Tools Used: Snowflake, Salesforce, Qlik Sense, SQL (Advanced), Qlik Script

Category: Marketing Analytics · Attribution · Data Modeling · ETL Pipelines

🧩 Problem ?

Marketing performance was evaluated using raw volume metrics (responses, influenced opportunities, attributed value), which led to misleading insights:

• Long-running campaigns appeared artificially strong
• Short, high-impact campaigns were undervalued
• Inconsistent data across systems reduced trust in reporting

There was no standardized way to measure true marketing impact across time and funnel stages.

Note: Created this rough diagram to convey my logic to non-technical stakeholders

🛠️ My Approach

1. Data Integration & Validation

• Integrated campaign and response data from Snowflake with opportunity and revenue data from Salesforce.
• Used SQL to validate:
  • Record consistency across systems
  • Temporal alignment (campaign, response, opportunity timelines)
  • Missing or duplicate mappings

2. Data Modeling

• Built a unified data model linking:
  • Campaigns & assets
  • Response events
  • Opportunity lifecycle and revenue
• Enabled analysis across:
  • Campaign-level performance
  • Asset-level contribution

3. In-Market Days Calculation

For each campaign, the In-Market Duration was calculated as:

In-Market Days = End Date − Start Date