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

Category: Go-To-Market Analytics · Buyer Journey Modeling · Data Modeling · ETL Pipelines

🧩 Problem ?

Buyer intent signals were distributed across systems but lacked a unified framework to connect them with opportunity outcomes and revenue impact.

As a result:

• Engagement data was fragmented and difficult to interpret
• No clear mapping between buying agendas and pipeline movement
• Teams relied on volume-based signals, not conversion quality
• GTM teams lacked guidance on which messaging actually drives pipeline

🛠️ My Approach

1. Data Integration & Validation

• Integrated:
  • Snowflake**:** buyer intent signals, engagement events
  • Salesforce**:** opportunity lifecycle, stage progression
• Performed SQL-based validation to ensure:
  • Correct mapping between engagement events and opportunities
  • Temporal alignment across engagement to opportunity to conversion
  • Consistent categorization across buying agenda segments

2. Data Modeling & Agenda Structuring

• Standardized and normalized Buying_Agenda values:
  • Cleaned text fields (case normalization, delimiter handling, noise removal)
  • Split multi-agenda entries into structured categories
• Built a model linking:
  • Engagement events → Buying agendas → Opportunities
• Enabled:
  • One-to-many relationships between signals and opportunities
  • Aggregation at both agenda-level and segment-level

3. Analytical Framework (Core Logic)

Designed a framework to evaluate agenda effectiveness beyond volume metrics:

• Measured:
  • Engagement volume by buying agenda
  • Conversion tendency (agenda → opportunity)
  • Relative strength of agendas across segments