Overview

In this project, I took on the role of a marketing analyst for Cyclistic, a successful bike-share company that launched in 2016. Cyclistic's unique selling proposition lies in its flexible pricing plans, which cater to a wide range of customers, including casual riders and annual members. The project was conceived as part of the Google Data Analytics Professional Certificate capstone project, focusing on a critical business challenge set forth by the company's marketing director, Moreno.

Objective

The primary goal was to devise effective marketing strategies to convert casual riders into annual members. To achieve this, it was essential to understand the distinct behaviors and preferences of annual members versus casual riders, utilizing Cyclistic's historical bike trip data.

Key Findings

The analysis revealed several key insights:

  • Seasonality: Both groups used bikes more in warmer months (June-August).
  • Weekday vs. Weekend: Annual members used bikes more on weekdays, while casual riders favored weekends.
  • Time of Day: Usage peaked in the afternoon (4-6 pm) for both groups, with an additional morning peak (7-9 am) for annual members.
  • Bike Type: Classic bikes were most popular. Casual riders used docked bikes more frequently.
  • Trip Duration: Casual riders averaged longer rides (22.94 minutes) compared to annual members (12.13 minutes).
  • Popular Routes: Most rides occurred within districts: Near North Side (both groups), Near West Side (annual members), Lincoln Park & LOOP (casual riders).

    • Insights

  • Annual members mostly use bikes for commuting (workdays, busy districts).
  • Casual riders mostly use bikes for leisure (weekends, fun spots).

    • Data Source

    The analysis was based on an extensive dataset provided by Motivate International Inc., covering over 5 million bike trips from January 2023 to December 2023. This dataset includes detailed information about each trip, such as ride ID, rideable type, start and end times, station names and IDs, and geographical coordinates.

    Technical Skills Highlighted:

  • Data cleaning and transformation (SQL): This phase included integrating monthly datasets into a unified table, data type corrections, addressing inconsistencies in station names and IDs, creating a separate stations table for storgae and performance efficiency.
  • Python scripting I used Community Areas Boundaries data from the City of Chicago to categorize stations by district, to enhance the spatial analysis. Connecting the spatial data to stations table was performed in Python.
  • Data visualization: Tableau

    • View SQL queries and Python code on GitHub

    Data Analysis and Visualization

    After cleaning the data, I imported it into Tableau to create insightful visualizations and dashboards. The analysis focused on comparing the usage patterns of annual members and casual riders, examining aspects such as ride frequency, duration, preferred bike types, and usage by time of day, week, and month.

    Reflection

    This project not only allowed me to apply and enhance my data analytics skills but also offered a real-world perspective on how data-driven strategies can impact business decisions. The challenges encountered, particularly in data processing and analysis, have enriched my problem-solving skills and my ability to derive actionable insights from complex datasets.

    mehdi.rezagholipour@gmail.com
    (+44) 07466683765
    Birmingham, UK