• Business Question: How do annual members and casual riders use Cyclistic bikes differently?

• Objective: Analyze historical data to identify usage patterns and trends.

• Deliverables: Provide insights and recommendations for increasing annual memberships.

• Dataset Used:Divvy_Trips_2019_Q1 Divvy_Trips_2020_Q1
• Data Source: Cyclistic historical bike usage data.
• Data Range: January 2019 - March 2020

1. Average Trip Duration by User Type
   • Casual riders: 36.46 min
   • Members: 11.41 min
   • Casual riders take longer, leisure-focused trips.
2. Total Number of Rides by User Type
   • Members dominate with 341,998 rides.
   • Casual riders contribute 23,118 rides.
3. Daily Ride Patterns
   • Members commute during peak hours.
   • Casual riders have a more spread-out, leisure pattern.

1. Weekly Ride Patterns
   • Members ride more on weekdays.
   • Casual riders prefer weekends.
2. Top 10 Start Stations: Casual
   • Focus on tourist areas.
   • Opportunities for targeted promotions.
3. Top 10 Start Stations: Members
   • Focus on residential and transit hubs.
   • Enhance availability and service.

1. Enhance Member Retention and Engagement
   • Implement loyalty programs and incentives.
2. Target High Traffic Casual Stations
   • Promotional offers at popular leisure locations.
3. Optimize Bike Availability During Peak Hours
   • Redistribute bikes effectively during commute hours.

1. Data Import and Initial Inspection
   • Imported datasets for 2019 and 2020 using read_csv().
   • Inspected structure and content using glimpse() and summary().
2. Handling Missing Values
   • Identified and handled missing values using filter() and mutate().
   • Removed rows with missing critical information such as station names.
3. Standardizing Column Names and Data Types
   • Standardized column names across datasets.
   • Converted trip_duration to minutes to ensure consistency.

1. Ensuring Unit Consistency
   • Verified that trip duration is uniformly in minutes for both datasets.
2. Combining Datasets
   • Merged the cleaned datasets using bind_rows() and validated the combined data structure.
3. Handling Duplicates and Data Integrity
   • Removed duplicate records and ensured logical consistency in the dataset.
4. Final Validation
   • Performed final checks to ensure the dataset was clean and ready for analysis.

1. Average Trip Duration by User Type

• Observation:

  • Casual riders have a significantly longer average trip duration (around 36.46 minutes) compared to annual members (about 11.41 minutes).

• Implication:

  • Casual riders are likely using the bikes for leisure and longer trips, while members use them more for shorter, possibly more frequent, commutes or errands.

2. Total Number of Rides by User Type

• Observation:

  • Annual members account for a much larger share of total rides (341,998) compared to casual riders (23,118).

• Implication:

  • Annual members are the core user base of Cyclistic, contributing significantly more to ride volume, which aligns with the company’s profitability goals. Marketing strategies should focus on retaining and expanding this segment.

3. Daily Ride Patterns: Casual vs. Member

• Observation:

  • Members have two clear peaks: at 8 AM and evening 5PM rush hours, reflecting commuting behavior. Casual riders have a more spread-out pattern throughout the day, peaking around noon.

• Implication:

  • Members primarily use the service for commuting, whereas casual riders use it for leisure activities, which likely occur outside traditional work hours.

4. Weekly Ride Patterns: Casual Riders vs. Members

• Observation:

  • Members have a relatively consistent usage pattern throughout the weekdays, with slight dips during the weekends. Casual riders show a reverse trend, with higher usage on weekends.

• Implication:

  • Casual riders are more likely to use the service during weekends for recreational purposes, while members use it for weekday commutes.

5. Top 10 Start Stations for Casual Users

• Observation:

  • Casual riders predominantly start their trips at popular tourist locations and parks, such as “Streeter Dr & Grand Ave” and “Millennium Park”.

• Implication:

  • These stations could be targeted with promotional offers or partnerships with nearby businesses to attract more casual riders.

6. Top 10 Start Stations for Members

• Observation:

  • Annual members frequently start their trips at stations near residential areas and transit hubs, such as “Clark St & Elm St” and “Lincoln Ave & Fullerton Ave”.

• Implication:

  • These locations are important for the convenience of daily commuters. Enhancing bike availability and station maintenance here could improve user experience and retention.

1. Increase Member Retention and Engagement:

• Action:

  • Implement loyalty programs or referral bonuses for current members. Consider adding benefits such as discounted bike maintenance services or exclusive access to new bike types.

• Rationale:

  • Given that members make up a large portion of the total rides, keeping this group engaged and satisfied is critical for sustaining revenue growth. Offering incentives can reduce churn and attract more long-term users.

2. Leverage High Traffic Casual Rider Stations:

• Action:

  • Introduce special offers or temporary membership discounts at popular tourist and leisure locations, such as “Millennium Park” or near beaches.

• Rationale:

  • Casual riders use the service for recreational purposes. Targeted promotions at these high-traffic areas could convert them into annual members, thereby increasing profitability.

3. Optimize Bike Availability During Peak Hours:

• Action:

  • Improve bike redistribution efforts during peak commute times and at key member stations to ensure availability. Use predictive analytics to anticipate demand based on historical data.

• Rationale:

  • Member usage peaks during commute hours and at specific stations. Ensuring bike availability at these times and places will enhance user experience, increase satisfaction, and possibly encourage casual riders to become members.

The analysis of Cyclistic’s bike usage data reveals distinct differences in behavior between annual members and casual riders. Annual members predominantly use the bikes for shorter, more frequent trips, likely reflecting their use for daily commutes or routine activities. In contrast, casual riders tend to take longer trips, suggesting a preference for leisure and recreational use. This indicates that targeted marketing efforts should focus on promoting the benefits of membership for frequent users, while casual riders could be encouraged to transition to membership by highlighting exclusive benefits, such as cost savings for long trips or additional services. These insights provide a strong foundation for designing more effective strategies to convert casual riders into loyal members, ultimately driving growth and maximizing revenue for Cyclistic.