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Bridging the Gap: How B2B SaaS Startups Leverage Interactive Analytics to Reduce Customer Churn
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Velox
Built for the Bubble #PromptAndCircumstance Challenge.
Velox is a modern operations dashboard that gives teams a single place to manage projects, track business metrics, and monitor performance in real time.
Instead of juggling spreadsheets, analytics tools, and task boards, teams can visualize their entire workflow from project pipelines to revenue trends in one clean workspace.
Built on a Bubble AI-generated foundation and taken the rest of the way by hand.
Started with a single prompt to Bubble AI.
Bubble AI generated the initial pages, database structure, and dashboard layout.
From there, I refined the workflows, analytics, navigation, and project management experience, then redesigned the interface into a clean, modern dashboard with interactive charts, KPI cards, Kanban boards, and responsive layouts.
Features
1. Real-time business dashboard with KPI tracking
2. Revenue analytics and performance trends
3. Kanban-style project management
4. Task prioritization and workflow tracking
The goal was to transform Bubble AI's starting point into a polished operations platform that feels like a production-ready SaaS product rather than a dashboard template.
This was my first time building with Bubble AI, and it was impressive to see how quickly a single prompt became a functional application.
Built a SaaS concept this week. Luma, a tool that scans incoming interactions and sorts them by conversion potential.
Tried to think through it as a real product, not just a landing page. Hero, dashboard, the logic behind why someone would actually open this every morning.
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Prokhorov®
helloprokhorov.com
Love this product-first approach! Designing with the actual user logic and daily routine in mind makes a massive difference, and it really shows here. The layout is incredibly clean, professional, and built for real business value. Brilliant work on Luma! 🚀💼
Project: Velocity SaaS – Revenue Turnaround Analysis
Type: Independent portfolio project
Dataset: Synthetic B2B SaaS, 2,000 accounts / 2.2M rows, benchmarked against ProfitWell/OpenView
What I did:
MRR waterfall analysis, churn segmentation by plan/industry/channel
Usage decay / silent churn risk modeling
Logistic regression churn predictor: 98% recall, 70% precision, 0.82 F1
Built executive Looker Studio dashboard + Streamlit churn predictor
Outcome: $411K silent MRR at risk identified, CS-ready prioritized account list
Stack: SQL, DuckDB, Python, Pandas, Scikit-learn, Looker Studio, Streamlit
Live app: velocity-saas-churn-predictor.streamlit.app
Repo: github.com/SURAJRAJPUT2006
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