This project delivers a business-focused analysis of marketing funnel performance and revenue efficiency using a snapshot dataset.
It is designed to answer a critical revenue question:
Where are we losing conversions, and which channels are truly driving revenue—not just leads?
By combining funnel analysis, channel performance, and cost efficiency, this dashboard enables data-driven decisions across Marketing, Sales, and Revenue Operations (RevOps).
The goal is to identify:
- High-performing acquisition channels
- Funnel bottlenecks impacting conversion
- Revenue inefficiencies and optimization opportunities
Many organizations optimize for lead volume, but lack visibility into:
- Where leads drop off in the funnel
- Which channels generate revenue vs. just traffic
- How efficiently marketing spend converts into customers
This often results in:
- Misallocated marketing budgets
- Low-quality pipeline
- Revenue leakage across funnel stages
This project uses a snapshot-based dataset, where leads are observed at a single stage at a given point in time.
- Funnel stage distribution analysis
- Conversion efficiency benchmarking
- Channel-level revenue contribution
- Customer acquisition cost (CAC) evaluation
- Lead progression tracking over time
- Time-to-conversion analysis
- Funnel velocity measurement
- Cohort-based lifecycle insights
⚠️ Interpretation Note: Conversion rates represent stage-level distribution, not true lifecycle conversion.
The dataset includes:
- Funnel Stages:
Lead → MQL → SQL → Customer - Marketing Channel
- Campaign
- Cost
- Revenue
- Region
- SQL (BigQuery) → Data extraction & KPI computation
- Python (Pandas) → Data cleaning & transformation
- Power BI → Dashboard development & visualization
| Metric | Purpose |
|---|---|
| Total Leads | Top-of-funnel volume |
| Total Customers | Revenue-driving outcomes |
| Conversion Rate | Funnel efficiency |
| Revenue | Business performance |
| Customer Acquisition Cost (CAC) | Cost efficiency |
| Revenue per Customer | Value generation |
The largest drop-off occurs between:
MQL → SQL (47.5%)
This indicates a critical breakdown in:
- Lead qualification quality
- Marketing-to-sales handoff
- Sales acceptance criteria
- Sales teams spend time on low-quality leads
- High pipeline leakage before opportunity stage
Referral
- Highest conversion rate
- Highest revenue contribution
- Lowest CAC
- Strongest overall efficiency
- Consistent performance across funnel stages
- Scalable and cost-effective
- Low conversion rate
- Weak revenue contribution
- High relative acquisition cost
LinkedIn may be:
- Poorly targeted
- Attracting low-intent users
- Inefficient relative to cost
A key insight from this analysis:
High lead volume does not equal high revenue performance
- Some channels generate many leads but low revenue
- High-performing channels balance conversion + value + cost efficiency
- Customer growth increased from 4 → 10 (+150%)
- Revenue trends improve over time
Trends reflect aggregate snapshot comparisons, not cohort progression.
- Revenue by channel
- CAC comparison
- Customer distribution
- Funnel stage breakdown
- Conversion rates
- Drop-off visualization
- Customer growth
- Revenue trends over time
- Refine MQL criteria
- Introduce stricter scoring before SQL stage
- Increase investment in Referral and Email
- Reduce or optimize LinkedIn spend
- Align marketing and sales definitions of qualified leads
- Improve lead routing and follow-up timing
- Prioritize channels that generate high-value customers
- Track revenue per lead, not just lead count
Raw Data
↓
Data Cleaning (Python)
↓
SQL KPI Computation
↓
Funnel & Revenue Analysis
↓
Power BI Dashboard
↓
Business Insights & Recommendations
SELECT
funnel_stage,
COUNT(DISTINCT lead_id) AS total_leads,
ROUND(
COUNT(DISTINCT lead_id) * 100.0 /
SUM(COUNT(DISTINCT lead_id)) OVER (),
2
) AS stage_percentage
FROM funnel_data
GROUP BY funnel_stage;
SELECT
channel,
SUM(revenue) AS total_revenue,
COUNT(DISTINCT lead_id) AS total_leads,
ROUND(SUM(revenue) / COUNT(DISTINCT lead_id), 2) AS revenue_per_lead
FROM funnel_data
GROUP BY channel
ORDER BY total_revenue DESC;
To evolve this into a production-level analytics solution:
- Implement cohort-based dataset with timestamps
- Add funnel velocity (time-to-conversion)
- Build lead scoring model
- Introduce multi-touch attribution
- Integrate with CRM (e.g., Salesforce)
- Automate data pipeline (ETL)
- Funnel Analysis
- Revenue Analytics
- Customer Acquisition Cost (CAC) Analysis
- Channel Performance Evaluation
- Data Cleaning & Transformation
- SQL Analytics
- Dashboard Design (Power BI)
- Business Insight Generation
- RevOps Thinking
This project demonstrates the ability to:
Translate business problems into analytical frameworks Identify revenue leakage across the funnel Evaluate marketing ROI at channel level Deliver actionable insights for growth teams
Revenue growth is not driven by more leads — it is driven by better conversion, smarter channel investment, and efficient funnel management.
Data Analyst | Business Intelligence | Revenue & Operations Analytics
GitHub: https://github.com/Richie-Rokka