Maximizing Lead Quality: From Raw Data to Revenue

Not all leads are created equal. Here's how to separate the wheat from the chaff and focus on prospects who will actually buy.

Quality vs. Quantity

The lead generation trap: more leads = more revenue, right?

Wrong.

1,000 bad leads will waste your time. 100 great leads will grow your business.

| Metric | Low Quality | High Quality | |--------|-------------|--------------| | Reply rate | 1-2% | 10-15% | | Meeting rate | 0.5% | 5% | | Close rate | 5% | 25% | | CAC | High | Low |

Focus on quality from the start.

Defining Lead Quality

What Makes a Quality Lead?

1. Fit - They match your ICP
2. Intent - They have the problem you solve
3. Timing - They're ready to buy now (or soon)
4. Authority - They can make or influence decisions
5. Budget - They can afford your solution

Your ICP Matters

Before scraping anything, define your Ideal Customer Profile:

• Company size - Employee count, revenue
• Industry - Vertical, niche
• Geography - Regions, languages
• Technology - Stack, tools they use
• Behavior - Hiring, raising, expanding

The more specific your ICP, the better your leads.

Improving Lead Quality at Each Stage

Stage 1: Source Selection

The problem: Scraping random websites produces random leads.

The solution: Be intentional about sources.

High-quality sources:

• Industry-specific directories (G2, Capterra, Clutch)
• Conference attendee lists
• Award lists and rankings
• Funded company databases
• Professional association members

Lower-quality sources:

• Generic business directories
• Purchased lists
• Scraped social media
• Random website scraping

Stage 2: Filtering

The problem: Raw scraped data includes everyone.

The solution: Filter aggressively.

Filter by:

• Company size - Remove too small/too large
• Industry - Focus on your verticals
• Role - Target decision makers
• Technology - Match your integration needs
• Location - Focus on serviceable regions

Stage 3: Validation

The problem: Invalid data wastes time and hurts reputation.

The solution: Multi-tier validation.

Scrappy validates:

• Email syntax and format
• Domain MX records
• Mailbox existence
• Spam trap detection
• Catch-all identification

Accept only: High-confidence emails (deliverability score 80%+)

Stage 4: Enrichment

The problem: Basic data isn't enough for personalization.

The solution: Enrich with additional data points.

Useful enrichment:

• Recent funding or news
• Technology stack
• Hiring activity
• Social media presence
• Company growth signals

Tools: Clearbit, Apollo, ZoomInfo, BuiltWith

Stage 5: Scoring

The problem: Not all filtered leads are equal.

The solution: Implement lead scoring.

Simple scoring model:

| Signal | Points | |--------|--------| | Company size 50-500 | +10 | | Industry match | +15 | | Decision maker title | +20 | | Recent funding | +25 | | Uses competitor | +30 | | Email from company domain | +10 | | Valid phone number | +5 |

Focus on leads scoring 50+ first.

Advanced Quality Techniques

Intent Signals

Look for leads showing buying intent:

• Job postings - Hiring for roles your product supports
• Technology changes - Switching tools you integrate with
• Funding - Fresh capital to spend
• Expansion - New offices, markets, products
• Pain signals - Public complaints about competitors

Negative Scoring

Remove points for red flags:

• Generic role titles
• Personal email addresses
• Tiny companies (under 10 employees)
• Overseas when you're local-only
• Already a customer or competitor

Time-Based Decay

Leads get stale. Score decay:

• 0-30 days: Full score
• 30-60 days: -10%
• 60-90 days: -25%
• 90+ days: -50% or re-validate

Human Review

For high-value deals, add manual review:

1. Check LinkedIn for real person
2. Verify company website
3. Look for mutual connections
4. Research recent activity
5. Confirm decision-making authority

Worth the time for enterprise deals.

Measuring Lead Quality

Upstream Metrics

Track quality at scraping stage:

• Valid email rate - % that pass validation
• Domain match rate - % with company email
• Decision maker rate - % with target titles
• ICP match rate - % matching your ICP

Downstream Metrics

Track quality through the funnel:

• Reply rate by source - Which sources perform?
• Meeting rate by segment - Which segments convert?
• Close rate by score - Does scoring predict wins?
• Revenue per lead - Ultimate quality metric

Feedback Loops

Close the loop between sales and sourcing:

• Which leads became customers?
• What did they have in common?
• Which sources produced winners?
• Update ICP based on data

Common Quality Mistakes

Mistake 1: Scraping Everything

Wrong: "Get as many leads as possible" Right: "Get as many qualified leads as possible"

Mistake 2: Skipping Validation

Wrong: "We'll find out when we email them" Right: Validate before any outreach

Mistake 3: One-Size-Fits-All

Wrong: Same message to every lead Right: Segment and personalize by quality tier

Mistake 4: Ignoring Feedback

Wrong: Keep sourcing the same way Right: Iterate based on conversion data

The Quality Flywheel

High-quality leads create a virtuous cycle:

1. Better leads → Higher reply rates
2. Higher replies → More meetings
3. More meetings → Better win rates
4. Better wins → Higher revenue per lead
5. Higher revenue → More budget for quality sources
6. → Even better leads

Action Plan

This Week

• [ ] Define or refine your ICP
• [ ] Audit your current lead sources
• [ ] Set up email validation (if not already)
• [ ] Create basic scoring criteria

This Month

• [ ] Add 2-3 high-quality sources
• [ ] Implement lead scoring
• [ ] Set up enrichment for top leads
• [ ] Track source-level conversion

This Quarter

• [ ] Build feedback loop with sales
• [ ] A/B test source quality
• [ ] Iterate on scoring model
• [ ] Optimize for revenue per lead

The Bottom Line

Quality beats quantity every time.

With Scrappy, you can:

• Target the right sources
• Validate every email
• Filter by ICP criteria
• Score and prioritize leads

Stop chasing bad leads. Start closing good ones.