lead-scorer

name	lead-scorer
description	Lead puanlama sistemi. Behavioral scoring, fit scoring, priority sıralama ve model tuning.
triggers	{"keywords":["lead scoring","lead puanlama","BANT","MQL","SQL","lead qualification","prioritization"]}
auto_load_when	Kullanıcı lead qualification, MQL/SDR süreç optimizasyonu veya puanlama modeli talep ettiğinde
agent	researcher
tools	["Read","Write","Grep","Glob"]

Lead Scorer (Lead Puanlama Uzmanı)

Odak Alanı: Lead qualification için puanlama sistemleri tasarlamak, behavioral ve fit scoring modelleri oluşturmak ve priority sıralama algoritmaları geliştirmek.

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Pattern 1: Behavioral Scoring Modeli

1.1 Web Behavior Scoring

Scoring Framework (Weight-based)
├── Page Visit Scoring
│   ├── Homepage: +1
│   ├── Pricing page: +3
│   ├── Case studies: +2
│   ├── Demo request: +5
│   └── Blog content: +1
├── Content Engagement
│   ├── PDF download: +2
│   ├── Video watch (>50%): +3
│   ├── Webinar attendance: +4
│   └── Newsletter signup: +1
├── Email Engagement
│   ├── Email open: +1
│   ├── Link click: +2
│   ├── Reply: +5
│   └── Attachment open: +2
└── Form Submissions
    ├── Contact form: +5
    ├── Free trial: +8
    └── Quote request: +10

1.2 Engagement Scoring Matrix

Lead Scoring Categories:
├── Awareness (Score 0-15)
│   ├── First visit
│   ├── Blog read
│   └── Social media engagement
├── Consideration (Score 16-40)
│   ├── Multiple page visits
│   ├── Demo video watch
│   └── Pricing page view
├── Intent (Score 41-70)
│   ├── Multiple downloads
│   ├── Case study read
│   └── Free trial started
└── Decision (Score 71+)
    ├── Demo request
    ├── Pricing inquiry
    └── Sales contact

1.3 Behavioral Data Points

B2B SaaS Specific:
├── Product usage signals
│   ├── Login frequency
│   ├── Feature adoption depth
│   ├── Active users count
│   └── In-app behavior
├── Account signals
│   ├── Company size
│   ├── Industry vertical
│   ├── Job title relevance
│   └── Geographic location
└── Intent signals
    ├── Competitor comparison
    ├── Implementation timeline
    ├── Budget authority
    └── Decision maker status

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Pattern 2: Fit Scoring Modeli

2.1 Firmographic Fit Scoring

ICP Criteria Weights:
├── Company Size (Total Score: 20)
│   ├── Ideal: 50-500 employees → +20
│   ├── Acceptable: 20-50, 500-1000 → +10
│   └── Outside: <20, >5000 → +0
├── Industry (Total Score: 15)
│   ├── Target industry → +15
│   ├── Adjacent industry → +8
│   └── Non-target → +0
├── Revenue (Total Score: 15)
│   ├── $1M-50M → +15
│   ├── $50M-200M → +10
│   └── <$1M, >$200M → +0
├── Technology Stack (Total Score: 10)
│   ├── Current customer tech → +10
│   ├── Compatible tech → +5
│   └── No fit → +0
└── Geographic (Total Score: 5)
    ├── In-region → +5
    └── Out-of-region → +0

2.2 Psychographic Scoring

Buyer Persona Fit:
├── Role-based (Score 0-20)
│   ├── Champion: +10
│   ├── User: +5
│   ├── Influencer: +8
│   ├── Decision maker: +20
│   └── No role identified: +0
├── Budget authority (Score 0-15)
│   ├── Has budget: +15
│   ├── Can request budget: +8
│   └── No budget control: +0
├── Timeline (Score 0-10)
│   ├── 0-3 months: +10
│   ├── 3-6 months: +5
│   └── 6+ months: +2
└── Initiative fit (Score 0-10)
    ├── Active initiative: +10
    ├── Future initiative: +5
    └── No initiative: +0

2.3 Combined Score Formula

Total Lead Score = Behavioral Score × Weight + Fit Score × Weight

Standard Weights:
├── Behavioral Score: 40%
├── Fit Score: 40%
└── Engagement Score: 20%

Score Thresholds:
├── Hot Lead (80-100): Immediate follow-up
├── Warm Lead (60-79): Nurture campaign
├── Cool Lead (40-59): Content nurture
└── Cold Lead (0-39): Long-term nurture

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Pattern 3: Priority Sıralama Sistemi

3.1 Urgency-Weighted Priority

Priority Matrix:
├── High Priority (Score 90+)
│   ├── Decision maker + High budget + Active now
│   ├── Demo requested + Fit score high
│   └── Hot behavioral signals + Low sales cycle
├── Medium Priority (Score 70-89)
│   ├── Influencer + Medium budget
│   ├── Consideration stage + Good fit
│   └── Multiple touchpoints + Medium intent
├── Low Priority (Score 50-69)
│   ├── Early stage + Good fit
│   ├── Low engagement + High fit
│   └── New lead + No fit assessment yet
└── Nurture (Score <50)
    ├── No fit identified
    ├── Low engagement
    └── Early in buying journey

3.2 Lead Routing Logic

Routing Rules:
├── Score-based routing
│   ├── Score >90 → A-team (senior reps)
│   ├── Score 70-89 → B-team (standard reps)
│   ├── Score 50-69 → Inside sales
│   └── Score <50 → SDR outbound queue
├── Geography-based
│   ├── Region match → Local team
│   └── Out of region → HQ or partner
├── Industry specialization
│   ├── Vertical match → Vertical specialist
│   └── General → Generalist team
└── Account-based
    ├── Target account → Named account team
    └── Non-target → General bucket

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Pattern 4: Model Tuning ve Optimizasyon

4.1 Scoring Calibration

Calibration Process:
├── Data collection
│   ├── 6+ months historical data
│   ├── Won/lost deal outcomes
│   └── Lead source tracking
├── Baseline metrics
│   ├── Conversion rates by score
│   ├── Time-to-close by segment
│   └── Deal size by score range
├── Model validation
│   ├── A/B test scoring model
│   ├── Identify false positives
│   └── Identify false negatives
└── Continuous improvement
    ├── Monthly score review
    ├── Quarterly model update
    └── Annual framework overhaul

4.2 KPI Tracking

Scoring Model KPIs:
├── Prediction accuracy
│   ├── Precision: % of hot leads that convert
│   ├── Recall: % of actual converters identified
│   └── F1 score: Balance metric
├── Business impact
│   ├── Conversion rate improvement
│   ├── Sales cycle reduction
│   └── Revenue per lead
└── Operational metrics
    ├── Lead response time
    ├── Outreach efficiency
    └── Rep satisfaction

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Pattern 5: BANT ve MEDDIC Frameworks

5.1 BANT Qualification

Traditional BANT:
├── Budget
│   ├── Question: "What is your budget for this?"
│   ├── Score: Yes = 20, Maybe = 10, No = 0
├── Authority
│   ├── Question: "Who will make the final decision?"
│   ├── Score: Decision maker = 20, Influencer = 10, User = 5
├── Need
│   ├── Question: "What problem are you solving?"
│   ├── Score: Clear need = 20, Vague = 10, None = 0
└── Timeline
    ├── Question: "When do you want to implement?"
    ├── Score: <3 months = 20, 3-6 months = 10, >6 months = 5

BANT Scoring Total: /80 points

5.2 MEDDIC Qualification

Advanced Framework:
├── Metrics
│   ├── "What metrics will determine success?"
│   └── Quantified success criteria
├── Economic Buyer
│   ├── "Who controls the budget?"
│   └── Decision maker identification
├── Decision Criteria
│   ├── "What are your evaluation criteria?"
│   └── Technical + business requirements
├── Decision Process
│   ├── "How will you make this decision?"
│   └── Timeline + stakeholders
├──Identify Pain
│   ├── "What is the cost of the problem?"
│   └── Urgency quantification
├── Champion
│   ├── "Who is advocating for this?"
│   └── Internal sponsor strength
└── Score: Each category = 10 points → /60 total

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Key Patterns (Özet)

Pattern	Odak	Uygulama
Behavioral Scoring	Digital signals	Page visits, email, content
Fit Scoring	ICP alignment	Firmographic + psychographic
Priority Sorting	Urgency + fit	Matrix-based routing
Model Tuning	Accuracy	Monthly calibration
Frameworks	Qualification	BANT, MEDDIC, GPCT

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Anti-Patterns

❌ Yasaklı Yaklaşımlar

Scoring errors:
  - Fixed weights (never adjust)
  - No lead source tracking
  - Ignoring intent signals
  - No model validation
  
Operational errors:
  - Manual scoring (not automated)
  - No response SLA
  - Rep bypass scoring
  - Stale data (>30 days)

✅ Doğru Yaklaşımlar

Best practices:
  - Weighted scoring model
  - Automated data collection
  - Regular model calibration
  - Clear score thresholds
  
Validation:
  - A/B testing
  - Conversion tracking
  - Feedback loop with sales
  - Quarterly review

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Quick Reference

Score Range	Segment	Action	SLA
90-100	Hot	Immediate call	<15 min
70-89	Warm	Personal outreach	<1 hour
50-69	Cool	Email nurture	<24 hour
0-49	Cold	Automated nurture	>24 hour

Weight Category	Default	Adjust for
Behavioral	40%	Long sales cycle
Fit	40%	Product-led growth
Engagement	20%	High-volume inbound

Framework	Best for	Questions
BANT	SMB	4
MEDDIC	Enterprise	6
GPCT	SaaS	6

Metric	Target	Warning
Precision	>70%	<50%
Recall	>80%	<60%
Lead-to-Opportunity	>25%	<15%

🌍 Universal Language Support

• Turkish Native: This skill natively supports Turkish. If the user prompt is in Turkish, all analysis, formatting, and output MUST be entirely in Turkish. You do not need explicit "write in Turkish" instructions.