Marketing Performance & Budget Optimization Dashboard

Multi-channel marketing analysis · United States

Key Metrics

Dashboard

Power BI dashboard showing revenue trends and different metrics.

The Problem

A marketing team was running campaigns across multiple paid channels, but leadership had no visibility into which campaigns were truly profitable and which were wasting budget. Decisions were based on traffic volume rather than financial efficiency.

As the marketing analyst, the task was to evaluate campaign performance by calculating key KPIs — CTR, conversion rate, CPA, ROAS, and ROI — then identify which channels generate efficient returns, which underperform, and how budget should be redistributed.

Tech Stack

Key Features

• Executive Summary Layer High-level KPI cards with automated trend insights (e.g., "Google Ads Highest CPC").
• Normalized Performance Tracking A custom Average Daily Revenue trend line that corrects for calendar artifacts like the February dip.
• Hierarchical Deep-Dives Matrix views allowing stakeholders to drill down from broad demographics (Age) to specific personas (Tech Enthusiasts).
• Geographic Heatmaps Performance-coded mapping to identify profitable markets vs. high-cost regions.

Top Insights & Recommendations

• Scale the Power Persona Women aged 35–44 (Tech Enthusiasts) represent the highest efficiency with an 8.12% CVR and 6.04x ROAS.
• Budget Reallocation Houston identified as a high-potential market where CPA is $10 lower than the New York average.
• Efficiency Caps Recommended a $645 CPA ceiling for the Men 25–34 Health segment to protect net margins.
• Targeting Audit Data proved "All Ages" broad targeting significantly underperforms compared to niche demographic segments.
• Double Down on Facebook Facebook shows strong CVR relative to cost — increasing allocation here improves overall portfolio efficiency.

Data Transformation (Python)

To ensure data integrity, a Python pipeline was used to:

• Handle missing values (NaN and NaT) to prevent visual artifacts in Power BI.
• Validate data types for all financial metrics.
• Generate high-density Seaborn heatmaps to cross-validate conversion clusters.