🏦 Project Background
BrightWave Bank is a modern digital-first financial institution based in West Africa, serving individuals and small businesses through a wide range of banking solutions, including savings, loans, debit/credit cards, and investment services. The bank has a significant customer base made up of university students who typically open their first accounts while in school.
As these students graduate and move into the workforce, many continue using the same student accounts, often unaware of BrightWave’s upgraded offerings tailored for young professionals. The bank saw a strategic opportunity to deepen customer relationships and drive cross-sell/upsell efforts by identifying these transitioning customers and offering personalised financial products.
❓ Business Problem
How can BrightWave Bank identify former student customers who are now gainfully employed and receiving regular salary payments, in order to offer them upgraded account types, loans, and investment services?
🎯 Project Objectives
- Detect accounts originally opened by students that are now showing regular salary inflows.
- Segment these customers based on salary size and frequency of deposits.
- Enable the marketing team to launch tailored campaigns for high-potential customer segments.
- Provide a data-driven basis for future customer lifecycle engagement strategies.
🔧 Tools & Technologies Used
- SQL: For data extraction and transformation from the bank’s customer and transaction databases.
- Excel: For preliminary analysis and data cleaning.
- Power BI: For building a visually engaging, interactive dashboard used by the bank’s marketing and product strategy teams.
🗂️ Data Overview
- Customers Table: This includes account number, account type, employment status at opening, and demographic details.
- Transactions Table: Contains transaction dates, types (credit/debit), descriptions, and amounts.
🧠 My Approach
- Data Extraction (SQL)
- All customers who opened their accounts as “students”.
- Accounts with ≥10 salary credit transactions over the past 12 months.
- Customers with an average monthly salary of > 200,000k.
- Segmentation
- Average monthly salary.
- Frequency of salary deposits.
- Total transaction volume.
- Insights Generation & Recommendations
- Account upgrades for mid- and high-income earners.
- Credit card pre-approvals and tailored loan options.
- Financial advisory and investment product outreach for top-tier earners.
- Dashboard Development (Power BI)
- Total transitioning customers.
- Salary distribution insights.
- Customer segmentation visualisations.
- Engagement opportunity scores.
Queried the customer and transaction tables to identify:
Grouped qualifying customers into income brackets (Low, Mid, High) and calculated:
Provided a breakdown of each segment's financial behaviour and recommended:
Created a 4-page report dashboard highlighting:
📌 Key Deliverables
- SQL scripts for data filtering and salary pattern identification.
- Excel workbook for validation and cross-checking.
- Power BI dashboard for internal use by the marketing team.
- Strategic summary slide deck with recommendations
🌟 Outcome & Impact
The insights allowed BrightWave Bank’s marketing team to identify high-value young professionals within their existing customer base and initiate targeted outreach campaigns. Early feedback indicated a 12% increase in response rates to personalised product offers over traditional broad messaging.
💡 Key Learning & Reflection
This project reinforced the value of data-driven personalisation in financial services and gave me hands-on experience in aligning analytics with business objectives. It was a great exercise in storytelling with data and helped me strengthen my dashboard design and segmentation logic using real-world financial datasets.