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Strong breakdown of churn drivers.
Have you looked at cohort-based churn instead of aggregate churn rate?
Sometimes retention patterns by acquisition month reveal structural issues earlier than overall churn trends.
Great catch! I did consider a cohort-based view to catch those acquisition month trends. For this specific dashboard, I prioritized a high-level breakdown of churn drivers to keep it digestible for stakeholders, but I agree that a cohort layer would add much-needed depth to the 'when' behind the 'why.'
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Project Title:
Sales Performance Dashboard Using Power BI
Description Example:
Designed an interactive Power BI dashboard to track sales performance, revenue trends and monthly KPIs. Automated data transformation using Power Query and built dynamic Excel models for reporting accuracy.
Tools Used:
• Microsoft Excel
• Power Query
• Power BI
Clean layout.
Curious — did you design the data model in a star schema, or were the relationships handled directly inside Power BI?
In my experience, long-term maintainability depends more on the model layer than the visuals.
Most dashboards fail before they are even built.
Not because of bad charts.
Not because of wrong formulas.
Because the data underneath is structurally broken.
Here’s what I see often in small businesses:
• Dates stored as text
• Multiple columns for the same metric
• Inconsistent naming (Revenue / Sales / Total Sales)
• Manual copy-paste every week
• No clear data flow
Then they ask:
“Why doesn’t this dashboard update properly?”
A dashboard is just a mirror.
If the data structure is messy, the reflection will be distorted.
Before I build any report, I focus on 3 things:
Standardized column logic
Single source of truth
Repeatable data flow (no manual dependency)
Clean structure → Reliable metrics → Better decisions.
If your reporting feels fragile, the issue usually isn’t Excel.
It’s the foundation.
Completely agree. Dashboards amplify whatever foundation they sit on — messy data → misleading clarity. Standardization and single source of truth are massively underrated.
The analysis provides an overall assessment of a lending portfolio’s performance, highlighting strengths in stability and customer behavior but also several areas needing improvement. It identifies moderate credit‑model accuracy, data‑quality issues, and uneven risk across customer segments and loan types. Seasonal patterns, acquisition‑channel differences, and delinquency behavior further shape the portfolio’s risk profile.
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