Search-Signal Data Pipeline: $350K in Conversion Value
Case Study Summary
Organization: Emeritus
Industry: EdTech / Digital Marketing
Role: Senior Data Scientist
Impact Metrics:
- $350,000 in organic conversion value
- A reusable feature and modeling layer that other models can build on
- Systematic, data-driven keyword monetization instead of ad-hoc choices
People searching for programs leave behind strong signals of what they want. Those signals were valuable, but they weren't being captured at scale or in a form that models and marketing teams could use.
Challenge
Build a dependable way to ingest and process high-intent search signals at scale, then turn them into something the business could act on: which keywords and topics to invest in, and how to convert that organic interest into enrollments.
Approach
I architected an end-to-end data pipeline:
- Ingestion of high-intent search signals through Google APIs
- Processing at scale in SQL and BigQuery
- A reusable feature and modeling layer, so search signals become inputs for downstream models, not one-off reports
- Systematic keyword monetization, prioritizing the opportunities with the highest conversion value
Solution Overview
flowchart TD
A[Google APIs<br/>search signals] --> B[(BigQuery)]
B --> C[SQL processing<br/>& cleaning]
C --> D[Reusable feature<br/>& modeling layer]
D --> E[Keyword<br/>prioritization]
D --> F[Downstream<br/>ML models]
E --> G[Organic<br/>conversions]Results & Impact
- $350,000 in organic conversion value generated through systematic keyword monetization.
- A shared feature layer that made search intent available to other models and analyses.
Tech Stack
- SQL
- Google BigQuery
- Google APIs
- Python (Pandas)
- Google Cloud Platform
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