
Always re-score existing accounts after prompt updates to catch accounts that were previously misclassified.
Can I have different ICP tiers for different products?
Yes. Create separate segments and prompts per product line.
Example:
- "Enterprise Product Tier 1": 1,000+ employees, Fortune 500, dedicated RevOps team
- "SMB Product Tier 1": 50-200 employees, Series A-B funded, founder-led sales transitioning to team selling Route leads to different queues based on which product ICP they match.
What's the best way to convince sales to trust AI scoring?
Start with a shadow period. Score leads with AI but don't change routing. After 30 days, compare:
- Close rates by AI tier
- Rep feedback: "Was this lead a good fit?"
- Time saved on bad-fit leads Present data, not opinions. If Tier 1 accounts close at 30% and Tier 3 accounts close at 2%, the scoring is working.
How do I handle leads that are Tier 1 firmographically but have zero intent?
Add them to account-based nurture, not hot outbound. They're the right company, but timing is wrong. Track them for intent spikes using intent data. When they visit your pricing page or show research activity, move them to active outreach.
Further Reading
Warmly Product Resources
Lead Scoring & Intent Guides
Sales Automation & Tools
Website Visitor Identification
Competitor Comparisons
Pricing Guides
Data & Enrichment
Final Thoughts: The Compounding Power of Better Filtering
Poor filtering is expensive:
- 40-60% of rep time wasted on junk leads
- Best-fit buyers buried in noise
- Missed opportunities while chasing dead ends Great filtering is a competitive advantage:
- 3-5x higher close rates on Tier 1 accounts
- 50-70% reduction in sales time wasted
- 15-second response windows to high-intent visitors
- Predictable pipeline based on ICP match rate x close rate
The companies winning with ICP filtering:
- Start simple (firmographics + exclusions)
- Layer behavioral signals (page visits, repeat engagement)
- Add AI-driven scoring (prompts that evolve with your business)
- Automate routing (right lead to right rep at the right time)
- Measure and iterate (close rates by tier, false positive rate)
Within 30 days, you should have:
- 50-70% reduction in alert noise
- 3-5 high-intent Tier 1 accounts per week entering pipeline
- Clear ROI tied to ICP match rate and Tier 1 close rate
- A repeatable playbook to scale across teams
The companies seeing 3-5x ROI on intent platforms aren't doing anything magical. They're filtering ruthlessly and acting on the right signals fast.
Now it's your turn.