Product & Marketing Analytics

Project: Logicworks B2B Customer Engagement & Churn Prediction System

Tech Stack: Google Analytics, Looker Studio, Python ETL, Data Import

The Challenge

Logicworks needed to translate raw Client Portal analytics user clickstream data into actionable account-level insights. As Google Analytics’ standard web analytics tracks only individual users’ page views and events, the account management team lacked an aggregated view of which clients were highly engaged on the client portal versus which clients were unengaged and at risk of churn.

The Solution

I engineered a custom Google Analytics architecture that merged the web traffic data and CRM data:

  • Custom Events Instrumentation: Configured granular event tracking for high-value actions (e.g. "Enable Data Loss Prevention",  "Configure Backups Schedule") to capture engagement with the client portal.

  • Identity Aggregation Pipeline: Built an ETL process to map individual User_IDs to client entities, syncing CRM data into Google Analytics via Data Import. This allowed for aggregation of usage metrics at the desired client level, not just the user level.

  • Scoring & Visualization: Developed Looker Studio dashboards that calculated "Engagement Scores" for each client and for each portal feature.

The Impacts

  • Churn Prevention: Provided Account Managers with early warning indicators for clients with dropping engagement scores.

  • Revenue Growth: Identified cross-sell opportunities for clients based on their specific feature usage patterns.

Project: Competitive Pricing Intelligence & Conversion Analytics

Client: A Top-Tier Middle Eastern International Airline

Tech Stack: Google Analytics, Real-Time Competitive Price Data Feed

The Challenge The airline had good visibility into consumer behavior on their own website but was blind to the competitive market context. They could not determine the effects on consumer behavior from competitors offering alternative flights & prices at that exact moment.

The Solution

I implemented a sophisticated data enrichment pipeline that integrated a feed of timestamped competitive market data directly into the airline’s Google Analytics platform:

  • Contextual Data Modeling: Enriched user sessions in Google Analytics with custom dimensions showing competitors’ alterative flights & pricing that user would have seen if comparison-shopping multiple airlines’ sites.

  • Elasticity Analysis: Enabled the creation of comparative funnels (e.g. "Conversion Rate when we are the lowest price" vs. "Conversion Rate when Competitor X is 10% cheaper").

The Impacts

  • The airline moved from simple site traffic analysis to Price Elasticity Modeling, allowing them to optimize pricing strategies based on how price sensitivity actually impacted conversion funnels in real-time.

  • The airline was able to determine which routes serviced less price-sensitive consumers, thus allowing the airline to enjoy better margins on those routes.