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Exploratory Data Analysis on the fictional company, Cyclistic, a bike-share company out of Chicago. "R" was used to complete this analysis for the Google Data Analytics Professional Certification.

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Google Data Analytics Case Study: Cyclistic-Bike-Share

Bike-Share Analysis

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This analysis is an optional case study from the Google Data Analytics Professional Certificate on Coursera.

Introduction

This case study is the final project for the Google Data Analytics Certification Course. For this case study, I performed real-world tasks of a junior data analyst conducting analysis for a fictional company, Cyclistic, that has more than 5,800 bicycles and 600 docking stations. Data was collected from a real bike share company, Divvy.

About The Company

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime.

Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members.

Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs.

Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends.

Scenario

I'm a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, my team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, my team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve my recommendations, so they must be backed up with compelling data insights and professional data visualizations.

Key Stakeholders

Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels

Business Task

  1. How do annual members and casual riders use Cyclistic bikes differently?
  2. Why would casual riders buy Cyclistic annual memberships?
  3. How can Cyclistic use digital media to influence casual riders to become members?

Data Source

The data used would cover rider information spanning a one-year period from August 2021 to July 2022. The data has been made available by Motivate International Inc. with license and is originally stored in separate CSV files organized by the different months of the year here.

Conclusion

It can be seen from the graphs that cyclist subscribers use the bikes consistently every day for a brief period of time, but customers utilize them more inconsistently. On average, customers ride for significantly longer periods of time than subscribers, yet they ride bikes less often during the week and more on Saturdays and Sundays. In short, customers mostly use the service for leisure and enjoyment, while subscribers utilize it primarily for transportation for their everyday routines.

Key Recommendations

  1. Place advertisment at the top 20 bike stations where customer users book from. In addition, place advertisement at high traffic areas in the city. These are the places that customers would possibly work, attend school or grab a bite to eat with family and friends.
  2. Create a quirky and relatable skit on social media platforms to reach audience members of every age. This will result in big engagement numbers and a curiosity that will result in more subscribed members. In addition, run an ad that dispalys a short second survey question that the company is trying to collect data on. Collecting the data from the short second survey will be benefical to the company's growth.
  3. According to the data, customers use the bike share on the weekends more. Offering a weekend only subscription will result in customers enjoying the bike share service. In addition, create a special-offer when you upgrade from weekend only subscription to annual subscription.

Tools

Excel and R Studio for Data Cleaning, R Studio for Data Transformation, Data Visualisation and Data Analysis

About

Exploratory Data Analysis on the fictional company, Cyclistic, a bike-share company out of Chicago. "R" was used to complete this analysis for the Google Data Analytics Professional Certification.

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