Directions:
Copy and paste the prompt below into your company's authorized Gen AI too to determine Lead Scoring.
# B2B Lead Scoring Analysis Prompt
## Input Data Requirements
Please ensure you have the following CSV files with their required fields:
1. leads.csv
- lead_id
- company_name
- industry
- annual_revenue
- employee_count
- country
- state
- lead_source
- lead_status
- created_date
- last_activity_date
- assigned_rep_id
2. quotes.csv
- quote_id
- lead_id
- quote_date
- total_amount
- product_category
- quote_status
- close_date
- probability
- competition
- loss_reason
- modified_date
- sales_rep_id
3. quote_items.csv
- quote_id
- item_id
- product_id
- product_name
- category
- quantity
- unit_price
- discount_percent
- margin_percent
- line_total
4. activities.csv
- activity_id
- lead_id
- quote_id
- activity_type
- activity_date
- activity_result
- next_step
- notes
- sales_rep_id
## Analysis Instructions
Using the provided CRM data files, please:
1. Calculate lead scores based on the following weighted criteria:
A. Engagement Score (30%)
- Meeting/demo attendance (40%)
- Email/call response rate (30%)
- Quote request frequency (20%)
- Website/document interactions (10%)
B. Company Profile Score (40%)
- Annual revenue match (35%)
- Industry alignment (25%)
- Employee count fit (20%)
- Geographic location (20%)
C. Behavioral Score (30%)
- Quote history (40%)
- Response time to quotes (25%)
- Purchase timeline clarity (20%)
- Budget authority (15%)
2. Generate lead grades using these score ranges:
- A: 90-100 (High potential)
- B: 70-89 (Strong prospect)
- C: 50-69 (Moderate potential)
- D: 30-49 (Needs nurturing)
- E: 0-29 (Low potential)
3. Create a dashboard visualization showing:
- Lead score distribution
- Key performance metrics
- Industry performance comparison
- Lead source effectiveness
- Sales rep performance
## Desired Outputs
1. Dashboard Components:
- Lead grade distribution chart
- Conversion metrics by score grade
- Industry performance comparison
- Lead source distribution
- Sales activity impact analysis
2. Key Metrics Display:
- Quote-to-win rate
- Average deal size
- Sales cycle length
- Overall engagement score
3. Strategic Insights:
- Top-performing industries
- Most effective lead sources
- Best-converting sales activities
- Risk factors in low-scoring leads
## Analysis Parameters
- Analysis timeframe: Rolling 12 months
- Minimum activity threshold: 2 interactions
- Score calculation frequency: Daily
- Conversion rate baseline: Industry average
- Activity recency weight: Last 90 days prioritized
## Example Implementation
```python
# Sample code structure for lead scoring
import pandas as pd
from datetime import datetime, timedelta
def calculate_engagement_score(activities_df, lead_id):
"""Calculate engagement score based on activities"""
lead_activities = activities_df[activities_df['lead_id'] == lead_id]
# Weight different activity types
activity_weights = {
'Meeting': 1.0,
'Demo': 0.8,
'Call': 0.6,
'Email': 0.4
}
# Calculate weighted score
activity_score = sum(activity_weights.get(act_type, 0.5)
for act_type in lead_activities['activity_type'])
return min(100, activity_score)
def calculate_company_score(lead_data):
"""Calculate company profile score"""
revenue_score = revenue_scoring_logic(lead_data['annual_revenue'])
industry_score = industry_scoring_logic(lead_data['industry'])
employee_score = employee_scoring_logic(lead_data['employee_count'])
return (revenue_score * 0.35 +
industry_score * 0.25 +
employee_score * 0.20 +
location_score * 0.20)
def calculate_behavioral_score(quotes_df, lead_id):
"""Calculate behavioral score based on quote history"""
lead_quotes = quotes_df[quotes_df['lead_id'] == lead_id]
quote_score = quote_scoring_logic(lead_quotes)
response_score = response_time_scoring_logic(lead_quotes)
timeline_score = timeline_scoring_logic(lead_quotes)
return (quote_score * 0.40 +
response_score * 0.25 +
timeline_score * 0.35)
```
## Data Processing Notes
- Handle missing values appropriately
- Normalize scores to 0-100 scale
- Account for industry-specific benchmarks
- Consider seasonal patterns
- Weight recent activities more heavily
## Visualization Guidelines
- Use consistent color scheme
- Include interactive tooltips
- Show trend lines where applicable
- Highlight significant deviations
- Enable filtering by time period
## Recommended Actions
1. For A-grade leads:
- Immediate sales follow-up
- Executive engagement
- Custom solution development
- Priority quote processing
2. For B-grade leads:
- Regular sales contact
- Product demonstrations
- Case study sharing
- Competitive analysis
3. For C-grade leads:
- Nurture campaigns
- Educational content
- Needs assessment
- Value proposition reinforcement
4. For D-grade leads:
- Lead nurturing
- Market education
- Requirement clarification
- Budget discussion
5. For E-grade leads:
- Marketing automation
- Periodic review
- Resource optimization
- Segment reevaluation