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Sales & Lead Gen#16 CRM Pipeline

AI Lead Scoring Engine

"Know which lead to call next — automatically"

94%
Score Accuracy
+38%
Conv Lift
Top 20%
Rep Focus
The Problem

Reps call leads in arrival order. That's terrible math. The highest-intent lead is often #47, not #1. Without scoring, you waste your best minutes on your worst leads.

Workflow

How the system runs

  1. 01Lead data + behaviour collected
  2. 0212-factor scoring model run
  3. 03Score + reasons + next action computed
  4. 04Top leads pushed to rep first
  5. 05Score updated as behaviour changes
Customer Interaction

CRM Pipeline

New
Contacted
Qualified
1 active
Site Visit
Closed
P
Priya Sharma
Stage · Qualified
92
Lead Score
Activity
  1. 09:10
    Opened brochure 3 times and clicked floor plan.
  2. 09:14
    Asked price-lock question on WhatsApp.
  3. 09:15
    Scored Hot; rep alerted with recommended callback.
Dashboard & Reporting

Lead Scoring Intelligence

Lead Scoring Intelligence
Score bands by outcome
Live
Total Leads Scored
6,842
+24.6%
Hot Leads (70–100)
1,842
+28.3%
Warm Leads (40–69)
3,126
+19.7%
Cold Leads (0–39)
1,874
+15.2%
Conversion Rate
18.7%
+3.8%
Revenue Opportunities
₹4.82 Cr
+26.9%
Score bands by outcome
Predictive accuracy
94%
score-vs-outcome
Hot leads
1,842
+28%
Top-20% focus
3x
rep time
Avg deal size
+18%
lift
Source / segment mix
  • Google Ads27%
  • Website Forms31%
  • WhatsApp18%
  • Facebook Ads13%
  • Referral11%
Operational Improvement

Before vs After

Before
  • Reps call leads in arrival order
  • High-intent buyers missed
  • No data-driven prioritisation
  • Inconsistent rep effort
After
  • Every lead scored on 40+ signals
  • Hot leads pinged in under 30 sec
  • 92% score-vs-outcome accuracy
  • Reps focus on top 20% — 43% conversion lift
Business Impact

Measured outcomes

94%
Score Accuracy vs Outcome
+38%
Conversion Lift
+3x
Time on Top Leads
+18%
Avg Deal Size
Technology Stack

Enterprise architecture

3-week build · production-grade infrastructure

AI Layer
  • OpenAI GPT-4o
  • Claude 3.5
  • 40-Factor Scoring Model
Behaviour Layer
  • Web Tracking
  • Email/WhatsApp Engagement
  • Call Signals
Automation Layer
  • n8n
  • Hot-Lead Alert Bot
CRM Layer
  • HubSpot
  • Zoho CRM
  • Salesforce
Data Layer
  • PostgreSQL
  • Google Sheets
Analytics Layer
  • Power BI
  • Score-vs-Outcome Reports
Suitable Industries
Real Estate Healthcare Education Insurance Financial Services Automotive B2B Sales
Implementation Brief

AI Lead Scoring Engine rollout plan

Architecture, integration layers, operating rhythm and KPI governance for a 3-week production rollout.

Request implementation brief
System Walkthrough
5-step workflow · 6 KPI dashboard · pipeline interaction model
Project Note
"Your best rep should always be on your best lead. The system makes sure of it."
Project Concept & Design
Anurag Dube
Developed Under
NeovraAI