🌍 Overview
This project builds an AI-driven customer segmentation model using Salesforce CRM data to identify, prioritize, and target high-value customers. It combines CRM analytics, clustering, and visualization to enable smarter lead prioritization, campaign alignment, and data-backed decision-making for sales and marketing teams!
📘 View Dashboard + Full Case Study →
The client’s sales and marketing teams were struggling to identify which customers offered the highest value potential. Their existing CRM system tracked large volumes of lead data but lacked data-driven segmentation, resulting in low conversion rates and inconsistent outreach.
✅ Analyze Salesforce CRM data to uncover actionable customer clusters
✅ Align sales strategies with segment behavior and revenue potential
✅ Create visual dashboards for continuous market insight
Collaborated with sales and marketing teams to define clear segmentation goals, focusing on improving lead targeting and building customized engagement playbooks for top customer tiers.
Pulled contacts, opportunities, and engagement data from Salesforce CRM. Cleaned and standardized datasets, removing duplicates and aligning inconsistent fields. Normalized key attributes such as industry, deal size, and lifecycle stage using Python.
Applied K-Means clustering and hierarchical segmentation in Amazon SageMaker and Python. Grouped customers based on revenue potential, opportunity stage, engagement frequency, and region. Created profiles for high-growth, retention, and low-priority customer segments.
Developed interactive dashboards in Tableau and Amazon QuickSight. Enabled filtering by industry, region, and lifecycle stage for deeper analysis. Generated segment-level recommendations for sales and campaign targeting.
Built sales playbooks for each segment category — from retention workflows to cross-sell and upsell automation in Salesforce. Provided marketing with actionable messaging guidelines tailored to each segment’s needs.
AWS Services:
Amazon S3
AWS Lambda
Amazon SageMaker
Amazon QuickSight
AWS Glue
Amazon Redshift
Salesforce CRM
Python
Pandas
Tableau
CRM Data Analysis
Market Segmentation
Customer Insights Modeling
Data Visualization
Key Outcomes:
📊 Lead Conversion Rate: Improved by 33% through targeted engagement
💡 Sales Productivity: Increased by 27% due to prioritization of high-value segments
🎯 Campaign ROI: Enhanced by 42% with personalized messaging per cluster
⚙️ Dashboard Adoption: Sales and marketing teams integrated visualization into daily workflows
The segmentation model transformed raw Salesforce CRM data into strategic intelligence for decision-makers. It enabled the organization to: Focus on high-value customer clusters with the greatest ROI potential Align sales and marketing actions through unified data visualization Establish a scalable segmentation framework applicable across industries