Introduction:

Cyclistic has partnered with the city of New York to provide shared bikes. Currently, there are bike stations located throughout Manhattan and neighboring boroughs. Customers are able to rent bikes for easy travel between stations at these locations. Cyclistic’s Customer Growth Team is creating a business plan for next year. The team wants to understand how their customers are using their bikes; their top priority is identifying customer demand at different station locations.

Things to Perform:

Data Modelling

ETL Data Pipeline

Dashboard

Questions:

1. How does the usage of Cyclistic bikes differ between subscribers and non-subscribers?
2. What are the most popular starting locations for bike trips?
3. Can we identify any patterns or trends?
4. Which destination locations are most frequently chosen by customers based on the total trip minutes?
5. What are the peak months for bike usage?
6. Are there any seasonal trends or variations in demand? How has the number of trips grown year over year?
7. Can we identify any significant changes or patterns?
8. Are there any stations that experience high congestion or imbalance between incoming and outgoing bikes?
9. How do weather conditions, such as rain, impact the usage of Cyclistic bikes?
10. Are there any variations in demand based on different weather conditions?
11. Can we identify any specific time of day or season that experiences peak bike usage?
12. How can the insights from customer usage data inform the decision-making process for new station growth?
13. What are the key findings and recommendations based on the analysis of the data?

ETL Data Pipeline With Big Query SQL:

Dashboard with Tableau:

Findings

Subscriber vs. Non-subscriber Usage:

Subscribers accounted for the majority of bike trips, indicating a loyal customer base. At Manhattan Area

Non-subscribers showed a lower frequency of bike usage, suggesting potential for conversion into subscribers through targeted marketing efforts.

Popular Starting Locations:

A map visualization revealed several hotspots for bike trips, with high starting activity in downtown areas and popular neighborhoods. Highlighted with Read Color. Locations with higher population densities exhibited increased bike usage, indicating a correlation between user demand and urban areas.

Preferred Destination Locations:

Analysis of total trip minutes highlighted popular destination locations, predominantly in commercial and recreational areas. Like Manhattan ,Brooklyn . Peak months showed higher trip durations in tourist destinations, suggesting the presence of seasonal demand.

Seasonal Trends:

Bike usage demonstrated clear seasonality, with peak months in the summer and autumn showing the highest number of trips. Demand decreased during inclement weather conditions, indicating a potential impact of weather on customer behavior

Year-over-Year Trip Growth:

The number of bike trips exhibited consistent growth year over year, signaling an increasing demand for Cyclistic services. The growth rate varied across locations, with certain neighborhoods experiencing higher growth rates than others.

Congestion at Stations:

By analyzing net differences between starting and ending trips per station, congested stations were identified. These stations experienced imbalances between incoming and outgoing bikes, suggesting a need for bike redistribution strategies to ensure availability.

Weather Impact:

Trips were affected by weather conditions, particularly during rainy periods. Usage decreased during inclement weather, highlighting the need to consider weather forecasts and its potential influence on bike availability and demand.

Recommendations:

Subscriber Engagement:

Focus on targeted marketing campaigns to convert non-subscribers into subscribers. Offer incentives and personalized promotions to increase engagement and loyalty among existing subscribers.

Station Expansion:

Allocate resources to open new stations in areas with high starting and ending activity to cater to customer demand. Consider partnerships with popular destinations and commercial areas to enhance convenience and accessibility.

Seasonal Strategies:

Develop seasonal promotions and offers to capitalize on peak months and encourage usage during off-peak periods. Collaborate with local businesses to create synergies between tourist attractions and Cyclistic bike usage.

Bike Redistribution:

Implement efficient bike redistribution strategies to address congestion at stations and ensure an adequate supply of bikes. Utilize real-time data and analytics to optimize bike availability across the network.

Weather Considerations:

Monitor weather forecasts and align bike availability with anticipated demand based on weather conditions. Offer incentives or discounts during inclement weather to encourage usage and mitigate the impact of weather on demand.

Link To Dashboard:https://public.tableau.com/app/profile/muhammad.tauqeer.khalid/viz/Cyclist_16876815408970/Dashboard1