🚴♂️ Cyclistic Bike-Share & 📱 Bellabeat Smart Devices
This project is part of the Google Data Analytics Professional Certificate, where I applied SQL, R, and Tableau to analyze two real-world business scenarios:
1️⃣ Cyclistic Bike-Share Analysis: Understanding customer behavior to convert casual riders into annual members. 2️⃣ Bellabeat Smart Devices: Uncovering insights from fitness data to drive marketing strategies for a wellness tech company.
Through data wrangling, visualization, and insights-driven recommendations, I provide actionable business strategies for both companies.
Cyclistic is a bike-sharing program with 5,800+ bicycles and 600 docking stations, offering traditional and assistive bikes. The company wants to increase annual memberships, as they are more profitable than casual riders.
How can Cyclistic convert casual riders into annual members?
Data includes ride details, timestamps, locations, and user types. Cleaned station ID inconsistencies and handled missing values. Used SQL (SQLite, DB Browser) to preprocess large datasets.
✔️ Annual members ride longer distances but for shorter durations than casual riders. ✔️ Casual riders use bikes more on weekends, while members ride evenly throughout the week. ✔️ Tourist-heavy locations have higher casual rider usage. ✔️ Peak usage times for casual riders align with leisure activities, while members ride during commute hours.
🔹 Target casual riders with discounted weekend membership trials. 🔹 Improve marketing at tourist hotspots to encourage subscriptions. 🔹 Promote a loyalty program rewarding frequent casual riders. 🔹 Use social media & email campaigns showcasing member benefits.
Bellabeat is a wellness tech company that develops health-focused smart devices for women. The company seeks data-driven insights to expand its market presence.
How can Bellabeat leverage smart device data to improve marketing?
Data includes fitness tracking details (sleep, activity, stress levels, reproductive health, etc.). Processed large datasets using SQL (DB Browser for SQLite). Performed exploratory data analysis (EDA) & visualizations in R & Tableau.
✔️ Users with consistent activity levels show better sleep patterns. ✔️ High-stress users engage less in physical activity, indicating a potential product-market gap. ✔️ Wearable device usage spikes in the morning and late evening, aligning with fitness routines. ✔️ Social media engagement correlates with increased device usage.
🔹 Introduce personalized wellness programs based on activity data. 🔹 Market smart devices as stress management tools for high-stress demographics. 🔹 Leverage social media influencers to increase awareness. 🔹 Improve app engagement by integrating workout reminders & wellness insights.
✅ SQL (SQLite, DB Browser) – Data cleaning & transformation ✅ R – Exploratory Data Analysis (EDA) & visualization ✅ Tableau – Interactive dashboards for key insights
✅ Data Cleaning & Wrangling ✅ Exploratory Data Analysis (EDA) ✅ Data Visualization (Tableau & R) ✅ SQL Querying & Processing ✅ Business Strategy Development ✅ Google Data Analytics Capstone Completion
This project showcases my ability to analyze real-world business problems using data-driven insights and visual storytelling. By leveraging SQL, R, and Tableau, I provided clear recommendations for customer conversion & market expansion.