Welcome to the leadworks-intelligence-platform- wiki!

Lead scoring is a predictive AI process that assigns a numerical score to each lead based on their likelihood to convert. Higher scores indicate high-potential leads, while lower scores indicate less valuable leads.

✅ Predictive Scoring - AI models analyze historical data to score new leads.
✅ Behavior-Based Scoring - Tracks engagement like email opens, website visits, form submissions.
✅ Demographic & Firmographic Scoring - Uses industry, company size, job title, and location.
✅ Customizable Scoring Rules - Allow businesses to define custom weights for different factors.
✅ Real-Time Scoring - Automatically updates lead scores based on new interactions.

• Model: Logistic Regression, Random Forest, XGBoost, or Deep Learning.
• Data Inputs:
  • Explicit Data: Industry, revenue, company size, job title, email domain.
  • Implicit Data: Website visits, time spent on page, email open rates.
  • CRM Data: Past interactions, support tickets, deal closures.
• Output: A lead score (0-100) with categories (Hot, Warm, Cold).

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This module ensures that leads are valid, complete, and unique before they enter the sales pipeline.

✅ Spam Detection - Detects fake, bot-generated, or malicious leads.
✅ Duplicate Detection - Identifies multiple leads with the same email, phone, or company.
✅ Data Completeness Check - Flags leads with missing critical fields (e.g., email, phone).
✅ Role-Based Filtering - Removes low-value leads like interns, students, or generic emails.

• Spam Detection: Use NLP models (SpamAssassin, Naïve Bayes) or pattern-based filtering.
• Duplicate Matching: Apply fuzzy matching on names, emails, and phone numbers.
• Data Completeness: Check for missing values in key fields.
• Role-Based Filtering: Use a blacklist of emails (e.g., *@gmail.com, *@yahoo.com).

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Lead enrichment automatically fills missing details in a lead by pulling data from external sources like LinkedIn, Clearbit, Apollo.io and other databases.

✅ Company Information Fetching - Adds company name, industry, size, revenue.
✅ Social Profile Linking - Finds LinkedIn, Twitter, and other social profiles.
✅ Email & Phone Verification - Checks if emails/phone numbers are valid and active.
✅ Job Title & Department Matching - Ensures correct job role classification.
✅ Geo-Location Detection - Finds city, country, and timezone.

• APIs Used: LinkedIn API, Clearbit API, Hunter.io, ZoomInfo, FullContact.
• Data Sources: Public data, web scraping, CRM integrations.
• Workflow:
  1. Lead submitted → Check missing fields
  2. Call enrichment API → Append fetched data
  3. Store updated lead in database

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This module predicts which leads are most likely to convert by analyzing past engagement and behavioral patterns.

✅ Conversion Probability Prediction - Uses AI to predict the likelihood of a lead converting.
✅ Engagement Score - Tracks user behavior (email clicks, webinar participation).
✅ Sentiment Analysis on Emails - Analyzes email responses for positive/negative tone.
✅ Lead Buying Intent Detection - Identifies leads showing strong purchase intent.

• Model Used: Deep Learning (LSTMs), Gradient Boosting (XGBoost).
• Data Inputs:
  • Email open rates, meeting attendance, support ticket interactions.
  • Past purchases, customer feedback, website session duration.
• Output:
  • Conversion probability (0-100%)
  • Engagement score (Low, Medium, High)

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This module syncs lead data with CRM platforms (HubSpot, Salesforce) and automates workflows based on lead quality & scoring.

✅ Auto-Sync Lead Scores to CRM - Updates lead records in HubSpot, Salesforce.
✅ Workflow Triggers - Example: Send a follow-up email if a lead is Hot.
✅ Lead Routing - Assigns high-quality leads to top sales reps.
✅ Automated Follow-Ups - Triggers email/SMS based on lead behavior.

• CRM APIs Used: HubSpot API, Salesforce API, Zoho CRM API.
• Workflow Example:
  • If lead score > 80 → Assign to senior sales rep.
  • If lead score < 40 → Send nurture email sequence.
• Lead Status Automation:
  • New → Contacted → Engaged → Closed/Won

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✅ Lead Scoring: Predict lead potential using AI.
✅ Lead Quality Check: Remove spam, duplicates, and incomplete leads.
✅ Lead Enrichment: Fetch missing details (LinkedIn, company info).
✅ Lead Insights: Predict conversion likelihood & engagement levels.
✅ Automated CRM Actions: Sync & trigger sales workflows.

Feature | Technology -- | -- Backend API | Flask / FastAPI Database | PostgreSQL / MongoDB Machine Learning | Scikit-Learn, TensorFlow, XGBoost Frontend | React / Next.js Lead Enrichment | LinkedIn API, Clearbit API CRM Integration | HubSpot API, Salesforce API Automation | Zapier, Webhooks

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• ✅ Start with Lead Scoring (ML Model + API)
• ✅ Add Lead Quality Check (Spam Detection, Duplicates)
• ✅ Integrate Lead Enrichment (Data Fetching from APIs)
• ✅ Implement Conversion Probability & Engagement Prediction
• ✅ Automate Lead Routing & CRM Sync

Would you like help setting up the ML models for Lead Scoring & Insights? 🚀