POWER BI · PYTHON · SQL · MARKETING ANALYTICS
Executive Marketing Intelligence Platform
Transforming fragmented marketing data into executive decisions with a Python-supported data pipeline, structured SQL model, and interactive Power BI reporting.
Role
Revenue Operations, Marketing Operations & BI Lead
Scope
Data engineering, semantic modeling, dashboard UX, KPI governance
Technology
Python, pandas, SQL, Power Query, DAX, Power BI
Portfolio demonstration: dashboard values and company data are synthetic. The case study illustrates the architecture, analytical methods, reporting practices, and executive decision framework without exposing confidential information.
Executive summary
Marketing leaders frequently have access to more data than insight. Campaign, CRM, advertising, web analytics, and financial data may all exist, but inconsistent naming, disconnected systems, and manual spreadsheet reporting prevent leadership from seeing a trusted view of performance.
I designed an end-to-end marketing intelligence framework that uses Python for repeatable preparation and validation, SQL for structured reporting models, and Power BI for executive decision support. The result is a scalable approach to attribution, campaign efficiency, revenue visibility, and KPI governance—not simply a collection of charts.
The business challenge
Leadership needed a reliable way to understand marketing investment, attributed revenue, channel efficiency, regional performance, and business growth without waiting for a manually assembled report.
- Marketing data lived across disconnected platforms and files.
- Campaign and UTM naming conventions were inconsistent.
- KPI definitions differed among Marketing, Sales, Finance, and leadership.
- Manual preparation slowed recurring reporting.
- Executives lacked a mobile-friendly, self-service view of performance.
Solution architecture
The reporting ecosystem separates ingestion, validation, modeling, and visualization so each layer can scale without overloading the dashboard.
1. Data sources
CRM, advertising platforms, GA4, marketing automation, finance systems, SQL databases, APIs, and controlled CSV exports.
2. Python pipeline
Extraction, deduplication, naming normalization, UTM standardization, null handling, validation, enrichment, and automated quality checks.
3. SQL model
Fact and dimension tables, historical snapshots, controlled keys, reusable business logic, and a reporting-ready star schema.
4. Semantic model
Power Query shaping, governed relationships, a reusable DAX measure layer, time intelligence, and documented KPI definitions.
5. Executive reporting
KPI scorecards, marketing ROI, campaign detail, geography, targets, revenue trends, drill-down analysis, and mobile views.
Python-supported data engineering
Preparation
- Import API, SQL, and file-based data.
- Standardize dates, regions, channels, and campaign names.
- Remove duplicates and enforce reporting keys.
- Handle missing values through documented rules.
Validation
- Flag missing campaigns and invalid spend values.
- Test attribution completeness and data freshness.
- Identify anomalies before dashboard refresh.
- Produce repeatable QA outputs for review.
Delivery
- Write clean reporting tables to SQL or governed files.
- Support incremental refresh and reusable pipelines.
- Reduce manual transformations inside Power BI.
- Preserve traceability from source to metric.
import pandas as pd
campaigns = pd.read_csv("campaign_performance.csv")
clean = (
campaigns
.drop_duplicates()
.assign(
campaign_name=lambda df: df["campaign_name"].str.strip().str.lower(),
channel=lambda df: df["channel"].str.strip().str.title()
)
)
assert clean["campaign_name"].notna().all()
clean.to_csv("reporting_campaign_performance.csv", index=False)Illustrative portfolio code showing the type of repeatable preparation used in the reporting workflow.
Executive dashboard experience
The dashboard design prioritizes business questions over visual complexity. The first view communicates overall health; deeper pages explain the drivers behind performance.
Leadership scorecards
Marketing spend, attributed revenue, ROAS, cost per conversion, revenue, profit, orders, margin, and growth.
Performance drivers
Channel efficiency, campaign trends, product or category performance, geography, targets, and period comparisons.
Self-service analysis
Year, region, campaign, channel, and category filters support follow-up questions during leadership meetings.
Governed KPI framework
Trusted reporting requires shared definitions. Each executive metric is documented with its business purpose, formula, owner, source, refresh cadence, and known limitations.
| KPI | Purpose | Example definition |
|---|---|---|
| ROAS | Evaluate advertising efficiency | Attributed revenue ÷ marketing spend |
| Cost per conversion | Compare acquisition efficiency | Marketing spend ÷ conversions |
| Revenue growth | Measure business momentum | Current-period revenue vs. prior period |
| Marketing contribution | Connect campaigns to commercial outcomes | Revenue associated with governed campaign and attribution rules |
Business value
Faster reporting
Reusable pipelines and modeled data reduce repetitive manual preparation.
Greater trust
Governed definitions and validation improve confidence in executive metrics.
Better decisions
Leaders can allocate resources based on campaign, channel, region, and revenue performance.
Self-service access
Interactive reporting reduces dependence on one-off spreadsheet requests.
Scalable architecture
Separated engineering and BI layers support new sources, metrics, and audiences.
Clear accountability
Ownership, refresh standards, and QA rules make reporting operationally sustainable.
What this case study demonstrates
Executive analytics leadership across data engineering, Python automation, SQL modeling, Power BI development, attribution, KPI governance, dashboard UX, and cross-functional decision support.