Business Implications
The segmentation initiative transformed raw CRM data into actionable sales intelligence. By aligning sales priorities with customer value and readiness, it improved conversion efficiency, streamlined targeting efforts, and empowered teams with a scalable, data-driven approach to customer relationship management.
Steps Performed
Analyzed Salesforce CRM datasets using clustering and visualization to segment customers by potential value, engagement level, and deal readiness for sales and marketing optimization.
1.
Define Segmentation Objective
Collaborated with sales and marketing teams to identify segmentation goals focused on improving conversion rates and designing personalized outreach strategies for different customer tiers.
2.
Extract And Clean CRM Data
Pulled contact, opportunity, and engagement data from Salesforce CRM, cleaning duplicates and normalizing key attributes such as industry, deal size, and lifecycle stage.
3.
Build AI Segmentation Model
Applied K-Means and hierarchical clustering in Python and SageMaker to group customers based on opportunity stage, revenue potential, and engagement frequency.
4.
Visualize Segments And Insights
Created interactive dashboards in Tableau and Amazon QuickSight, visualizing customer clusters with filters for region, industry, and lifecycle stage to inform go-to-market decisions.
5.
Recommend Strategic Actions
Developed actionable playbooks for each segment—automating Salesforce workflows for lead nurturing, retention, and cross-sell opportunities to improve customer lifetime value.
AWS Services Used
Amazon S3
AWS Lambda
Amazon SageMaker
Amazon QuickSight
AWS Glue
Amazon Redshift
Salesforce CRM
Python
Pandas
Tableau
Technical Tools Used
CRM Data Analysis
Market Segmentation
Customer Insights Modeling
Data Visualization
Skills Demonstrated
Salesforce Market Segmentation
CRM Data-Driven Customer Prioritization Framework
Designed a market segmentation model using Salesforce CRM data to identify and prioritize high-value customer segments. The project improved lead targeting, optimized campaign strategy, and enabled smarter sales decision-making through AI-enhanced data clustering and visualization.
