---
title: Lead Scoring Systems
summary: How to build point-based scoring that ranks leads by fit and interest, then route them automatically.
est_minutes: 20
sort: 6
---

#### LESSON 1: Building Point-Based Systems That Prioritize High-Value Prospects

Lead scoring is simple in concept. Every lead gets a score based on two factors: how likely they are to buy and how valuable they will be if they do buy.

High score = high priority. Low score = low priority.

But implementation is where most businesses fail. They either score too many attributes and create complexity, or they score too few attributes and miss critical signals.

Here is how to build a lead scoring system that actually works.

**The Two-Dimensional Scoring Model**

Every lead scoring system must measure two dimensions:

**Dimension 1: Fit (Who They Are)**

Does this lead match your ideal customer profile?

Fit is measured by demographic and firmographic data:

- Company size
- Industry
- Revenue
- Role/title
- Location
- Technology stack
- Number of employees

Fit tells you: "Is this person even a potential customer?"

**Dimension 2: Interest (What They Do)**

Is this lead actively engaged and showing buying intent?

Interest is measured by behavioral data:

- Content consumed
- Pages visited
- Emails opened
- Forms submitted
- Assessment completed
- Webinar attended
- Demo requested

Interest tells you: "Is this person ready to buy?"

**Why Both Dimensions Matter**

A lead with high fit but low interest is not ready yet. They need nurturing until interest increases.

A lead with high interest but low fit is not a good customer. They will buy but churn quickly or require unsustainable support.

A lead with high fit AND high interest is your top priority. They are qualified and ready.

Your scoring model must measure both dimensions and combine them into one total score.

**Defining Your Scoring Scale**

Most lead scoring systems use a 0-100 point scale.

**0-25 points: Cold Lead**

- Low fit or low interest or both
- Route to long-term nurture
- No sales contact yet

**26-50 points: Warm Lead**

- Moderate fit and moderate interest
- Continue marketing nurture
- Watch for score increases

**51-75 points: Hot Lead**

- High fit or high interest
- Sales-ready but needs qualification call
- Route to sales development rep (SDR) for qualification

**76-100 points: Priority Lead**

- High fit AND high interest
- Immediate sales contact
- Route directly to account executive
- Follow up within 1 hour

These thresholds trigger automatic routing decisions. A lead that hits 51 points automatically gets assigned to an SDR. A lead that hits 76 points goes straight to sales.

**Allocating Points: The 50/50 Rule**

How many total points should come from fit versus interest?

**Recommended allocation: 50 points maximum for fit, 50 points maximum for interest.**

This ensures that a lead needs BOTH fit and interest to reach priority status.

A lead with perfect fit (50 points) but zero interest (0 points) = 50 total (warm, not hot)

A lead with perfect interest (50 points) but zero fit (0 points) = 50 total (warm, not hot)

A lead with strong fit (40 points) and strong interest (40 points) = 80 total (priority)

This prevents bad fits with high engagement from clogging your sales pipeline, and prevents good fits with no engagement from getting contacted too early.

**Building Your Fit Scoring Model**

Fit scoring is about matching your ideal customer profile.

**Step 1: Define Your Ideal Customer Profile (ICP)**

List the characteristics of your best customers:

- Industry
- Company size (revenue and employees)
- Role/title of decision maker
- Geographic location
- Technology they use
- Budget capacity

**Step 2: Assign Point Values to Each Characteristic**

Not all characteristics are equally important. Weight them accordingly.

**Example Fit Scoring Model (B2B SaaS Company)**

**Industry (10 points maximum):**

- Target industry (SaaS, Technology, Professional Services): +10 points
- Adjacent industry (Finance, Healthcare, Education): +5 points
- Non-target industry: 0 points

**Company Revenue (15 points maximum):**

- $5M-$50M (sweet spot): +15 points
- $1M-$5M or $50M-$100M (viable but not ideal): +10 points
- Under $1M (too small): +5 points
- Over $100M (too large, long sales cycles): +5 points

**Company Size - Employees (10 points maximum):**

- 50-200 employees (sweet spot): +10 points
- 20-50 or 200-500 (viable): +7 points
- Under 20 or over 500: +3 points

**Role/Title (10 points maximum):**

- VP or Director of Sales/Marketing/Operations: +10 points
- Manager level: +7 points
- Individual contributor: +3 points
- Unidentified role: 0 points

**Location (5 points maximum):**

- North America or Europe: +5 points
- Other regions: +3 points

**Total Fit Points Possible: 50**

**Step 3: Decide What Disqualifies**

Some attributes should immediately disqualify a lead regardless of score.

Examples:

- Competitor (disqualified)
- Student or job seeker (disqualified)
- Company size below minimum threshold (disqualified)
- Industry you do not serve (disqualified)

Disqualified leads get a score of 0 and are removed from active nurture.

**Building Your Interest Scoring Model**

Interest scoring is about measuring engagement and buying intent.

**Step 4: List All Engagement Activities**

What actions can a lead take that indicate interest?

Examples:

- Visit website
- View pricing page
- Download lead magnet
- Complete assessment
- Open email
- Click email link
- Attend webinar
- Request demo
- View case study
- Watch video
- Submit contact form

**Step 5: Assign Point Values Based on Intent Level**

Not all actions indicate the same level of buying intent. Weight them accordingly.

**Low Intent Actions (1-3 points):**

- Visit website homepage: +1 point
- Open email: +2 points
- Follow on social media: +1 point

These show awareness but not serious interest.

**Medium Intent Actions (5-10 points):**

- Download lead magnet: +5 points
- Attend webinar: +7 points
- View case study: +5 points
- Watch product video: +7 points
- Complete assessment: +10 points

These show research and consideration behavior.

**High Intent Actions (15-20 points):**

- View pricing page: +15 points
- Request demo: +20 points
- Request consultation: +20 points
- Visit competitor comparison page: +15 points
- Download free trial: +20 points

These show decision-stage behavior. They are actively evaluating.

**Repeat Behavior Multipliers**

Some actions matter more when repeated.

Examples:

- Visit website once: +1 point
- Visit website 5+ times in 7 days: +5 points (indicates serious research)
- Open one email: +2 points
- Open 5+ emails in 30 days: +10 points (indicates sustained interest)

**Step 6: Time Decay**

Interest scores should decay over time. A lead who engaged heavily 6 months ago but has done nothing since is no longer hot.

**Time Decay Rules:**

- Engagement in last 7 days: Full points
- Engagement 8-30 days ago: 75% of points
- Engagement 31-60 days ago: 50% of points
- Engagement 61-90 days ago: 25% of points
- Engagement 90+ days ago: 0 points (expired interest)

This ensures scores reflect current engagement, not historical engagement.

**Total Interest Points Possible: 50** (from active engagement, before decay)

**Combining Fit and Interest: The Total Score**

Total Lead Score = Fit Score + Interest Score

Examples:

**Lead A:**

- Fit: 45/50 (perfect industry, size, role)
- Interest: 25/50 (attended webinar, opened emails, visited site 3 times)
- **Total: 70 (Hot Lead - route to SDR)**

**Lead B:**

- Fit: 50/50 (perfect profile)
- Interest: 5/50 (just opted in, minimal engagement)
- **Total: 55 (Hot Lead - but needs more nurturing before sales contact)**

**Lead C:**

- Fit: 30/50 (adjacent industry, slightly too small)
- Interest: 45/50 (viewed pricing, requested demo, high engagement)
- **Total: 75 (Priority Lead - route to sales despite imperfect fit due to strong intent)**

**Lead D:**

- Fit: 15/50 (wrong industry, too small)
- Interest: 40/50 (high engagement)
- **Total: 55 (Warm Lead - high interest but poor fit, proceed with caution)**

The total score determines routing and priority.

**Lead Scoring in Practice: Routing Rules**

Once you have scores, you need routing rules.

**Score 0-25 (Cold Leads):**

- Route to: Long-term nurture campaign
- Cadence: Monthly educational emails
- Sales contact: None
- Re-evaluation: If score increases to 50+

**Score 26-50 (Warm Leads):**

- Route to: Active nurture campaign
- Cadence: Weekly emails with content and offers
- Sales contact: None yet
- Re-evaluation: If score increases to 50+

**Score 51-75 (Hot Leads):**

- Route to: Sales Development Rep (SDR)
- Action: Qualification call to confirm fit and interest
- Timeline: Contact within 48 hours
- Outcome: Qualify to AE or send back to marketing nurture

**Score 76-100 (Priority Leads):**

- Route to: Account Executive (direct sales)
- Action: Immediate outreach
- Timeline: Contact within 1 hour
- Outcome: Book demo/consultation immediately

These routing rules can be automated in most CRM and marketing automation platforms.

**Score Increases and Decreases**

Scores are not static. They change based on ongoing behavior.

**Score Increases:**

- Lead takes new engagement actions: Score increases by point value of action
- Lead updates profile with better fit data: Fit score increases

**Score Decreases:**

- Time decay: Points lose value over time based on decay rules
- Negative signals: Unsubscribe from email (-10 points), job change to non-target role (-fit points), company acquisition by non-target company (-fit points)

**Score Monitoring:**

Sales and marketing should review:

- Leads that recently crossed threshold (e.g., jumped from 48 to 53, now sales-ready)
- Leads that recently dropped below threshold (e.g., dropped from 52 to 47, send back to marketing)
- Leads stuck in a score range for 60+ days (may need intervention or disqualification)

#### LESSON 2: Behavioral Scoring vs. Demographic Scoring

You now understand the two dimensions of scoring. Let's go deeper on the difference between behavioral and demographic scoring and how to balance them.

**Demographic Scoring: Who They Are**

Demographic scoring measures fixed or slowly-changing attributes.

**What Demographic Scoring Captures:**

**Firmographic Data (B2B):**

- Company name
- Industry
- Revenue
- Number of employees
- Location
- Public vs. private
- Funding stage

**Personal Demographic Data:**

- Job title
- Seniority level
- Department
- Years of experience
- Education level

**Technographic Data:**

- Current technology stack
- Tools they use
- Platforms they are on

**Why Demographic Scoring Matters:**

Demographic data tells you if this lead CAN be a good customer, regardless of their current engagement level.

A lead with perfect demographic fit who has not engaged yet is still more valuable than a highly engaged lead who will never be a good customer.

**Demographic Scoring Limitations:**

**Limitation 1: Data Availability**

You often do not have complete demographic data when a lead first enters your system. They opt in with just an email. You have to enrich data over time.

Solution: Score based on available data, enrich progressively through:

- Progressive profiling (ask for more info over time)
- Data enrichment tools (Clearbit, ZoomInfo, etc.)
- Behavioral inference (someone from @google.com is probably at Google)

**Limitation 2: Data Accuracy**

Demographic data can be outdated or incorrect. Someone with "VP of Marketing" in their profile might have changed jobs.

Solution: Implement data hygiene practices:

- Quarterly data cleanup
- Re-confirmation campaigns ("Is this still accurate?")
- Monitor for job change signals (LinkedIn updates, email bounces)

**Limitation 3: Demographic Alone Does Not Predict Readiness**

A perfect demographic fit with zero engagement is not ready to buy. You need behavioral signals too.

Solution: Never route to sales based on demographic score alone. Require minimum interest score.

**Behavioral Scoring: What They Do**

Behavioral scoring measures actions and engagement.

**What Behavioral Scoring Captures:**

**Website Behavior:**

- Pages visited
- Time on site
- Repeat visits
- Specific high-intent pages (pricing, demos, case studies)

**Content Consumption:**

- Lead magnets downloaded
- Webinars attended
- Videos watched
- Blog articles read

**Email Engagement:**

- Emails opened
- Links clicked
- Replies sent

**Product Interaction:**

- Free trial signup
- Product demo requested
- Consultation booked
- Assessment completed

**Why Behavioral Scoring Matters:**

Behavioral data tells you if this lead is READY to buy, regardless of whether they are a perfect fit.

A moderately good fit with very high engagement often converts better than a perfect fit with low engagement.

**Behavioral Scoring Advantages:**

**Advantage 1: Real-Time Updates**

Behavioral data updates immediately. When someone visits your pricing page, their score increases instantly.

This allows for immediate action. A lead that crosses from 49 to 76 because they requested a demo gets immediate sales attention.

**Advantage 2: Intent Signals**

Behavioral data reveals buying intent directly. Visiting pricing, requesting demos, and attending webinars are active buying behaviors.

Demographic data does not tell you intent. It tells you fit.

**Advantage 3: Recency Tracking**

Behavioral scoring with time decay keeps scores current. A lead engaged 3 months ago is not as hot as a lead engaged yesterday.

Demographic data rarely changes, so it does not indicate recency.

**Behavioral Scoring Limitations:**

**Limitation 1: Can Be Misleading**

High engagement from a poor fit wastes sales time. Someone researching your product for competitive intelligence or academic purposes will score high behaviorally but should not be contacted.

Solution: Require minimum fit score before routing to sales.

**Limitation 2: Varies by Sales Cycle**

In a 3-day sales cycle, yesterday's engagement matters. In a 6-month sales cycle, last week's engagement might not matter much.

Solution: Adjust time decay rules based on your sales cycle length. Longer cycles = slower decay.

**Limitation 3: False Positives**

Bot traffic, accidental clicks, or someone browsing because they are bored can inflate scores without real intent.

Solution: Weight high-intent actions (demo requests) much more heavily than low-intent actions (homepage visits).

**Balancing Demographic and Behavioral Scoring**

The key is balance. Neither dimension alone is sufficient.

**The Four Quadrants**

Plot every lead on a 2x2 matrix of fit (vertical axis) against interest (horizontal axis):

- **Quadrant 1, High Fit and High Interest (top right):** the ideal lead. Good fit plus high interest.
- **Quadrant 2, High Fit and Low Interest (top left):** a good fit who is not yet engaged.
- **Quadrant 3, Low Fit and Low Interest (bottom left):** poor fit with low interest.
- **Quadrant 4, Low Fit and High Interest (bottom right):** poor fit despite high engagement.

**Quadrant 1: Good Fit + High Interest**

- Score: 76-100
- Action: Immediate sales contact
- Priority: Highest

**Quadrant 2: Good Fit + Low Interest**

- Score: 40-60
- Action: Marketing nurture to increase interest
- Priority: Medium (potential)

**Quadrant 3: Poor Fit + Low Interest**

- Score: 0-30
- Action: Disqualify or long-term nurture
- Priority: Lowest

**Quadrant 4: Poor Fit + High Interest**

- Score: 40-60
- Action: Caution - high engagement but will not be good customer
- Priority: Low (likely to churn or require excessive support)

**Routing by Quadrant:**

Your scoring system should prevent Quadrant 4 leads from reaching sales even if they are highly engaged.

Set a minimum fit threshold: "Do not route to sales unless fit score is at least 25/50, regardless of total score."

This prevents wasting sales time on enthusiastic poor fits.

**Progressive Scoring: How Scores Change Over Time**

Let's walk through how a lead's score might evolve:

**Day 1: Lead Enters System**

- Action: Downloads lead magnet from LinkedIn ad
- Fit Score: 0 (no demographic data yet, only email)
- Interest Score: 5 (download action)
- Total Score: 5 (Cold Lead)
- Routing: Marketing nurture

**Day 3: Profile Enrichment**

- Data enrichment tool identifies: SaaS company, 100 employees, $10M revenue, VP of Marketing
- Fit Score: 40 (great fit)
- Interest Score: 5
- Total Score: 45 (Warm Lead)
- Routing: Continue marketing nurture

**Day 10: Email Engagement**

- Opens 3 emails: +6 points
- Clicks 2 links: +4 points
- Interest Score: 15
- Total Score: 55 (Hot Lead)
- Routing: Assign to SDR for qualification

**Day 12: Website Visits**

- Visits site 3 times: +3 points
- Views pricing page: +15 points
- Interest Score: 33
- Total Score: 73 (Hot Lead, close to priority)
- Routing: SDR confirms they are researching actively

**Day 15: High-Intent Action**

- Requests demo: +20 points
- Interest Score: 53 (capped at 50)
- Total Score: 90 (Priority Lead)
- Routing: Immediate handoff to Account Executive, demo scheduled within 24 hours

**Day 60: Post-Demo Silence**

- No engagement for 45 days after demo
- Time decay applied: Interest score drops to 25 (50% decay)
- Total Score: 65 (Hot Lead, but declining)
- Routing: Re-engagement campaign triggered

**Day 75: Re-Engagement Success**

- Replies to re-engagement email: +10 points
- Attends webinar: +7 points
- Interest Score: 42
- Total Score: 82 (Priority Lead)
- Routing: AE re-contacts, closes deal

This progression shows how scores change dynamically based on behavior and time.

**Negative Scoring: When to Subtract Points**

Not all actions increase scores. Some actions should decrease scores or trigger disqualification.

**Negative Behavioral Signals (-5 to -20 points):**

- Unsubscribes from email: -10 points (clear signal of disinterest)
- Marks email as spam: -20 points (strong negative signal)
- Repeatedly visits careers page but not product pages: -5 points (job seeking, not buying)
- Visits competitor domain immediately after visiting yours: -10 points (active comparison shopping, might be price shopping only)

**Negative Fit Signals:**

- Job change to non-target company: Reset fit score to 0, recalculate
- Company acquisition by non-target acquirer: -all fit points
- Email domain changes to @gmail from @company: -fit points (no longer at target company)
- Title change to non-target role: Adjust fit points accordingly

**Disqualification Triggers:**

Some signals should immediately disqualify:

- Identifies as student or professor doing research
- Identifies as competitor
- Explicitly states "not interested, remove from list"
- Email bounces as invalid (bad data)

Disqualified leads get score = 0 and are removed from active campaigns.

#### LESSON 3: How to Automate Lead Routing Based on Score Thresholds

Manual lead scoring does not scale. You need automation.

Here is how to set up automated lead routing based on scores.

**Automation Requirements**

To automate lead routing, you need:

1. **CRM or Marketing Automation Platform** that supports lead scoring
   - Examples: HubSpot, Salesforce, ActiveCampaign, Marketo, Pardot
2. **Scoring rules configured** as outlined in Lessons 1 and 2
3. **Routing workflows** that trigger based on score changes
4. **Assignment rules** that determine which rep gets which lead

**Step 1: Configure Scoring Rules in Your Platform**

Most platforms allow you to set up scoring rules visually without code.

**In HubSpot:**

- Navigate to Settings, Properties, Create custom property "Lead Score"
- Set up workflows that add/subtract points based on actions
- Example: "When contact downloads lead magnet, increase Lead Score by 5"

**In Salesforce:**

- Use Process Builder or Flow to create scoring rules
- Trigger: "When field changes" or "When record is created/updated"
- Action: "Update Lead Score field"

**In ActiveCampaign:**

- Use "Contact Scoring" feature
- Add rules for each action: "When contact clicks email link, add 2 points"
- Set up both behavioral and demographic scoring rules

**Scoring Rule Setup Checklist:**

- All fit attributes configured with point values
- All interest actions configured with point values
- Time decay rules set up (if platform supports)
- Negative scoring rules configured
- Disqualification triggers set up

**Step 2: Create Score Threshold Workflows**

Workflows trigger when a lead crosses a score threshold.

**Workflow 1: Lead Reaches Hot Status (Score 51+)**

Trigger: Lead score changes to 51 or above

Actions:

1. Add tag: "Hot Lead"
2. Assign to: SDR team (round-robin or based on territory)
3. Send internal notification: "New hot lead assigned to you: [Name], Score: [Score]"
4. Create task: "Qualify lead via phone call within 48 hours"
5. Update lead status field: "Sales Qualified Lead (SQL)"

**Workflow 2: Lead Reaches Priority Status (Score 76+)**

Trigger: Lead score changes to 76 or above

Actions:

1. Add tag: "Priority Lead"
2. Assign to: Account Executive (AE) directly, bypassing SDR
3. Send internal notification: "PRIORITY LEAD - Immediate action required: [Name], Score: [Score], Last action: [Action]"
4. Send email to lead: "Thanks for your interest, booking calendar link for demo"
5. Create high-priority task: "Contact within 1 hour"
6. Alert sales manager if not contacted within 2 hours

**Workflow 3: Lead Drops Below Hot Threshold (Score drops to 50 or below)**

Trigger: Lead score decreases to 50 or below (from previously being 51+)

Actions:

1. Remove from sales queue
2. Reassign to marketing nurture
3. Send internal notification: "Lead [Name] dropped below hot threshold, returned to marketing"
4. Add to re-engagement campaign

**Workflow 4: Lead Disqualified (Score = 0 or disqualify trigger)**

Trigger: Lead marked as disqualified or score = 0

Actions:

1. Remove from all active campaigns
2. Unassign from sales rep
3. Update status: "Disqualified"
4. Archive (do not delete - keep for reporting)

**Step 3: Set Up Assignment Rules**

When a lead reaches sales-ready status, who gets it?

**Assignment Options:**

**Option 1: Round Robin**

- Leads are assigned to available reps equally
- Ensures balanced workload
- Best for teams with similar territories or product lines

**Option 2: Territory-Based**

- Leads assigned based on geography, company size, or industry
- Ensures specialization and efficiency
- Best for teams with defined territories

**Option 3: Account-Based**

- Check if lead belongs to existing account
- If yes: Assign to current account owner
- If no: Use round robin or territory rules

**Option 4: Availability-Based**

- Only assign to reps who are currently available (not on vacation, not over capacity)
- Requires updating rep availability status

**Recommended: Hybrid Approach**

1. Check if lead belongs to existing account, assign to account owner
2. If no existing account, check territory/specialty, assign to appropriate rep
3. If multiple reps match, use round robin among qualified reps
4. If no reps available, queue for next available rep

**Step 4: Configure Notifications**

Sales reps need to know immediately when high-priority leads arrive.

**Notification Types:**

**Email Notifications:**

- Subject: "New Priority Lead Assigned: [Company Name]"
- Body: Lead name, score, company, last action taken, contact info, assignment timestamp
- Send to: Assigned rep + sales manager for priority leads

**Slack/Teams Notifications:**

- Post to sales channel: "Priority Lead: [Name] from [Company], Score: 82, requested demo. Assigned to: @rep"
- Use emojis to indicate urgency (fire for priority, warning for hot)

**SMS Notifications (for priority leads only):**

- "PRIORITY LEAD: [Name], [Company], Score: 85, Contact within 1 hour"
- Only use for scores 76+

**In-App Notifications:**

- Pop-up in CRM when rep logs in
- Shows queue of assigned leads by priority

**Notification Rules:**

- Priority leads (76+): Email + Slack + SMS + In-app
- Hot leads (51-75): Email + Slack + In-app
- Warm leads (26-50): In-app only (no immediate urgency)
- Cold leads (0-25): No notifications (handled by marketing)

**Step 5: Build Reporting Dashboards**

You need visibility into how the lead scoring system is performing.

**Key Metrics to Track:**

**Score Distribution:**

- How many leads in each score range?
- Is distribution healthy or are most leads clustered in one range?

**Conversion Rate by Score:**

- What percentage of 51-75 leads convert to customers?
- What percentage of 76-100 leads convert?
- Are higher scores actually converting better?

**Time to Conversion by Score:**

- How long does it take 51-75 leads to close?
- How long for 76-100 leads?
- Do higher scores close faster?

**Sales Response Time:**

- For priority leads, what is average time to first contact?
- Are priority leads being contacted within 1 hour?

**Lead Velocity:**

- How many leads are crossing into sales-ready (51+) each week?
- Is this number increasing or decreasing?

**Score Accuracy:**

- Are high-scoring leads actually qualified when sales contacts them?
- If not, scoring rules need adjustment

**Dashboard Setup:**

Create one dashboard visible to both sales and marketing showing:

- Current lead count by score range
- Leads that crossed thresholds this week
- Conversion rates by score
- Average response time
- Lead velocity trend

Review monthly and adjust scoring rules based on data.

**Step 6: Scoring Rule Optimization**

Your initial scoring model will not be perfect. Optimize over time based on data.

**Monthly Review Questions:**

1. **Are high-scoring leads actually qualified?**
   - If sales is rejecting 50%+ of 76+ leads, scores are inflated
   - Solution: Increase point requirements or add minimum fit threshold
2. **Are high-scoring leads converting at higher rates?**
   - If 51-75 leads convert at same rate as 26-50 leads, scoring is not predictive
   - Solution: Re-examine which actions/attributes get points
3. **Are we missing opportunities?**
   - If leads with scores 40-50 are converting well, threshold is too high
   - Solution: Lower hot lead threshold to 45
4. **Is time decay working?**
   - Are old, inactive leads still showing high scores?
   - Solution: Implement or strengthen time decay rules
5. **Are we seeing score gaming?**
   - Are leads taking low-value actions repeatedly to inflate scores?
   - Solution: Cap points from repeated low-value actions

**Optimization Process:**

1. Pull conversion data by score range
2. Identify gaps (high scores not converting, low scores converting)
3. Hypothesize which scoring rules are causing issues
4. Make targeted adjustments (do not overhaul entire model)
5. Monitor for 30 days
6. Measure impact
7. Keep changes that improve predictiveness, revert changes that do not

Lead scoring is not "set it and forget it." It requires ongoing optimization.
