👋 Hi, I'm Franco Cappanera
Data Analyst | Business Analyst | BI Engineer
🧪 Chemical Lab Technician Turned Data Analyst
💻 Excel, Tableau, SQL, Python R
🌄 Hiking, Running, Biking
📍 Lindon, Utah USA
Technical Skills
Tableau
Power BI
Looker
Project Management
SQL
Excel
Python
R
Projects
🍔 The Discount Mirage
Excel | Exploratory Data Analysis
◾ Real world-marketing campaign data
◾ Using only Excel for analysis and data visualization
◾ Utilized VLOOKUPS, Pivot Tables, Scatter Plots, & Bar Charts
◾ Provided a comprehensive write up to help the marketing team on their next campaign
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🏫 Evaluating School Success with Tableau Project
Tableau | Data Visualization
◾ Created dashboard evaluating 1,800 different schools' performance across 100's of features
◾ Used Scatter Plots, KPI's, Bar Plots, & Area charts to show performance differences
◾ Presented dashboard to stakeholders via recorded video
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💵 Financial Analysis of World Bank Loans
SQL | Exploratory Data Analysis
◾ Data-mined 1.2M real bank transactions to find financial outliers, patterns, & trends
◾ Used SQL clauses such as SELECT, WHERE, FROM, GROUP BY, AVG, MIN/MAX, SUM, AND, etc
◾ Created written report highlighting findings
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🏥 Analyzing Hospital Data with SQL
SQL| Exploratory Data Analysis
◾ Analyzed what affections hospital stay length in MySQL
◾ Created a histogram using SQL (weird, I know)
◾ Real data from over 101,000 hospital patients
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🏀 Analyzing NBA Data with Tableau
Tableau | Dashboarding
◾ Analyzed the 2022 NBA season statistics
◾ Created Stacked Bar Charts, Heatmaps, Treemaps & Bubble Charts
◾ Built a Tableau story & shared via written report
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About Me
👋 Hi, I'm Franco & welcome to my portfolio! Over the past few years, I’ve become passionate about data and the way it can transform decision-making. Coming from a background in accounting and finance, I’ve seen firsthand how turning numbers into insights can create real business impact.I’m proficient in analyzing data with:ExcelTableauSQLPythonRWhat excites me most is the intersection of accounting and data. I believe numbers aren’t just records on a ledger or rows in a database — they tell stories about how an organization operates, where it’s thriving, and where it can improve. By combining my accounting expertise with data-driven analysis, I aim to uncover insights that go beyond reporting: finding efficiencies in financial processes, identifying opportunities for growth, and helping teams make smarter, more confident decisions.This portfolio is a reflection of that vision — a space where finance meets analytics to create meaningful solutions. If you’d like to connect or discuss ideas, you can reach me at [email protected]
Previous Experience
💼 Staff Accountant & Financial Aid Manager
Snow Data Science · Internship · Oct 2023 - Jan 2024DeLaSalle High School · Nov 2024 – Present
◾ Manage the financial aid process, ensuring accurate application reviews and award distribution
◾ Maintain general ledger entries, reconciliations, and financial reporting support
◾ Provide families with clear communication regarding tuition and aid options📊 Accounts Receivable Specialist
Gurstel Law Firm · Mar 2024 – Oct 2024
◾ Reduced outstanding receivables through proactive client communication
◾ Reconciled accounts and produced detailed cash flow reports
◾ Resolved billing discrepancies and maintained accurate records📈 Bookkeeper / Accounting Specialist
U.S. Bank · Nov 2023 – Mar 2024 | Gabylo Accounting · Oct 2021 – Nov 2023
◾ Supported daily operations including reconciliations, accounts payable, and month-end close
◾ Processed invoices, payments, and bank transactions while supporting monthly reporting
The Discount Mirage: Why Discounts Fail and Loyalty Wins
It’s a Friday night, and after a long week, I find myself ordering food online just like countless others. The ease of tapping a few buttons—and the enticing discount popping up as I browse—definitely gets my attention. But it made me wonder: are these promotions truly the engine of growth for food delivery companies… or nothing more than a costly illusion?
Why This Project?
My curiosity about the food delivery sector, a booming yet relatively young industry, drove me to dig deeper. I noticed how heavily companies leaned on aggressive discounts to attract customers, but I questioned whether that approach was sustainable. This project gave me the chance to investigate, test assumptions with real data, and uncover insights that could help businesses focus less on short-term spikes and more on lasting customer loyalty.
What Readers Will Gain?
In this article, you’ll see why promotions often fail to deliver lasting results and how loyalty programs change the game. You’ll learn:
- Who the real core food delivery customer is, based on data (and why that matters for targeting).
- Why heavy discount users look valuable at first but rarely turn into loyal, high-value customers—the “Discount Mirage.”
- How loyalty programs flip the script, turning occasional buyers into repeat customers and stable revenue streams.
Dataset
I used a modified iFood case study (Brazil’s equivalent of DoorDash) with over 2,000 customer records. It included demographics, spending patterns, and behaviors, giving me the context to compare short-term promo spikes against long-term loyalty.
Who We're Really Selling To (ICP)
When you strip away the noise of discounts and one-off buyers, a clear picture of the real customer base starts to emerge. They’re not the teenagers chasing promo codes or the young parents juggling expenses. The bulk of the spending power lies with adults aged 36–50—who make up to 42% of our customers. This group is deep into their careers, pressed for time, and willing to pay for convenience.
Income sharpens the story even further. Households earning $80k+ spend nearly 4× more than those under $60k. Even the $60–79k range is leagues ahead of the lower brackets. This isn’t a surprise: more disposable income means food delivery becomes a habit, not a luxury.
And here’s the twist: our strongest segment isn’t the families with kids. It’s actually the no-kid households. They represent the majority of the customer base and consistently outspend households with children. With fewer budget constraints and simpler routines, they lean harder into the ease of ordering.
When you put it all together, the ideal customer profile is clear: 36–50 years old, middle-to-high income, and no kids at home. They’re the ones quietly driving most of the revenue—and the ones most likely to stick around if given the right loyalty program.
Problem 1: The Discount Mirage
Promotions are meant to create momentum. Flash a 20% discount and orders spike overnight. But when you zoom out, the picture isn’t so rosy. Our analysis shows that while discounts drive more frequent purchases, they don’t necessarily create higher-value customers.The chart below makes the point clear: customers who buy more often because of discounts don’t actually spend more in total—they simply spread their spend across multiple smaller orders.
In other words, discounts are a mirage. They make it feel like sales are growing, but in reality, they’re eating into margins without lifting overall revenue. Worse, they condition customers to wait for the next promo, eroding brand loyalty and long-term value.
Problem 2: Promotion Fatigue
The other danger with discounts is that they wear thin fast. The first time a customer sees 20% off, it feels like a win. The fifth time, it’s just expected. And once promotions become the norm, companies are trapped: they have to keep running them just to maintain the same level of engagement.Our data points in that direction. Frequent promo users weren’t just less profitable in total spend—they also showed signs of slowing down. The more discounts they received, the less impact each new one had. It’s a classic case of diminishing returns: the “boost” from promos kept shrinking with each round.This is where the real cost of promotion fatigue shows itself. Instead of building long-term habits, discounts encourage short-term chasing. Over time, customers get harder to excite, margins get thinner, and companies spend more to stand still. It’s not a growth strategy—it’s a treadmill.
Conclusion: From Discounts to Durable Growth
This analysis makes one truth hard to ignore: discounts create noise, but loyalty creates value. The data shows us who really drives food delivery revenue—36–50 year-olds, higher-income households, and customers without kids—and why chasing discount-driven buyers is a losing game. Promotions may inflate order counts in the short term, but they don’t lift total spend or build lasting relationships. Loyalty programs, on the other hand, give your best customers a reason to stay and spend more over time.For food delivery companies, the choice is simple: keep investing in promotions that burn cash and breed dependence, or shift toward loyalty strategies that build sustainable, compounding growth. The half-life of a promo is short. But loyalty? That’s where real customer lifetime value is forged.Want to talk more? Check out my LinkedIn article:
Don't hesitate to reach out!
🏫 Evaluating School Success with Tableau Project
In the state of Massachusetts, there are approximately 953,748 high school students, which is nearly a million kids! This means that decisions regarding high school education affect one-seventh of the state’s population — that’s almost 7 million people.
Background
The Massachusetts Department of Education requested an analysis of their school data. The Superintendent was interested in understanding the relationship between class size and college attendance, as well as identifying the top-performing schools in math and the schools that may be struggling the most.Using Tableau, I created an interactive dashboard to uncover patterns in enrollment, graduation rates, and proficiency levels across the state.You can explore the interactive visuals here: 👉 Tableau DashboardAnd review the raw data here: 👉 Dataset
Analysis
From the analysis, I learned that last year, 162,137 students did not graduate, while 791,610 students successfully graduated.Another 205,818 students did not attend college after finishing high school, while 585,791 students pursued higher education.Looking at class size, the average across the state is 18 students per class. Interestingly, some of the schools with the highest college-going rates actually had larger class sizes — suggesting that while class size plays a role, economic background and school resources may be even stronger contributors to student outcomes.
When examining math proficiency, I looked specifically at fourth-grade MCAS scores by district. Only a few districts surpassed the 50% proficiency threshold, while the majority fell below.The top five districts in math proficiency included Hingham, Winchester, Lynnfield, Manchester Essex Regional, and Sherborn. Studying these districts more closely could help uncover best practices that could be shared statewide.
On the other end, several schools are facing urgent challenges with graduation and college enrollment. These include Springfield Public, Boston Charter, Whaling City Junior, Springfield High, and Phoenix Academy.A closer look reveals that struggling schools often serve communities with higher concentrations of economically disadvantaged students. On average, 77% of students in these struggling schools are economically disadvantaged, compared to only 13% in the top-performing schools.
Conclusion
I recommend that Massachusetts explore knowledge-sharing partnerships, where high-performing schools collaborate with lower-performing schools to exchange successful strategies.Rather than focusing solely on building new schools, resources could be better spent by investing in programs that directly support disadvantaged students.Finally, forming partnerships with local colleges and universities could strengthen the transition to higher education for students in struggling schools.If you enjoyed this project, feel free to connect with me on LinkedIn — I’ll be sharing more data-driven insights and visualizations soon!This project was created for educational purposes as part of my Tableau practice portfolio.Want to talk more? Check out my LinkedIn article:
Don't hesitate to reach out!
Lending, Growth, and Gaps: Latin America's Story Through World Bank Data
Why THIS Project?
Growing up in Buenos Aires, I’ve seen how access to international financing can shape opportunities—and how mismanagement can hold entire regions back. When I joined the Data Career Jumpstart Bootcamp, I knew I wanted to bring that perspective into my work.This project began as a simple assignment: analyze a large dataset using SQL. But when I came across the World Bank’s IDA data for Latin America, it stopped feeling like just an exercise. Here was a chance to look beyond headlines and uncover the actual patterns of how my region borrows, repays, and evolves over time.What started as a technical challenge quickly turned into an exploration of Latin America’s long-term relationship with development finance—a story about growth, priorities, and resilience. For me, this project is as much about data as it is about understanding the forces shaping Latin America’s future.
Here's Why You Should Keep Reading
Latin America’s economic story is often told in sweeping terms—growth rates, crises, reforms—but the real picture lies in the flow of financing over time. By analyzing the World Bank’s IDA data, I uncovered trends that reveal who in the region is borrowing the most, how repayment behaviors have shifted, and where funding gaps are emerging.This article isn’t just about SQL queries—it’s about transforming raw data into insight:- If you’re learning data analytics: you’ll see how to go from a massive dataset to clear, actionable visuals.
- If you’re curious about global finance: you’ll discover what long-term lending says about a region’s priorities and resilience.
- And if you care about Latin America: you’ll get a data-driven perspective on the opportunities and challenges shaping its future.
What & Where of Dataset
The data for this project comes from the World Bank’s IDA (International Development Association) Statement of Credits, Grants, and Guarantees—a comprehensive dataset with 1.4M+ rows of historical lending activity. It tracks commitments, disbursements, and repayments across low-income countries, converted into USD for comparability.For this analysis, I focused on Latin America and the Caribbean, filtering the dataset down to borrowing, disbursement, and repayment patterns across the region. As of August 23, 2025, the 2025 data is still incomplete, so values for that year represent partial activity up to the time of reporting.I used SQL (via CSVFiddle) to clean, filter, and aggregate the raw data, then transformed those queries into interactive visuals with Tableau. To present my SQL logic clearly, I also used Carbon to create clean, shareable code snippets.This combination of data engineering + visualization turned a massive dataset into a narrative about how Latin America engages with long-term development finance—and what that reveals about its priorities and resilience.
1. Following the Money: How Much Has Flowed Into Latin America?
To start, I wanted to see the big picture — how much has been disbursed, repaid, and still owed by Latin America over time. Using SQL, I aggregated yearly totals of disbursements, repayments, and outstanding balances.
Latin America’s IDA disbursements have grown steadily, peaking in 2024 before a sharp dip in 2025 (due to partial data). Net flows remain positive, showing sustained engagement, but repayment ratios are declining — a sign of increasing long-term commitments.
2. Who's leading the Borrowing Race in Latin America?
To understand which countries drive these trends, I broke down IDA flows by country. Using SQL, I ranked nations by total amounts borrowed, disbursed, and repaid since 2011. This highlights who the key players are in leveraging international development finance.
Bolivia emerges as the largest recipient, with nearly $400B disbursed. Honduras and Nicaragua follow closely, both with significant outstanding dues. Haiti’s profile is unique—while its disbursements are lower, repayment remains minimal, indicating more grant-based support. This distribution reveals varying financial strategies: some countries leverage repayable credits aggressively, while others rely more on non-repayable aid.
3. How Does Latin America Compare to the Rest of the World?
To see how leading borrowers evolved over time, I filtered the dataset to Latin America’s top five IDA recipients and tracked their yearly disbursements. This approach highlights which countries sustain long-term borrowing and how patterns shift in response to regional needs.
The top five borrowers show a consistent upward trend, reflecting enduring reliance on concessional finance. Bolivia and Honduras dominate, while Haiti’s surge post-2015 underscores increased aid during recovery phases. The 2024 peak followed by a 2025 drop is due to incomplete data (as of Aug 23, 2025) rather than a sudden decline.
4. How does Latin America Compare Globally?
To understand how Latin America fits into the global picture, I compared its annual IDA disbursements and repayments against the rest of the world. This reveals not just absolute growth but whether the region’s importance in global development finance is expanding or shrinking.
While total disbursements to Latin America have grown, the region’s global share has fallen from over 3.5% in 2011 to under 2.7% in 2025 (partial data). This suggests other regions are capturing a larger portion of IDA resources, reflecting shifting global priorities. Latin America remains a steady borrower, but no longer commands the same relative weight in concessional financing.
Final Thoughts: Data with a Personal Lens
What started as a bootcamp project became something much more personal. Growing up in Buenos Aires, I’ve seen how financing—or the lack of it—can change the course of entire communities. Behind every number in this dataset are roads, schools, health programs, and people whose lives depend on whether funds arrive on time, whether debts are sustainable, and whether a region is trusted to keep moving forward.The data shows Latin America’s resilience: steady borrowing, growing commitments, and long-term engagement with development finance. But it also reveals a challenge: our share of global resources is shrinking. As competition for funding grows, the question becomes not just how much we can access, but how effectively we can turn those funds into lasting progress.For me, this project reaffirmed why I’m drawn to data analytics. It’s not just about queries or dashboards—it’s about using data to tell stories that matter. And this is one of them: a story of a region striving to build a better future, one line of credit at a time.Want to talk more? Check out my LinkedIn article:
Don't hesitate to reach out!