The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
AI Generated Data Analysis
Problem: Weekly call-center ops data needed actionable synthesis; manual analysis was slow and inconsistent.
Solution: Built an LLM data analysis agent: ingest call data, run NLP + aggregations, detect anomalies, and generate a weekly executive PDF with insights, metrics, and recommended actions Metrics Automated weekly PDF; similar setups cut analysis time ~90% and improved decision turnaround (one related client saw 17% revenue lift over 7 months)
Tech: Python ETL, Pandas, NLP, GPT-4o, Airflow
Result: Consistent reports that surface issues fast and drive quicker corrective action.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
I tried using ChatGPT 6.1 Sol to turn Shan Hai Cuties ⛰ 🌊✨ into interactive holo stickers, inspired by the playful feel of collectible holographic stickers.
So creative, looks amazing 🎉
Telecom Customer Churn Analysis
Python • Pandas • Matplotlib • Jupyter Notebook
Overview
Cleaned and analyzed customer-level telecom data to identify patterns associated with customer churn across contracts, tenure, pricing, service adoption, and reported churn reasons.
What I Built
Using Python and Pandas, I investigated missing values and data quality issues, prepared the dataset for analysis, performed exploratory analysis, and created visualizations to communicate the most important patterns.
Rather than simply removing unusual or incomplete records, data-quality decisions were validated against related fields and documented to preserve the integrity of the analysis.
Key Findings
Overall churn was approximately 27% • Month-to-month customers had substantially higher churn than customers with longer contracts • Churned customers had higher average monthly charges • Internet customers without Online Security showed higher churn • Competitor-related factors represented a major group of reported churn reasons
Tools: Python • Pandas • Matplotlib • Jupyter Notebook
Want to see the analysis?
View the complete notebook and technical walkthrough on GitHub → https://github.com/jamietran8818/business-data-analytics-portfolio/blob/main/python-customer-churn-analysis/README.md
Built an AI-Powered Real Estate Automation System 🏠🤖
Real estate businesses often lose leads because of slow responses, manual follow-ups and scattered customer data.
I built a system that automates the complete lead journey:
✅ AI Chat Assistant — answers FAQs & qualifies leads
✅ AI Lead Capture — collects customer requirements
✅ CRM Automation — automatically saves leads
✅ Appointment Booking — schedules site visits
✅ Google Calendar Integration
✅ Automated Email Notifications
✅ Automated Lead Follow-ups & Reminders
✅ AI Voice & Chat Support
The goal:
Capture more leads → Respond faster → Automate follow-ups → Book more appointments → Manage everything from one system.
I also build custom AI automation systems for real estate agencies, clinics, gyms, salons and other service businesses.
📩 Looking to automate your business? Let's talk.
The AI chat assistant handling FAQs and qualifying leads removes a lot of manual back and forth for agents. Syncing the lead capture straight into the CRM keeps data tidy.