📊 Tableau Dashboard & Call to Action

As I started to build out the Tableau dashboard, it became clear that the real value in this project was not just generating SQL queries, but creating a Visual Tool that could be used by stakeholders to make better decisions in real-time: in other words, whoever finds numbers and graphs difficult to read and understand.

⚽ Main Goals

Build and refine the Tableau Dashboard
Add filters and interactivity for better insights Implement call-to-action for customer retention
Focus on Tier-1 customers as they are the most valuable, and identifying the high-risk ones

📋 Step by Step

📍 Step 1: Designed the KPI Summary tab, key metrics like loan portfolio size, average balance, and high utilization 📍 Step 2: Built the Customer Overview tab, showing account balances and behaviors by account type
📍 Step 3: Integrated a Credit Card Health tab to highlight customers above 80% utilization 📍 Step 4: Added the Customer Value bubble chart, to compare tier lists and net positions of each client

🔑 Key Insights

Tier-1 customers (high-value, low-risk) emerged as the main focus for retention.
These customers use credit more frequently, despite their negative balances, their income are safer for long-term profitability.
The dashboard visualizes this, allowing teams to focus on creating customised product and services for these clients.

📊 Tableau Visual: CI Dashboard

Customer Value by Tier The Dashboard shows the KPIs table, customer segmentation, credit card types, loan type an status barchart seen previously, and the Customer Value bubble chart.

🔗 View the Live Dashboard 👉

🏆 Final Thoughts: The Other Side of the Dashboard

After being inside this project, I can say one thing: building dashboards and writing SQL queries was not easy, but the real challenge is understanding what kind of world that data comes from.

This dataset, as mentioned before, comes from the U.S. banking market, and you can feel it; why? Americans have a completely different relationship with money: credit is not a danger, it’s a tool: they borrow to invest and build; the debt, for them, is justified as part of growth.

In Europe (especially in Southern Europe) it’s the opposite story: we save before spending, we find it scary and dangerous.

So while analyzing this dataset, I genuinely found myself thinking:

“If these were European customers, half these behaviors would never appear.”

That’s the interesting part: the same SQL queries, the same segmentation logic, but a completely different financial psychology behind the numbers, a context that has to be understood.

That’s exactly the reason why I’m passionate about this work, because I start with columns, rows, queries and dashboard, and then I end up questioning why people are doing this or that!!

💬 Next step? Maybe I’ll rebuild this entire analysis using European-style banking data, to see what happens when “credit” becomes the exception, not the rule.

⚡ Credits

© 2025 Pietro Di Leo. From Operations to Data, one Commit at a Time.