Rosè Crème Instagram Campaign Dashboard
A Power BI marketing dashboard built to evaluate Instagram campaign performance for a Montreal-based dessert brand. Because purchases were not tracked through Meta Pixel, New Messaging Contacts were used as the primary lead KPI to assess demand, campaign efficiency, and budget allocation decisions.
Power BI + DAX
Built using Power BI modeling and DAX-based KPI measures.
Lead-Based KPI Tracking
Used messaging contacts as the core measurable outcome instead of purchases.
Campaign Phase Comparison
Compared Winter, Product Expansion, and Valentine campaign phases.
Optimization-Focused Reporting
Designed to identify which campaigns to scale, redesign, or pause.
Project Overview
Rosé Crème is a Montreal-based dessert brand that acquires customers through Instagram advertising, where users click ads, send DMs, and complete payments via Interac. Since purchases were not tracked through Meta Pixel, the analysis focused on messaging contacts as the primary lead indicator. The dashboard was built to measure campaign efficiency, compare campaign phases, and support executive-level marketing decisions.
- Instagram ads → DMs → Interac order flow
- Messaging contacts used as the primary KPI
- Phase-based performance comparison
- executive overview + diagnostic deep dive
Key Business Questions
- Which campaign phase generated the highest demand?
- What was the blended cost per messaging contact?
- Which campaigns should be scaled versus redesigned or paused?
- How did conversion quality change with spend?
Business Requirements
The project was designed to answer practical marketing questions around efficiency, demand generation, and budget use across campaign phases. The dashboard needed to support both leadership-level reporting and detailed campaign diagnostics.
Measure Marketing Efficiency
Track spend, contacts, and conversion behavior across campaigns.
Compare Campaign Phases
Evaluate Winter, Product Expansion, and Valentine performance side by side.
Identify Underperformers
Detect campaigns generating spend without conversions.
Support Optimization Decisions
Highlight scale candidates and campaigns that should be redesigned or paused.
Architecture / Model Flow
The dashboard followed a simple reporting pipeline: source exports were cleaned in Power Query, mapped into a star schema, enhanced with DAX measures, and used to power two report pages. The architecture diagram shows the flow from data sources to Power Query, the fact and dimension tables, the model relationships, and finally the Executive Overview and Performance Diagnostic pages.
Star Schema Data Model
The reporting model uses a star schema with Campaign_Meta_Data as the fact table and Campaign Mapping plus DateTable as supporting dimensions. Relationships were defined as one-to-many from the dimension tables into the fact table to support clean filtering and stable KPI calculations.
Dashboard Pages
Executive Overview
KPI cards, spend by phase, contacts by phase, cost trend, and a narrative summary for leadership-level visibility.
Campaign Breakdown / Performance Diagnostic
Phase slicer, performance matrix, cost ranking, efficiency quadrant scatter plot, and dynamic insight area for campaign-level optimization.
Core DAX Measures
Spend
Spend = SUM('Campaign_Meta_Data'[Amount spent (CAD)])
Total New Messaging Contacts
Total New Messaging Contacts = SUM('Campaign_Meta_Data'[New messaging contacts])
Cost Per New Messaging Contact
Cost per New Msg Contact = DIVIDE([Spend], [Total New Messaging Contacts])
Messaging Conversion Rate
Messaging Conversion Rate = DIVIDE( [Total New Messaging Contacts], SUM('Campaign_Meta_Data'[Unique clicks (all)]))
Phase Spend
Phase Spend = [Spend]
Phase Contacts
Phase Contacts = [Total New Messaging Contacts]
Key Insights & Findings
Highest Volume
Product Expansion
Lowest CPA
Winter Campaign
Rising CPA
Valentine Phase
No Conversions
1 Campaign Flagged
Top Scales
High Conv. + Low Spend
What This Project Demonstrates
Business KPI Translation
Turned marketing goals into measurable dashboard KPIs.
Power Query Data Cleaning
Prepared campaign data for structured reporting.
Professional Data Modeling
Built a clean star schema for campaign reporting.
Marketing Optimization Analysis
Used spend, contacts, and conversion behavior to identify actionable campaign decisions.
Executive Storytelling Dashboard Design
Combined summary KPIs with deeper diagnostics for decision support.
Limitations
No Revenue / ROAS Tracking
- Interac payments are not tracked by Meta Pixel
Impact
- Cannot measure true ROI
Aggregated Exports Only
- No daily breakdown available
Impact
- Limited trend analysis
No CRM Integration
- No order or customer lifecycle data
Impact
- Incomplete funnel visibility
Future Improvements
