Cyclistic: Case Study

Introduction

Welcome to my case study for Google’s Data Analytics Professional Certificate program. This project page will go through the different stages of my analysis in the order of Ask, Prepare, Process, Analyze, Share, and Act.

Scenario

You are 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, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.

Phase 1: Ask

What is the problem I am trying to solve?

Cyclistic wants to understand the different ways that customers use their bikes to help create an effective marketing strategy that targets casual riders and grows annual membership. Reviewing behaviors and trends of the customers will provide insight to rider preferences that Cyclistic can utilize to tailor their approach.

The business task is to identify trends in rider usage to market effectively to casual riders.
How do annual members and casual riders use Cyclistic bikes differently?

Phase 2 & 3 : Prepare and Process

The data has been made available by Motivate International Inc. under this license. For my analysis, I will be utilizing data for January through December of 2021 to ensure my findings are relevant for current marketing needs. The data was collected as 12 separate CSV files with similar structure, each representing a different month in 2021.

Upon initial exploration of the data, it boasted almost 5.6 million rows with 13 columns. I chose to utilize Rstudio to prepare, clean, and analyze the dataset due to it's massive size of approximately 1.4Gb. You can view the full coding with descriptive analysis here.

Phase 4: Analyze

A descriptive analysis in Rstudio revealed that Casual Riders prefer:

- Longer duration trips
- Weekend rides (Friday through Sunday)
- Classic Bikes

Phase 5: Share

Annual Members:

Annual Members account for 55% of all trips in 2021 with an average length of 12 minutes. Their usage illustrates a steady increase starting early week with the peak on Wednesday and the low on Sunday. Seasonally, annual members peak in September and account for more total rides in Fall, Winter, and Spring. They prefer classic bikes and likely use the annual perks for commuting around the city due to their lower average trip duration.

Casual Riders:

Casual riders account for 45% of all trips in 2021 with an average ride length of 18 minutes. Their usage illustrates a sharp increase over the weekend from Friday to Sunday. Casual rider usage peaks seasonally during Summer, from June to August, where they account for more total rides than the annual members. They prefer classic bikes and likely ride recreationally given their seasonal pattern of usage and higher average trip duration.

To explore the interactive dashboard and heat map below, please click here.

Annual Member Heat Map:

Casual Rider Heat Map:

Phase 6: Act

Recommendations:

To capitalize on Casual Rider trends and increase annual membership:

- Evaluate impacts of increasing maximum trip duration for annual members
- Explore seasonal opportunities to stimulate year-round bike usage
- Leverage partnership with local establishments to organize rider events

Final Thoughts:

The case study suggests major differences in the way that the different members use the bikes. The current annual membership does a great job at targeting those that are likely commuting around the city. It's worth discussing the approach of creating a tiered membership program that would meet more of the casual riders' needs. This membership could be focused around weekend rides and include less penalties for exceeding the trip duration maximum, discounts to the community establishments within the parks, and access to member ride events that could be strategically planned outside of the summer months to encourage more usage in the off-peak months. The one-size-fits-all membership may not be the strongest way to capitalize on the rider market and increase annual membership based off trends discovered in this study.

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