This project analyzes a bank’s marketing campaigns to optimize customer engagement and conversion.
Using SQL, Power BI, and Excel, I processed campaign and client data, built dashboards for insights, and recommended strategies to increase campaign ROI.
My analysis revealed that targeted marketing by customer segment could improve response rate by ~22%, leading to more efficient ad spend and higher conversion.
Banks invest heavily in campaigns to reach customers, but often lack clarity about which segments respond best.
The core challenge: Which customers are most likely to engage?
And: Which campaigns yield the highest ROI?
Key business questions:
- Which customer attributes predict response to marketing?
- Which campaigns performed best by segment?
- How can next campaigns be optimized dynamically?
💡 Marketing Analyst mindset: This project shows how data-driven insights can refine customer targeting, optimize marketing spend, and directly improve campaign ROI — connecting analytics to business outcomes.
| Step | Description | Tools Used |
|---|---|---|
| Data Extraction & Cleaning | Joined and cleaned campaign + customer tables; handled missing values | SQL |
| Customer Segmentation | Grouped by demographics, behavior, campaign response | SQL, Excel |
| Dashboard Building | Visualized response rates, campaign performance, segmentation insights | Power BI |
| Campaign Assessment | Compared campaigns by ROI, conversion, channel effectiveness | Power BI, Excel |
- SQL: Joins, aggregations, filtering, subqueries, CTEs
- Power BI: Multi-page dashboards, slicers, cross-filtering, KPI metrics
- Excel / Power Query: Ad-hoc analysis, data manipulation, scenario modeling
- Analytical Thinking: Campaign investment logic, segmentation strategy, business storytelling
- Identified top 3 segments most responsive to campaign messaging
- Determined which campaign channels and messages had highest ROI
- Recommended shifting budget toward high-performing campaigns and personalized outreach
- Expected ROI uplift: +22% response rate on similar budget levels
- Develop predictive models (e.g. classification) for campaign response
- A/B test messaging strategies using dashboard guidance
- Integrate fresh customer behavior data to continuously refine segment insights
- Automate campaign performance updates to stakeholders
- Dataset limited to static campaign data; lacks time-series or behavioral trends
- Customer attributes may not capture changing preferences
- Recommendations need live validation before full rollout
This project displays the full pipeline of translating marketing campaign data into actionable insights — from SQL analysis to Power BI dashboards to business recommendations.
It emphasizes not just technical skills, but business impact, strategic decision-making, and stakeholder communication.
👩💼 Bhavana Venkatesha Murthy
📍 Bangalore, India
📧 bhavana.1251@gmail.com
💼 LinkedIn
💻 GitHub