Data Analysis Portfolio

Objective

The goal was to analyze user data from 240 different 2gnoMe accounts to understand user engagement, identify underutilized features, and implement changes that would improve Admin Portal usage and overall platform performance.

Role & Responsibilities

• Collected and processed data from 240 accounts using SQL to identify patterns in user behavior.
• Created visualizations in Tableau to present data trends and insights to the C-suite and collaborated with the product team to implement improvements.

Process/Approach

• Aggregated and analyzed usage data across accounts to calculate average user engagement and determine which pages were most and least visited.
• Linked user behavior data to customer feedback, helping the team understand how platform usage aligned with customer needs.
• Recommended 15 new features that focused on improving user engagement on underutilized pages and driving overall Admin Portal usage.

Key Metrics/Results

The data analysis led to the implementation of 15 new features and a 45% increase in Admin Portal usage. User engagement on previously underutilized pages saw a 30% boost, improving overall satisfaction and retention.

Challenges & Solutions

A key challenge was identifying why critical Admin Portal pages were underused. By closely examining user behavior and combining it with qualitative customer feedback, I was able to work with the product team to redesign and promote these pages, leading to higher engagement.

Tools/Technologies Used

SQL for data aggregation, Tableau for data visualization, and internal CRM systems to correlate data with customer insights.

Objective

The project aimed to analyze foot traffic data to identify the best possible location for Ooh Baby's first physical store in New York City, considering factors like footfall, dwell time, bounce rate, and rent.

Role & Responsibilities

• Gathered foot traffic data from potential store locations using platforms like Unacast and modeled various scenarios to evaluate each location.
• Collaborated with Ooh Baby's C-suite to weight key metrics (footfall, conversion rate, rent) and generated a list of five viable store locations.

Process/Approach

• Analyzed key metrics such as Footfall (total visitors), Dwell Time (time spent in specific areas), Bounce Rate (visitors who leave quickly), Peak Hours, Conversion Rate, and Repeat Visits.
• Developed a weighted scoring system based on these metrics, considering their importance to the business and the rent cost in each borough.

Key Metrics/Results

The analysis resulted in the decision to open Ooh Baby's store on Christopher Street in Greenwich Village in 2021. This location was chosen based on high footfall, peak hours, and proximity to target customers, while balancing rent considerations.

Challenges & Solutions

One of the main challenges was balancing high foot traffic and optimal rent prices in a competitive market. The weighted scoring system ensured that key business priorities were met while managing costs, leading to an informed and data-driven decision.

Tools/Technologies Used

Unacast for foot traffic data collection, Excel for data modeling and analysis, and internal communication tools for presenting the findings to the executive team.