GA / Platform Data Log Sequence Analysis
Bridging Google Analytics and Platform Data to drive product improvements
Project Duration
Q1 2024 - Q2 2025
My Role
As the Lead UX Researcher, I spearheaded the strategy for integrating Google Analytics and Platform Data. I led the analysis of user behavior, including user path analyses, custom metric development, and cohort analysis, to identify key pains and opportunities. My work in designing actionable dashboards and delivering data-driven recommendations directly supported product roadmap prioritization and UX enhancements.
Team
Project Overview
This quantitative UX research initiative integrated Google Analytics (GA) session data with Platform Data events for a unified view of user behavior. Through behavioral data modeling, user path analysis, and custom engagement metrics, we achieved significant outcomes: user engagement scores increased by **~11% (Q4)**, and the proportion of returning daily active users grew by **~34 percentage points** (from ~28% to ~62%), indicating enhanced retention. The platform demonstrated strong user stickiness with an **~80% WAU/MAU ratio**. Furthermore, integrating data into actionable dashboards saved analysts **10+ hours weekly** in manual reporting. These insights, including identification of the main dashboard as a key Platform Data hub and dominant user interaction patterns (clicks, views, scrolls), directly informed UX improvements and product strategy.
Methods
Tools
The Challenge: Unifying a Fragmented User View
Product and marketing teams operated with a fractured view of the user journey. Web analytics (GA) tracked in-product user behavior, while Platform Data and product databases held in-app engagement data. This separation made it nearly impossible to understand the complete user lifecycle, measure true engagement, or confidently attribute product success to specific marketing efforts.
Disconnected User Journey
How did users *really* move through the platfrom?
Disconnected User Journey
Mapping the end-to-end user journey from initial touchpoints (GA) to in-product feature usage and Platform Data milestones was fragmented and unclear.
Measuring True Engagement
What did 'engagement' *truly* mean for our product?
Measuring True Engagement
Relying on isolated metrics (e.g., GA page views vs. Platform Data activity logs) provided an incomplete picture of genuine user engagement and product stickiness.
Attributing Impact Accurately
Which efforts *actually* led to valuable user actions?
Attributing Impact Accurately
Difficulty in correlating website behavior (GA) with specific in-product behaviors, feature adoption, and long-term retention (Platform Data).
The Cost of Siloed Data
- •Inability to identify critical drop-offs across the full user lifecycle.
- •Marketing and product strategies were often misaligned or based on incomplete data.
- •Wasted resources on unproven initiatives or poorly prioritized features.
- •Limited understanding of what truly drove long-term user retention and value.
Our Research Aimed To:
- •Create a unified data model integrating GA data points and Platform Data behavioral data.
- •Quantify user paths, funnel conversions, and drop-off points across systems.
- •Develop holistic engagement metrics reflecting true product interaction.
- •Enable data-driven decisions for UX, product, and marketing strategies.
Bridging the Data Divide for Holistic Insights
To overcome these critical blind spots, this research initiative focused on integrating disparate data sources. The goal was to build a comprehensive, unified view of the user journey, enabling accurate measurement of engagement and the direct attribution of outcomes to specific interactions, thereby empowering smarter, data-informed decisions across the organization.
Key Research Questions
To bridge the gap between siloed GA and Platform Data data, our research was driven by critical questions aimed at creating a unified understanding of user behavior and its impact.
Understanding User Navigation & Interaction
To map and analyze how users move through and interact with integrated GA and Platform Data/features.
- Q1:
How do users navigate between Platform Data and platform features?
- Q2:
What session behaviors (e.g., page sequences, time on page, clicks) indicate areas of friction or high value within the integrated data environment?
- Q3:
Which specific user roles (identified via Platform Data) show the highest engagement with particular platform features (tracked via GA)?
Measuring & Predicting Engagement
To define, measure, and predict user engagement and satisfaction based on combined data.
- Q1:
How can we develop a robust metric for 'product stickiness' that leverages both GA session data and Platform Data activity across different user segments?
- Q2:
What quantifiable correlations exist between specific Platform Data lifecycle events (e.g., onboarding completion, support ticket resolution) and overall user satisfaction or engagement patterns observed in GA?
- Q3:
Based on current cross-platform usage patterns (GA & Platform Data), can we build a model to predict future user engagement, potential churn, or likelihood of upselling?
Guiding Methodological Choices for Integrated Insights
These research questions were fundamental in guiding our methodological choices. They necessitated an approach centered on data integration (Behavioral Data Modeling), pathway analysis (User Path & Funnel Analysis), nuanced engagement measurement (Custom Engagement Metric Development), and longitudinal tracking (Cohort-Based Analysis) to provide a truly holistic view of user behavior across systems.
Research Methods
We used these quantitative techniques to turn raw GA and Platform Data data into actionable insights:
Behavioral Data Integration & Modeling
Integrated GA & Platform Data for a complete user view
Designed a unified data model integrating Google Analytics behavioral logs with Platform Data events, enabling comprehensive analysis of cross-platform user journeys and interaction patterns.
What this means: Connected GA web logs and Platform Data user actions into one model. This provided a full user story, linking marketing campaigns (GA) to feature adoption (Platform Data).
Data Integration Flow
User Path & Funnel Analysis
Mapping user journeys and identifying drop-offs
Quantitatively mapped common user navigation paths and conversion funnels by analyzing sequences of page views and Platform Data events to identify high-traffic routes, critical drop-off points, and areas of friction.
What this means: Tracked user steps from landing page to conversion, visualizing pathways and identifying funnel drop-offs (e.g., a 60% drop at one stage) to prioritize UX fixes.
Funnel with 60% Drop-off Identified
Custom Engagement Metric Development
Creating tailored scores for user interaction depth
Developed and validated custom engagement scores by algorithmically weighting session duration, feature interactions, and key Platform Data event completions to quantify user involvement and platform stickiness.
What this means: Developed a composite engagement score combining GA session duration, Platform Data feature use frequency, and GA interaction depth. This offered a better measure of true engagement, identifying power users and at-risk segments.
Inputs to Engagement Score
Cohort-Based Retention & Stickiness Analysis
Tracking user groups\' long-term engagement
Conducted cohort analysis on key user segments to measure product stickiness (e.g., DAU/MAU ratios) and track long-term retention trends, identifying factors influencing sustained usage.
What this means: Grouped users by start date (cohorts) and analyzed their activity over time. This showed retention and stickiness (DAU/MAU), revealing long-term UX impact and that specific Platform Data events boosted 90-day retention by 35%.
Cohort Retention Over Time
Foundation for Process: The chosen analytical methods (Data Integration, Path Analysis, Custom Metrics, Cohort Analysis) were instrumental in executing our research process. They directly enabled the crucial stages of defining data requirements (leading to SQL Metric Building), uncovering user behaviors for insight generation, and ultimately informed the evidence-based dashboard design.
Research Process
Our systematic approach to implementing analytics and uncovering actionable insights
Analytics Audit & PM Usage Challenges
Data Requirements Definition
SQL Metric Building & Data Validation
Insight Generation & Analysis
Dashboard Design & Deployment
Analytics Audit & PM Usage Challenges
Identified current analytics assets and investigated Product Managers' utilization and adoption challenges.
Data Requirements Definition
Collaborated with stakeholders to define critical business questions, KPIs, and the specific data needed from GA and Platform Data systems.
SQL Metric Building & Data Validation
Developed new metrics by joining GA and Platform Data using SQL, and rigorously validated the accuracy and consistency of this integrated data.
Insight Generation & Analysis
Analyzed the validated, integrated dataset to uncover actionable insights into user behavior, engagement patterns, and product stickiness.
Dashboard Design & Deployment
Designed, developed, and deployed interactive dashboards to provide PMs with ongoing access to key metrics and actionable insights.
Key Findings
Our analysis of 70+ million data points revealed these critical insights about user behavior
Platform Data Sequence Transition Analysis
This pattern below suggests the dashboard serves as a primary launchpad for, and return point from, Platform Data-related tasks, underscoring its importance in the user workflow for these key activities.
Engagement Trend Analysis
Analysis of median 'Whole Session Engagement Scores' indicated a positive growth in engagement, suggesting increased user acclimatization and value derived from the platform of nearly 11%.
Early Oct '24
24.94
Late Dec '24
27.64
Median Whole Session Engagement Score
Dominant User Interactions
Analysis of over 23.2 million session events highlights core user interaction patterns. This data signifies a highly engaged user base actively navigating content and interacting with page elements, forming the foundational layer of user experience on the platform.
Streamlined Reporting & Efficiency Gains
Integrating multiple data systems into a unified dashboard saved analysts 10+ hours weekly by eliminating manual report compilation from disparate sources. This streamlined workflow significantly boosted efficiency and contributed to $312,000 in annual operational savings.
Key Opportunity
Leveraging integrated GA and Platform Data data provided deep insights into user behavior. Key achievements include an ~11% increase in median user engagement scores (Q4) and a reduction of 10+ hours per week in manual analytics preparation time.
Impact & Outcomes
How our analytics integration transformed raw data into actionable business intelligence and measurable results
Data Insights
Tracked engagement growth (~11% increase in Q4 median scores).
Identified dominant user actions (clicks, views, scrolls).
Unified dashboard visualizations revealed critical Platform Data interaction patterns.
Implementation
Redesigned user flows based on behavioral analytics
Developed custom engagement metrics for deeper behavioral understanding.
Created automated data visualization dashboards for teams
User Impact
Improved understanding of user engagement and stickiness.
Enabled data-informed UX and product strategy decisions.
Increased team adoption of data for informed actions.
Strengthened User Retention
The proportion of daily active users who were returning users grew significantly from ~28% to ~62% over Q4.
Elevated Platform Engagement
Observed in Q4, indicating improved user acclimatization and perceived platform value.
Time Efficiency
By analysts due to unified dashboard, eliminating manual data compilation.
Analytics Transformation
Before Integration
10+ hours per week manually gathering analytics
Data silos between marketing and product teams
Only 19% of decisions backed by user data
After Integration
93% of analytics work automated through dashboards
Unified data platform accessible to all teams
87% of key decisions now data-informed
Visuals
User Engagement Overview
Shows overall session counts, role-based breakdowns, and page frequencies.
Use stacked bars for time-based trends; see the pie chart for client usage share.
Identifies which roles or clients drive the bulk of engagement.
