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CASE STUDY

Data-Driven Health Management for Engagement & Insights

AT A GLANCE

The project enhanced a health management platform by integrating data from health and fitness apps using Selenium and developing predictive models with Scikit-learn. These advancements enriched the platform with targeted health metrics, streamlined data collection by 70%, and provided actionable insights, including a 58% retention rate for premium users, to improve user engagement and segmentation strategies.

Client information

Company Name
A health management platform provider
location
Waterloo, Ontario, Canada
SIZE
Small Business
INDUSTRY
Health Technology
Services Provided
Data Integration Solutions
70%
Reduction in data collection time, increasing operational efficiency, lowering costs, and enabling faster updates, ensuring the platform stays competitive and responsive to user needs
58%
Retention rate for premium users, identifying strong customer loyalty, providing actionable insights for segmentation and strategies to drive revenue growth and user engagement.

CHALLENGE

The client needed to enhance the development of a health management application by systematically aggregating and analyzing health service information from various apps integrated with Apple Health.

SOLUTION

Leveraging Selenium for web scraping, the project systematically aggregated health service information from various apps integrated with Apple Health, providing detailed insights into the types of health metrics collected. These insights enabled targeted and effective feature integration to enhance user engagement and service delivery. Scikit-learn was used to develop predictive models, further refining the application’s ability to analyze health data and deliver actionable recommendations to users.

IMPACT

  • Targeted Health Metrics Integration: By analyzing and integrating data from various health and fitness apps, the project enriched the health management platform, offering users more personalized and relevant insights.
  • Actionable Customer Insights: The analysis identified a 58% retention rate for premium users, empowering stakeholders with insights for effective customer segmentation and targeted strategies.
  • Improved Predictive Capabilities: The integration of a machine learning model enabled the prediction of health irregularities based on user data, enhancing the platform’s ability to deliver timely notifications and interventions, improving user engagement and satisfaction.
  • Streamlined Data Collection Process: Advanced web scraping techniques reduced data collection and processing time by 70%, ensuring an efficient method to continuously update and expand the platform’s database with new health app integrations.

TOOLS

Selenium
Scikit Learn

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