Introduction:

In this report, I provide an overview of bike usage patterns of different age groups, gender, and user types along with recommendations on how to attract more customers and subscribers and increase their level of satisfaction.

Executive Summary:

In this report, I will present my data analysis of the Ford Go Bike service, a bike-sharing system covering the greater San Francisco Bay area. I obtained the data from a free dataset on Kaggle. It contains information on 183,412 individual rides and 16 columns such as start time, end time, user type, member age, and member gender. My goal is to answer the following questions based on the data:

• What is the age distribution of the bike users?

• How does the user type affect the bike-sharing behavior?

• Which gender has the longest average ride duration?

Summary of Findings

The following are the main findings of my data analysis of the Ford Go Bike service:

• Age-user distribution

Analysis of user demographics reveals a concentration of bike users within the range of 25-45 years old, with a median age of 34.

• User type comparison

The data shows that the number of subscribers exceeds that of customers signifying a higher prevalence of subscriber user type. Subscribers make up (89% ) while customers make up (10.8%).

• Gender-based user Analysis

The gender distribution is dominated by male users, who represent (74.6%) of the users, followed by female users (23.3%) and other gender users (2.2%).

• Age-Duration relationship

Examining the relationship between age and trip duration reveals that the majority of trips have a duration of less than 10,000 units and involve users below the age of 80.

• User Type-Bike share analysis

When exploring the relationship between user type and bike share for all trips I found out that only a small number of subscribers have bike shares less than 2,000.

• Gender-Duration relationship

Females and other genders have longer trip duration than males. The average trip duration for females is 600 seconds, for other genders 650, and for males is 500.

• User Type-Duration Comparison

The user type variable influences trip duration, as customers have longer trip duration than subscribers. The average trip duration for customer user types is 13 minutes, while that for subscribers is 8 minutes.

Key Insights for Presentation

In conclusion, this analysis reveals important findings in the Ford Go bike Dataset:

• Bike users are between 25-45 years old with the majority being 35 years of age.

• The number of male users is more than female users. Males make up 74.6% females make up 23.3%.

• Only a small number of subscribers 20,000 share their bikes from 150,000 subscribers.

• Customers have longer trips on average 13 minutes while subscribers have shorter trips on average 8 minutes.

Recommendations:

• To increase the number of customers, the business could offer more attractive pricing plans, discounts, or promotions for occasional users. For example, the business could introduce a referral program, where customers can get a free ride for inviting a friend to use the service.

• Educate people above 45 years on the benefits of bike riding to their health this will in turn increase the number of subscribers.

• Ford Go Bike can also offer promotions and discounts to attract new customers. All new customers will be subject to a subsidized rate.

• Ford Go Bike should offer good customer service in that they should respond promptly to repair and maintenance requests this will help retain existing customers and subscribers.

• To increase the number of females, Ford Go Bike should carry out aggressive marketing campaigns online and offline to reach all potential female customers. It could also conduct market research to understand their preferences, needs, and challenges. For example, the business could survey its existing and potential users to find out what motivates them to use or not use the service, and what improvements they would like to see.

• The business could create a mobile app or a website that suggests personalized itineraries based on user profiles, goals, and interests.

This report serves as a sample of my writing and analytical skills I look forward to working with Genesis to be able to solve some of the world’s problems using my analytical skills and create innovative solutions.

You can access my data exploration on my website